From 75a096826061234e97a03bad7789712ca04e57cc Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:15:29 +0000 Subject: [PATCH 01/69] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20=E5=B4=94=E9=A9=BF?= =?UTF-8?q?=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91=E6=B7=B1?= =?UTF-8?q?=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA?= =?UTF-8?q?=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/.keep" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/.keep" new file mode 100644 index 0000000..e69de29 -- Gitee From be13659b7e2aa8f268a0b74e03c51265572a5dfb Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:30:41 +0000 Subject: [PATCH 02/69] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20data?= MIME-Version: 1.0 Content-Type: text/plain; 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charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../models/.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/.keep" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/.keep" new file mode 100644 index 0000000..e69de29 -- Gitee From 70657f744dc0d197b4b8db6c28a3d91e650160c7 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:40:40 +0000 Subject: [PATCH 05/69] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20pointnet2=5Fcls=5Fms?= =?UTF-8?q?g.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../models/pointnet2_cls_msg.py/.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" new file mode 100644 index 0000000..e69de29 -- Gitee From 3ae0bbf0b34c3158c7b52c0f14fde990707dd766 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:41:20 +0000 Subject: [PATCH 06/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/models/pointnet2?= =?UTF-8?q?=5Fcls=5Fmsg.py/.keep.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../models/pointnet2_cls_msg.py/.keep" | 61 +++++++++++++++++++ 1 file changed, 61 insertions(+) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" index e69de29..c750c42 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" @@ -0,0 +1,61 @@ +""" + PointNet++ Model for point clouds classification +""" + +import os +import sys +BASE_DIR = os.path.dirname(__file__) +sys.path.append(BASE_DIR) +sys.path.append(os.path.join(BASE_DIR, '../utils')) +import tensorflow as tf +import numpy as np +import tf_util +from pointnet_util import pointnet_sa_module + +def placeholder_inputs(batch_size, num_point): + pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) + labels_pl = tf.placeholder(tf.int32, shape=(batch_size)) + return pointclouds_pl, labels_pl + +def get_model(point_cloud, is_training, bn_decay=None): + """ Classification PointNet, input is BxNx3, output Bx40 """ + batch_size = point_cloud.get_shape()[0].value + num_point = point_cloud.get_shape()[1].value + end_points = {} + l0_xyz = point_cloud + l0_points = None + end_points['l0_xyz'] = l0_xyz + + # Set abstraction layers + # Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4). + # So we only use NCHW for layer 1 until this issue can be resolved. + l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_nchw=True) + l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2') + l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3') + + # Fully connected layers + net = tf.reshape(l3_points, [batch_size, -1]) + net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) + net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1') + net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay) + net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2') + net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3') + + return net, end_points + + +def get_loss(pred, label, end_points): + """ pred: B*NUM_CLASSES, + label: B, """ + loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) + classify_loss = tf.reduce_mean(loss) + tf.summary.scalar('classify loss', classify_loss) + tf.add_to_collection('losses', classify_loss) + return classify_loss + + +if __name__=='__main__': + with tf.Graph().as_default(): + inputs = tf.zeros((32,1024,3)) + output, _ = get_model(inputs, tf.constant(True)) + print(output) -- Gitee From daf68344f40880208e4725da0761949b9303ae8c Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:42:12 +0000 Subject: [PATCH 07/69] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20tf=5Fops?= MIME-Version: 1.0 Content-Type: text/plain; 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charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../tf_ops/3d_interpolation/interpolate.cpp" | 169 ++++++++++++++++++ 1 file changed, 169 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" new file mode 100644 index 0000000..c5ac240 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" @@ -0,0 +1,169 @@ +#include +#include +#include // memset +#include // rand, RAND_MAX +#include // sqrtf +#include +#include +using namespace std; +float randomf(){ + return (rand()+0.5)/(RAND_MAX+1.0); +} +static double get_time(){ + timespec tp; + clock_gettime(CLOCK_MONOTONIC,&tp); + return tp.tv_sec+tp.tv_nsec*1e-9; +} + +// Find three nearest neigbors with square distance +// input: xyz1 (b,n,3), xyz2(b,m,3) +// output: dist (b,n,3), idx (b,n,3) +void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { + for (int i=0;i Date: Thu, 27 Oct 2022 01:46:05 +0000 Subject: [PATCH 11/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/tf=5Fops/3d=5Fint?= =?UTF-8?q?erpolation/tf=5Finterpolate.cpp.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../3d_interpolation/tf_interpolate.cpp" | 267 ++++++++++++++++++ 1 file changed, 267 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate.cpp" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate.cpp" new file mode 100644 index 0000000..d01aeff --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate.cpp" @@ -0,0 +1,267 @@ +#include +#include +#include // memset +#include // rand, RAND_MAX +#include // sqrtf +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/framework/common_shape_fns.h" + +#pragma GCC diagnostic ignored "-Wunused-result" + +using namespace tensorflow; + +REGISTER_OP("ThreeNN") + .Input("xyz1: float32") + .Input("xyz2: float32") + .Output("dist: float32") + .Output("idx: int32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + c->set_output(1, c->input(0)); + return Status::OK(); + }); +REGISTER_OP("ThreeInterpolate") + .Input("points: float32") + .Input("idx: int32") + .Input("weight: float32") + .Output("out: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle dims1; // (b,m,c) + c->WithRank(c->input(0), 3, &dims1); + ::tensorflow::shape_inference::ShapeHandle dims2; // (b,n,3) + c->WithRank(c->input(1), 3, &dims2); + // (b,n,c) + ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)}); + c->set_output(0, output); + return Status::OK(); + }); +REGISTER_OP("ThreeInterpolateGrad") + .Input("points: float32") + .Input("idx: int32") + .Input("weight: float32") + .Input("grad_out: float32") + .Output("grad_points: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + return Status::OK(); + }); + +float randomf(){ + return (rand()+0.5)/(RAND_MAX+1.0); +} +static double get_time(){ + timespec tp; + clock_gettime(CLOCK_MONOTONIC,&tp); + return tp.tv_sec+tp.tv_nsec*1e-9; +} + +// Find three nearest neigbors with square distance +// input: xyz1 (b,n,3), xyz2(b,m,3) +// output: dist (b,n,3), idx (b,n,3) +void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { + for (int i=0;iinput(0); + OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,n,3) xyz1 shape.")); + int b = xyz1_tensor.shape().dim_size(0); + int n = xyz1_tensor.shape().dim_size(1); + + const Tensor& xyz2_tensor = context->input(1); + OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,m,3) xyz2 shape.")); + int m = xyz2_tensor.shape().dim_size(1); + + Tensor *dist_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,n,3}, &dist_tensor)); + Tensor *idx_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,n,3}, &idx_tensor)); + + auto xyz1_flat = xyz1_tensor.flat(); + const float *xyz1 = &(xyz1_flat(0)); + auto xyz2_flat = xyz2_tensor.flat(); + const float *xyz2 = &(xyz2_flat(0)); + auto dist_flat = dist_tensor->flat(); + float *dist = &(dist_flat(0)); + auto idx_flat = idx_tensor->flat(); + int *idx = &(idx_flat(0)); + threenn_cpu(b,n,m,xyz1,xyz2,dist,idx); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreeNN").Device(DEVICE_CPU), ThreeNNOp); + + + +class ThreeInterpolateOp: public OpKernel{ + public: + explicit ThreeInterpolateOp(OpKernelConstruction * context):OpKernel(context){} + + void Compute(OpKernelContext * context) override { + const Tensor& points_tensor=context->input(0); + OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolate expects (b,m,c) points shape")); + int b = points_tensor.shape().dim_size(0); + int m = points_tensor.shape().dim_size(1); + int c = points_tensor.shape().dim_size(2); + + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b && idx_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) idx shape")); + int n = idx_tensor.shape().dim_size(1); + const Tensor& weight_tensor=context->input(2); + OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) weight shape")); + + Tensor * out_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &out_tensor)); + + auto points_flat = points_tensor.flat(); + const float *points = &(points_flat(0)); + auto idx_flat = idx_tensor.flat(); + const int *idx = &(idx_flat(0)); + auto weight_flat = weight_tensor.flat(); + const float *weight = &(weight_flat(0)); + auto out_flat = out_tensor->flat(); + float *out = &(out_flat(0)); + threeinterpolate_cpu(b,m,c,n,points,idx,weight,out); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreeInterpolate").Device(DEVICE_CPU),ThreeInterpolateOp); + + +class ThreeInterpolateGradOp: public OpKernel{ + public: + explicit ThreeInterpolateGradOp(OpKernelConstruction * context):OpKernel(context){} + + void Compute(OpKernelContext * context) override { + const Tensor& points_tensor=context->input(0); + OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,m,c) points shape")); + int b = points_tensor.shape().dim_size(0); + int m = points_tensor.shape().dim_size(1); + int c = points_tensor.shape().dim_size(2); + + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) idx shape")); + int n = idx_tensor.shape().dim_size(1); + const Tensor& weight_tensor=context->input(2); + OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) weight shape")); + + const Tensor& grad_out_tensor=context->input(3); + OP_REQUIRES(context,grad_out_tensor.dims()==3 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==n && grad_out_tensor.shape().dim_size(2)==c, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,c) grad_out shape")); + + Tensor * grad_points_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,c}, &grad_points_tensor)); + + auto points_flat = points_tensor.flat(); + const float *points = &(points_flat(0)); + auto idx_flat = idx_tensor.flat(); + const int *idx = &(idx_flat(0)); + auto weight_flat = weight_tensor.flat(); + const float *weight = &(weight_flat(0)); + auto grad_out_flat = grad_out_tensor.flat(); + const float *grad_out = &(grad_out_flat(0)); + auto grad_points_flat = grad_points_tensor->flat(); + float *grad_points = &(grad_points_flat(0)); + memset(grad_points, 0, sizeof(float)*b*m*c); + threeinterpolate_grad_cpu(b,n,c,m,grad_out,idx,weight,grad_points); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreeInterpolateGrad").Device(DEVICE_CPU),ThreeInterpolateGradOp); + + -- Gitee From 3c4d19a5d161068bf36a4503b45335d133218f58 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:48:24 +0000 Subject: [PATCH 12/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/tf=5Fops/3d=5Finterpolation?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/3d_interpolation/.keep" | 0 .../tf_ops/3d_interpolation/interpolate.cpp" | 169 ----------- .../3d_interpolation/tf_interpolate.cpp" | 267 ------------------ 3 files changed, 436 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/.keep" delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate.cpp" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/.keep" deleted file mode 100644 index e69de29..0000000 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" deleted file mode 100644 index c5ac240..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" +++ /dev/null @@ -1,169 +0,0 @@ -#include -#include -#include // memset -#include // rand, RAND_MAX -#include // sqrtf -#include -#include -using namespace std; -float randomf(){ - return (rand()+0.5)/(RAND_MAX+1.0); -} -static double get_time(){ - timespec tp; - clock_gettime(CLOCK_MONOTONIC,&tp); - return tp.tv_sec+tp.tv_nsec*1e-9; -} - -// Find three nearest neigbors with square distance -// input: xyz1 (b,n,3), xyz2(b,m,3) -// output: dist (b,n,3), idx (b,n,3) -void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { - for (int i=0;i -#include -#include // memset -#include // rand, RAND_MAX -#include // sqrtf -#include "tensorflow/core/framework/op.h" -#include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/framework/shape_inference.h" -#include "tensorflow/core/framework/common_shape_fns.h" - -#pragma GCC diagnostic ignored "-Wunused-result" - -using namespace tensorflow; - -REGISTER_OP("ThreeNN") - .Input("xyz1: float32") - .Input("xyz2: float32") - .Output("dist: float32") - .Output("idx: int32") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->input(0)); - c->set_output(1, c->input(0)); - return Status::OK(); - }); -REGISTER_OP("ThreeInterpolate") - .Input("points: float32") - .Input("idx: int32") - .Input("weight: float32") - .Output("out: float32") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - ::tensorflow::shape_inference::ShapeHandle dims1; // (b,m,c) - c->WithRank(c->input(0), 3, &dims1); - ::tensorflow::shape_inference::ShapeHandle dims2; // (b,n,3) - c->WithRank(c->input(1), 3, &dims2); - // (b,n,c) - ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)}); - c->set_output(0, output); - return Status::OK(); - }); -REGISTER_OP("ThreeInterpolateGrad") - .Input("points: float32") - .Input("idx: int32") - .Input("weight: float32") - .Input("grad_out: float32") - .Output("grad_points: float32") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->input(0)); - return Status::OK(); - }); - -float randomf(){ - return (rand()+0.5)/(RAND_MAX+1.0); -} -static double get_time(){ - timespec tp; - clock_gettime(CLOCK_MONOTONIC,&tp); - return tp.tv_sec+tp.tv_nsec*1e-9; -} - -// Find three nearest neigbors with square distance -// input: xyz1 (b,n,3), xyz2(b,m,3) -// output: dist (b,n,3), idx (b,n,3) -void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { - for (int i=0;iinput(0); - OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,n,3) xyz1 shape.")); - int b = xyz1_tensor.shape().dim_size(0); - int n = xyz1_tensor.shape().dim_size(1); - - const Tensor& xyz2_tensor = context->input(1); - OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,m,3) xyz2 shape.")); - int m = xyz2_tensor.shape().dim_size(1); - - Tensor *dist_tensor = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,n,3}, &dist_tensor)); - Tensor *idx_tensor = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,n,3}, &idx_tensor)); - - auto xyz1_flat = xyz1_tensor.flat(); - const float *xyz1 = &(xyz1_flat(0)); - auto xyz2_flat = xyz2_tensor.flat(); - const float *xyz2 = &(xyz2_flat(0)); - auto dist_flat = dist_tensor->flat(); - float *dist = &(dist_flat(0)); - auto idx_flat = idx_tensor->flat(); - int *idx = &(idx_flat(0)); - threenn_cpu(b,n,m,xyz1,xyz2,dist,idx); - } -}; -REGISTER_KERNEL_BUILDER(Name("ThreeNN").Device(DEVICE_CPU), ThreeNNOp); - - - -class ThreeInterpolateOp: public OpKernel{ - public: - explicit ThreeInterpolateOp(OpKernelConstruction * context):OpKernel(context){} - - void Compute(OpKernelContext * context) override { - const Tensor& points_tensor=context->input(0); - OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolate expects (b,m,c) points shape")); - int b = points_tensor.shape().dim_size(0); - int m = points_tensor.shape().dim_size(1); - int c = points_tensor.shape().dim_size(2); - - const Tensor& idx_tensor=context->input(1); - OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b && idx_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) idx shape")); - int n = idx_tensor.shape().dim_size(1); - const Tensor& weight_tensor=context->input(2); - OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) weight shape")); - - Tensor * out_tensor = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &out_tensor)); - - auto points_flat = points_tensor.flat(); - const float *points = &(points_flat(0)); - auto idx_flat = idx_tensor.flat(); - const int *idx = &(idx_flat(0)); - auto weight_flat = weight_tensor.flat(); - const float *weight = &(weight_flat(0)); - auto out_flat = out_tensor->flat(); - float *out = &(out_flat(0)); - threeinterpolate_cpu(b,m,c,n,points,idx,weight,out); - } -}; -REGISTER_KERNEL_BUILDER(Name("ThreeInterpolate").Device(DEVICE_CPU),ThreeInterpolateOp); - - -class ThreeInterpolateGradOp: public OpKernel{ - public: - explicit ThreeInterpolateGradOp(OpKernelConstruction * context):OpKernel(context){} - - void Compute(OpKernelContext * context) override { - const Tensor& points_tensor=context->input(0); - OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,m,c) points shape")); - int b = points_tensor.shape().dim_size(0); - int m = points_tensor.shape().dim_size(1); - int c = points_tensor.shape().dim_size(2); - - const Tensor& idx_tensor=context->input(1); - OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) idx shape")); - int n = idx_tensor.shape().dim_size(1); - const Tensor& weight_tensor=context->input(2); - OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) weight shape")); - - const Tensor& grad_out_tensor=context->input(3); - OP_REQUIRES(context,grad_out_tensor.dims()==3 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==n && grad_out_tensor.shape().dim_size(2)==c, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,c) grad_out shape")); - - Tensor * grad_points_tensor = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,c}, &grad_points_tensor)); - - auto points_flat = points_tensor.flat(); - const float *points = &(points_flat(0)); - auto idx_flat = idx_tensor.flat(); - const int *idx = &(idx_flat(0)); - auto weight_flat = weight_tensor.flat(); - const float *weight = &(weight_flat(0)); - auto grad_out_flat = grad_out_tensor.flat(); - const float *grad_out = &(grad_out_flat(0)); - auto grad_points_flat = grad_points_tensor->flat(); - float *grad_points = &(grad_points_flat(0)); - memset(grad_points, 0, sizeof(float)*b*m*c); - threeinterpolate_grad_cpu(b,n,c,m,grad_out,idx,weight,grad_points); - } -}; -REGISTER_KERNEL_BUILDER(Name("ThreeInterpolateGrad").Device(DEVICE_CPU),ThreeInterpolateGradOp); - - -- Gitee From 5902994456d397c684465a19ddff95b467b7b7e6 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:48:34 +0000 Subject: [PATCH 13/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/tf=5Fops?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/.keep" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/.keep" deleted file mode 100644 index e69de29..0000000 -- Gitee From 74031f928e40ea998ca0b034fd3b29654c2036df Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:54:16 +0000 Subject: [PATCH 14/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/models/pointnet2=5Fcls=5Fmsg.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../models/pointnet2_cls_msg.py/.keep" | 61 ------------------- 1 file changed, 61 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" deleted file mode 100644 index c750c42..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_msg.py/.keep" +++ /dev/null @@ -1,61 +0,0 @@ -""" - PointNet++ Model for point clouds classification -""" - -import os -import sys -BASE_DIR = os.path.dirname(__file__) -sys.path.append(BASE_DIR) -sys.path.append(os.path.join(BASE_DIR, '../utils')) -import tensorflow as tf -import numpy as np -import tf_util -from pointnet_util import pointnet_sa_module - -def placeholder_inputs(batch_size, num_point): - pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) - labels_pl = tf.placeholder(tf.int32, shape=(batch_size)) - return pointclouds_pl, labels_pl - -def get_model(point_cloud, is_training, bn_decay=None): - """ Classification PointNet, input is BxNx3, output Bx40 """ - batch_size = point_cloud.get_shape()[0].value - num_point = point_cloud.get_shape()[1].value - end_points = {} - l0_xyz = point_cloud - l0_points = None - end_points['l0_xyz'] = l0_xyz - - # Set abstraction layers - # Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4). - # So we only use NCHW for layer 1 until this issue can be resolved. - l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_nchw=True) - l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2') - l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3') - - # Fully connected layers - net = tf.reshape(l3_points, [batch_size, -1]) - net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) - net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1') - net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay) - net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2') - net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3') - - return net, end_points - - -def get_loss(pred, label, end_points): - """ pred: B*NUM_CLASSES, - label: B, """ - loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) - classify_loss = tf.reduce_mean(loss) - tf.summary.scalar('classify loss', classify_loss) - tf.add_to_collection('losses', classify_loss) - return classify_loss - - -if __name__=='__main__': - with tf.Graph().as_default(): - inputs = tf.zeros((32,1024,3)) - output, _ = get_model(inputs, tf.constant(True)) - print(output) -- Gitee From 9b6df4fa03e7ea1698136b47806586903986aff7 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:54:43 +0000 Subject: [PATCH 15/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/models/pointnet.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../models/pointnet_cls.py" | 40 +++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py" new file mode 100644 index 0000000..520e8f2 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py" @@ -0,0 +1,40 @@ +import torch.nn as nn +import torch.utils.data +import torch.nn.functional as F +from pointnet import PointNetEncoder, feature_transform_reguliarzer + +class get_model(nn.Module): + def __init__(self, k=40, normal_channel=True): + super(get_model, self).__init__() + if normal_channel: + channel = 6 + else: + channel = 3 + self.feat = PointNetEncoder(global_feat=True, feature_transform=True, channel=channel) + self.fc1 = nn.Linear(1024, 512) + self.fc2 = nn.Linear(512, 256) + self.fc3 = nn.Linear(256, k) + self.dropout = nn.Dropout(p=0.4) + self.bn1 = nn.BatchNorm1d(512) + self.bn2 = nn.BatchNorm1d(256) + self.relu = nn.ReLU() + + def forward(self, x): + x, trans, trans_feat = self.feat(x) + x = F.relu(self.bn1(self.fc1(x))) + x = F.relu(self.bn2(self.dropout(self.fc2(x)))) + x = self.fc3(x) + x = F.log_softmax(x, dim=1) + return x, trans_feat + +class get_loss(torch.nn.Module): + def __init__(self, mat_diff_loss_scale=0.001): + super(get_loss, self).__init__() + self.mat_diff_loss_scale = mat_diff_loss_scale + + def forward(self, pred, target, trans_feat): + loss = F.nll_loss(pred, target) + mat_diff_loss = feature_transform_reguliarzer(trans_feat) + + total_loss = loss + mat_diff_loss * self.mat_diff_loss_scale + return total_loss -- Gitee From 8be694b8c145aeafacda5bedd6c282760906f8df Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:55:14 +0000 Subject: [PATCH 16/69] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20data=5Futils?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../data_utils/.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/.keep" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/.keep" new file mode 100644 index 0000000..e69de29 -- Gitee From 808d02b5aa64dd48010e5e5b86c5199039bdfe52 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:58:01 +0000 Subject: [PATCH 17/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data=5Futils/coll?= =?UTF-8?q?ect=5Findoor3d=5Fdata.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data_utils/collect_indoor3d_data.py" | 24 +++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/collect_indoor3d_data.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/collect_indoor3d_data.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/collect_indoor3d_data.py" new file mode 100644 index 0000000..f1bdeeb --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/collect_indoor3d_data.py" @@ -0,0 +1,24 @@ +import os +import sys +from indoor3d_util import DATA_PATH, collect_point_label + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = os.path.dirname(BASE_DIR) +sys.path.append(BASE_DIR) + +anno_paths = [line.rstrip() for line in open(os.path.join(BASE_DIR, 'meta/anno_paths.txt'))] +anno_paths = [os.path.join(DATA_PATH, p) for p in anno_paths] + +output_folder = os.path.join(ROOT_DIR, 'data/stanford_indoor3d') +if not os.path.exists(output_folder): + os.mkdir(output_folder) + +# Note: there is an extra character in the v1.2 data in Area_5/hallway_6. It's fixed manually. +for anno_path in anno_paths: + print(anno_path) + try: + elements = anno_path.split('/') + out_filename = elements[-3]+'_'+elements[-2]+'.npy' # Area_1_hallway_1.npy + collect_point_label(anno_path, os.path.join(output_folder, out_filename), 'numpy') + except: + print(anno_path, 'ERROR!!') -- Gitee From bdc411069fce3e4e2b07bcee53dcec063d530af0 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:58:35 +0000 Subject: [PATCH 18/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data=5Futils/indo?= =?UTF-8?q?or3d=5Futil.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data_utils/indoor3d_util.py" | 598 ++++++++++++++++++ 1 file changed, 598 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/indoor3d_util.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/indoor3d_util.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/indoor3d_util.py" new file mode 100644 index 0000000..42a7d97 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/indoor3d_util.py" @@ -0,0 +1,598 @@ +import numpy as np +import glob +import os +import sys + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = os.path.dirname(BASE_DIR) +sys.path.append(BASE_DIR) + +DATA_PATH = os.path.join(ROOT_DIR, 'data','s3dis', 'Stanford3dDataset_v1.2_Aligned_Version') +g_classes = [x.rstrip() for x in open(os.path.join(BASE_DIR, 'meta/class_names.txt'))] +g_class2label = {cls: i for i,cls in enumerate(g_classes)} +g_class2color = {'ceiling': [0,255,0], + 'floor': [0,0,255], + 'wall': [0,255,255], + 'beam': [255,255,0], + 'column': [255,0,255], + 'window': [100,100,255], + 'door': [200,200,100], + 'table': [170,120,200], + 'chair': [255,0,0], + 'sofa': [200,100,100], + 'bookcase': [10,200,100], + 'board': [200,200,200], + 'clutter': [50,50,50]} +g_easy_view_labels = [7,8,9,10,11,1] +g_label2color = {g_classes.index(cls): g_class2color[cls] for cls in g_classes} + + +# ----------------------------------------------------------------------------- +# CONVERT ORIGINAL DATA TO OUR DATA_LABEL FILES +# ----------------------------------------------------------------------------- + +def collect_point_label(anno_path, out_filename, file_format='txt'): + """ Convert original dataset files to data_label file (each line is XYZRGBL). + We aggregated all the points from each instance in the room. + + Args: + anno_path: path to annotations. e.g. Area_1/office_2/Annotations/ + out_filename: path to save collected points and labels (each line is XYZRGBL) + file_format: txt or numpy, determines what file format to save. + Returns: + None + Note: + the points are shifted before save, the most negative point is now at origin. + """ + points_list = [] + for f in glob.glob(os.path.join(anno_path, '*.txt')): + cls = os.path.basename(f).split('_')[0] + print(f) + if cls not in g_classes: # note: in some room there is 'staris' class.. + cls = 'clutter' + + points = np.loadtxt(f) + labels = np.ones((points.shape[0],1)) * g_class2label[cls] + points_list.append(np.concatenate([points, labels], 1)) # Nx7 + + data_label = np.concatenate(points_list, 0) + xyz_min = np.amin(data_label, axis=0)[0:3] + data_label[:, 0:3] -= xyz_min + + if file_format=='txt': + fout = open(out_filename, 'w') + for i in range(data_label.shape[0]): + fout.write('%f %f %f %d %d %d %d\n' % \ + (data_label[i,0], data_label[i,1], data_label[i,2], + data_label[i,3], data_label[i,4], data_label[i,5], + data_label[i,6])) + fout.close() + elif file_format=='numpy': + np.save(out_filename, data_label) + else: + print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \ + (file_format)) + exit() + +def data_to_obj(data,name='example.obj',no_wall=True): + fout = open(name, 'w') + label = data[:, -1].astype(int) + for i in range(data.shape[0]): + if no_wall and ((label[i] == 2) or (label[i]==0)): + continue + fout.write('v %f %f %f %d %d %d\n' % \ + (data[i, 0], data[i, 1], data[i, 2], data[i, 3], data[i, 4], data[i, 5])) + fout.close() + +def point_label_to_obj(input_filename, out_filename, label_color=True, easy_view=False, no_wall=False): + """ For visualization of a room from data_label file, + input_filename: each line is X Y Z R G B L + out_filename: OBJ filename, + visualize input file by coloring point with label color + easy_view: only visualize furnitures and floor + """ + data_label = np.loadtxt(input_filename) + data = data_label[:, 0:6] + label = data_label[:, -1].astype(int) + fout = open(out_filename, 'w') + for i in range(data.shape[0]): + color = g_label2color[label[i]] + if easy_view and (label[i] not in g_easy_view_labels): + continue + if no_wall and ((label[i] == 2) or (label[i]==0)): + continue + if label_color: + fout.write('v %f %f %f %d %d %d\n' % \ + (data[i,0], data[i,1], data[i,2], color[0], color[1], color[2])) + else: + fout.write('v %f %f %f %d %d %d\n' % \ + (data[i,0], data[i,1], data[i,2], data[i,3], data[i,4], data[i,5])) + fout.close() + + + +# ----------------------------------------------------------------------------- +# PREPARE BLOCK DATA FOR DEEPNETS TRAINING/TESTING +# ----------------------------------------------------------------------------- + +def sample_data(data, num_sample): + """ data is in N x ... + we want to keep num_samplexC of them. + if N > num_sample, we will randomly keep num_sample of them. + if N < num_sample, we will randomly duplicate samples. + """ + N = data.shape[0] + if (N == num_sample): + return data, range(N) + elif (N > num_sample): + sample = np.random.choice(N, num_sample) + return data[sample, ...], sample + else: + sample = np.random.choice(N, num_sample-N) + dup_data = data[sample, ...] + return np.concatenate([data, dup_data], 0), list(range(N))+list(sample) + +def sample_data_label(data, label, num_sample): + new_data, sample_indices = sample_data(data, num_sample) + new_label = label[sample_indices] + return new_data, new_label + +def room2blocks(data, label, num_point, block_size=1.0, stride=1.0, + random_sample=False, sample_num=None, sample_aug=1): + """ Prepare block training data. + Args: + data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] + assumes the data is shifted (min point is origin) and aligned + (aligned with XYZ axis) + label: N size uint8 numpy array from 0-12 + num_point: int, how many points to sample in each block + block_size: float, physical size of the block in meters + stride: float, stride for block sweeping + random_sample: bool, if True, we will randomly sample blocks in the room + sample_num: int, if random sample, how many blocks to sample + [default: room area] + sample_aug: if random sample, how much aug + Returns: + block_datas: K x num_point x 6 np array of XYZRGB, RGB is in [0,1] + block_labels: K x num_point x 1 np array of uint8 labels + + TODO: for this version, blocking is in fixed, non-overlapping pattern. + """ + assert(stride<=block_size) + + limit = np.amax(data, 0)[0:3] + + # Get the corner location for our sampling blocks + xbeg_list = [] + ybeg_list = [] + if not random_sample: + num_block_x = int(np.ceil((limit[0] - block_size) / stride)) + 1 + num_block_y = int(np.ceil(collect_point_label(limit[1] - block_size) / stride)) + 1 + for i in range(num_block_x): + for j in range(num_block_y): + xbeg_list.append(i*stride) + ybeg_list.append(j*stride) + else: + num_block_x = int(np.ceil(limit[0] / block_size)) + num_block_y = int(np.ceil(limit[1] / block_size)) + if sample_num is None: + sample_num = num_block_x * num_block_y * sample_aug + for _ in range(sample_num): + xbeg = np.random.uniform(-block_size, limit[0]) + ybeg = np.random.uniform(-block_size, limit[1]) + xbeg_list.append(xbeg) + ybeg_list.append(ybeg) + + # Collect blocks + block_data_list = [] + block_label_list = [] + idx = 0 + for idx in range(len(xbeg_list)): + xbeg = xbeg_list[idx] + ybeg = ybeg_list[idx] + xcond = (data[:,0]<=xbeg+block_size) & (data[:,0]>=xbeg) + ycond = (data[:,1]<=ybeg+block_size) & (data[:,1]>=ybeg) + cond = xcond & ycond + if np.sum(cond) < 100: # discard block if there are less than 100 pts. + continue + + block_data = data[cond, :] + block_label = label[cond] + + # randomly subsample data + block_data_sampled, block_label_sampled = \ + sample_data_label(block_data, block_label, num_point) + block_data_list.append(np.expand_dims(block_data_sampled, 0)) + block_label_list.append(np.expand_dims(block_label_sampled, 0)) + + return np.concatenate(block_data_list, 0), \ + np.concatenate(block_label_list, 0) + + +def room2blocks_plus(data_label, num_point, block_size, stride, + random_sample, sample_num, sample_aug): + """ room2block with input filename and RGB preprocessing. + """ + data = data_label[:,0:6] + data[:,3:6] /= 255.0 + label = data_label[:,-1].astype(np.uint8) + + return room2blocks(data, label, num_point, block_size, stride, + random_sample, sample_num, sample_aug) + +def room2blocks_wrapper(data_label_filename, num_point, block_size=1.0, stride=1.0, + random_sample=False, sample_num=None, sample_aug=1): + if data_label_filename[-3:] == 'txt': + data_label = np.loadtxt(data_label_filename) + elif data_label_filename[-3:] == 'npy': + data_label = np.load(data_label_filename) + else: + print('Unknown file type! exiting.') + exit() + return room2blocks_plus(data_label, num_point, block_size, stride, + random_sample, sample_num, sample_aug) + +def room2blocks_plus_normalized(data_label, num_point, block_size, stride, + random_sample, sample_num, sample_aug): + """ room2block, with input filename and RGB preprocessing. + for each block centralize XYZ, add normalized XYZ as 678 channels + """ + data = data_label[:,0:6] + data[:,3:6] /= 255.0 + label = data_label[:,-1].astype(np.uint8) + max_room_x = max(data[:,0]) + max_room_y = max(data[:,1]) + max_room_z = max(data[:,2]) + + data_batch, label_batch = room2blocks(data, label, num_point, block_size, stride, + random_sample, sample_num, sample_aug) + new_data_batch = np.zeros((data_batch.shape[0], num_point, 9)) + for b in range(data_batch.shape[0]): + new_data_batch[b, :, 6] = data_batch[b, :, 0]/max_room_x + new_data_batch[b, :, 7] = data_batch[b, :, 1]/max_room_y + new_data_batch[b, :, 8] = data_batch[b, :, 2]/max_room_z + minx = min(data_batch[b, :, 0]) + miny = min(data_batch[b, :, 1]) + data_batch[b, :, 0] -= (minx+block_size/2) + data_batch[b, :, 1] -= (miny+block_size/2) + new_data_batch[:, :, 0:6] = data_batch + return new_data_batch, label_batch + + +def room2blocks_wrapper_normalized(data_label_filename, num_point, block_size=1.0, stride=1.0, + random_sample=False, sample_num=None, sample_aug=1): + if data_label_filename[-3:] == 'txt': + data_label = np.loadtxt(data_label_filename) + elif data_label_filename[-3:] == 'npy': + data_label = np.load(data_label_filename) + else: + print('Unknown file type! exiting.') + exit() + return room2blocks_plus_normalized(data_label, num_point, block_size, stride, + random_sample, sample_num, sample_aug) + +def room2samples(data, label, sample_num_point): + """ Prepare whole room samples. + + Args: + data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] + assumes the data is shifted (min point is origin) and + aligned (aligned with XYZ axis) + label: N size uint8 numpy array from 0-12 + sample_num_point: int, how many points to sample in each sample + Returns: + sample_datas: K x sample_num_point x 9 + numpy array of XYZRGBX'Y'Z', RGB is in [0,1] + sample_labels: K x sample_num_point x 1 np array of uint8 labels + """ + N = data.shape[0] + order = np.arange(N) + np.random.shuffle(order) + data = data[order, :] + label = label[order] + + batch_num = int(np.ceil(N / float(sample_num_point))) + sample_datas = np.zeros((batch_num, sample_num_point, 6)) + sample_labels = np.zeros((batch_num, sample_num_point, 1)) + + for i in range(batch_num): + beg_idx = i*sample_num_point + end_idx = min((i+1)*sample_num_point, N) + num = end_idx - beg_idx + sample_datas[i,0:num,:] = data[beg_idx:end_idx, :] + sample_labels[i,0:num,0] = label[beg_idx:end_idx] + if num < sample_num_point: + makeup_indices = np.random.choice(N, sample_num_point - num) + sample_datas[i,num:,:] = data[makeup_indices, :] + sample_labels[i,num:,0] = label[makeup_indices] + return sample_datas, sample_labels + +def room2samples_plus_normalized(data_label, num_point): + """ room2sample, with input filename and RGB preprocessing. + for each block centralize XYZ, add normalized XYZ as 678 channels + """ + data = data_label[:,0:6] + data[:,3:6] /= 255.0 + label = data_label[:,-1].astype(np.uint8) + max_room_x = max(data[:,0]) + max_room_y = max(data[:,1]) + max_room_z = max(data[:,2]) + #print(max_room_x, max_room_y, max_room_z) + + data_batch, label_batch = room2samples(data, label, num_point) + new_data_batch = np.zeros((data_batch.shape[0], num_point, 9)) + for b in range(data_batch.shape[0]): + new_data_batch[b, :, 6] = data_batch[b, :, 0]/max_room_x + new_data_batch[b, :, 7] = data_batch[b, :, 1]/max_room_y + new_data_batch[b, :, 8] = data_batch[b, :, 2]/max_room_z + #minx = min(data_batch[b, :, 0]) + #miny = min(data_batch[b, :, 1]) + #data_batch[b, :, 0] -= (minx+block_size/2) + #data_batch[b, :, 1] -= (miny+block_size/2) + new_data_batch[:, :, 0:6] = data_batch + return new_data_batch, label_batch + + +def room2samples_wrapper_normalized(data_label_filename, num_point): + if data_label_filename[-3:] == 'txt': + data_label = np.loadtxt(data_label_filename) + elif data_label_filename[-3:] == 'npy': + data_label = np.load(data_label_filename) + else: + print('Unknown file type! exiting.') + exit() + return room2samples_plus_normalized(data_label, num_point) + + +# ----------------------------------------------------------------------------- +# EXTRACT INSTANCE BBOX FROM ORIGINAL DATA (for detection evaluation) +# ----------------------------------------------------------------------------- + +def collect_bounding_box(anno_path, out_filename): + """ Compute bounding boxes from each instance in original dataset files on + one room. **We assume the bbox is aligned with XYZ coordinate.** + + Args: + anno_path: path to annotations. e.g. Area_1/office_2/Annotations/ + out_filename: path to save instance bounding boxes for that room. + each line is x1 y1 z1 x2 y2 z2 label, + where (x1,y1,z1) is the point on the diagonal closer to origin + Returns: + None + Note: + room points are shifted, the most negative point is now at origin. + """ + bbox_label_list = [] + + for f in glob.glob(os.path.join(anno_path, '*.txt')): + cls = os.path.basename(f).split('_')[0] + if cls not in g_classes: # note: in some room there is 'staris' class.. + cls = 'clutter' + points = np.loadtxt(f) + label = g_class2label[cls] + # Compute tightest axis aligned bounding box + xyz_min = np.amin(points[:, 0:3], axis=0) + xyz_max = np.amax(points[:, 0:3], axis=0) + ins_bbox_label = np.expand_dims( + np.concatenate([xyz_min, xyz_max, np.array([label])], 0), 0) + bbox_label_list.append(ins_bbox_label) + + bbox_label = np.concatenate(bbox_label_list, 0) + room_xyz_min = np.amin(bbox_label[:, 0:3], axis=0) + bbox_label[:, 0:3] -= room_xyz_min + bbox_label[:, 3:6] -= room_xyz_min + + fout = open(out_filename, 'w') + for i in range(bbox_label.shape[0]): + fout.write('%f %f %f %f %f %f %d\n' % \ + (bbox_label[i,0], bbox_label[i,1], bbox_label[i,2], + bbox_label[i,3], bbox_label[i,4], bbox_label[i,5], + bbox_label[i,6])) + fout.close() + +def bbox_label_to_obj(input_filename, out_filename_prefix, easy_view=False): + """ Visualization of bounding boxes. + + Args: + input_filename: each line is x1 y1 z1 x2 y2 z2 label + out_filename_prefix: OBJ filename prefix, + visualize object by g_label2color + easy_view: if True, only visualize furniture and floor + Returns: + output a list of OBJ file and MTL files with the same prefix + """ + bbox_label = np.loadtxt(input_filename) + bbox = bbox_label[:, 0:6] + label = bbox_label[:, -1].astype(int) + v_cnt = 0 # count vertex + ins_cnt = 0 # count instance + for i in range(bbox.shape[0]): + if easy_view and (label[i] not in g_easy_view_labels): + continue + obj_filename = out_filename_prefix+'_'+g_classes[label[i]]+'_'+str(ins_cnt)+'.obj' + mtl_filename = out_filename_prefix+'_'+g_classes[label[i]]+'_'+str(ins_cnt)+'.mtl' + fout_obj = open(obj_filename, 'w') + fout_mtl = open(mtl_filename, 'w') + fout_obj.write('mtllib %s\n' % (os.path.basename(mtl_filename))) + + length = bbox[i, 3:6] - bbox[i, 0:3] + a = length[0] + b = length[1] + c = length[2] + x = bbox[i, 0] + y = bbox[i, 1] + z = bbox[i, 2] + color = np.array(g_label2color[label[i]], dtype=float) / 255.0 + + material = 'material%d' % (ins_cnt) + fout_obj.write('usemtl %s\n' % (material)) + fout_obj.write('v %f %f %f\n' % (x,y,z+c)) + fout_obj.write('v %f %f %f\n' % (x,y+b,z+c)) + fout_obj.write('v %f %f %f\n' % (x+a,y+b,z+c)) + fout_obj.write('v %f %f %f\n' % (x+a,y,z+c)) + fout_obj.write('v %f %f %f\n' % (x,y,z)) + fout_obj.write('v %f %f %f\n' % (x,y+b,z)) + fout_obj.write('v %f %f %f\n' % (x+a,y+b,z)) + fout_obj.write('v %f %f %f\n' % (x+a,y,z)) + fout_obj.write('g default\n') + v_cnt = 0 # for individual box + fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 3+v_cnt, 2+v_cnt, 1+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (1+v_cnt, 2+v_cnt, 6+v_cnt, 5+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (7+v_cnt, 6+v_cnt, 2+v_cnt, 3+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 8+v_cnt, 7+v_cnt, 3+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 8+v_cnt, 4+v_cnt, 1+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 6+v_cnt, 7+v_cnt, 8+v_cnt)) + fout_obj.write('\n') + + fout_mtl.write('newmtl %s\n' % (material)) + fout_mtl.write('Kd %f %f %f\n' % (color[0], color[1], color[2])) + fout_mtl.write('\n') + fout_obj.close() + fout_mtl.close() + + v_cnt += 8 + ins_cnt += 1 + +def bbox_label_to_obj_room(input_filename, out_filename_prefix, easy_view=False, permute=None, center=False, exclude_table=False): + """ Visualization of bounding boxes. + + Args: + input_filename: each line is x1 y1 z1 x2 y2 z2 label + out_filename_prefix: OBJ filename prefix, + visualize object by g_label2color + easy_view: if True, only visualize furniture and floor + permute: if not None, permute XYZ for rendering, e.g. [0 2 1] + center: if True, move obj to have zero origin + Returns: + output a list of OBJ file and MTL files with the same prefix + """ + bbox_label = np.loadtxt(input_filename) + bbox = bbox_label[:, 0:6] + if permute is not None: + assert(len(permute)==3) + permute = np.array(permute) + bbox[:,0:3] = bbox[:,permute] + bbox[:,3:6] = bbox[:,permute+3] + if center: + xyz_max = np.amax(bbox[:,3:6], 0) + bbox[:,0:3] -= (xyz_max/2.0) + bbox[:,3:6] -= (xyz_max/2.0) + bbox /= np.max(xyz_max/2.0) + label = bbox_label[:, -1].astype(int) + obj_filename = out_filename_prefix+'.obj' + mtl_filename = out_filename_prefix+'.mtl' + + fout_obj = open(obj_filename, 'w') + fout_mtl = open(mtl_filename, 'w') + fout_obj.write('mtllib %s\n' % (os.path.basename(mtl_filename))) + v_cnt = 0 # count vertex + ins_cnt = 0 # count instance + for i in range(bbox.shape[0]): + if easy_view and (label[i] not in g_easy_view_labels): + continue + if exclude_table and label[i] == g_classes.index('table'): + continue + + length = bbox[i, 3:6] - bbox[i, 0:3] + a = length[0] + b = length[1] + c = length[2] + x = bbox[i, 0] + y = bbox[i, 1] + z = bbox[i, 2] + color = np.array(g_label2color[label[i]], dtype=float) / 255.0 + + material = 'material%d' % (ins_cnt) + fout_obj.write('usemtl %s\n' % (material)) + fout_obj.write('v %f %f %f\n' % (x,y,z+c)) + fout_obj.write('v %f %f %f\n' % (x,y+b,z+c)) + fout_obj.write('v %f %f %f\n' % (x+a,y+b,z+c)) + fout_obj.write('v %f %f %f\n' % (x+a,y,z+c)) + fout_obj.write('v %f %f %f\n' % (x,y,z)) + fout_obj.write('v %f %f %f\n' % (x,y+b,z)) + fout_obj.write('v %f %f %f\n' % (x+a,y+b,z)) + fout_obj.write('v %f %f %f\n' % (x+a,y,z)) + fout_obj.write('g default\n') + fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 3+v_cnt, 2+v_cnt, 1+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (1+v_cnt, 2+v_cnt, 6+v_cnt, 5+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (7+v_cnt, 6+v_cnt, 2+v_cnt, 3+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 8+v_cnt, 7+v_cnt, 3+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 8+v_cnt, 4+v_cnt, 1+v_cnt)) + fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 6+v_cnt, 7+v_cnt, 8+v_cnt)) + fout_obj.write('\n') + + fout_mtl.write('newmtl %s\n' % (material)) + fout_mtl.write('Kd %f %f %f\n' % (color[0], color[1], color[2])) + fout_mtl.write('\n') + + v_cnt += 8 + ins_cnt += 1 + + fout_obj.close() + fout_mtl.close() + + +def collect_point_bounding_box(anno_path, out_filename, file_format): + """ Compute bounding boxes from each instance in original dataset files on + one room. **We assume the bbox is aligned with XYZ coordinate.** + Save both the point XYZRGB and the bounding box for the point's + parent element. + + Args: + anno_path: path to annotations. e.g. Area_1/office_2/Annotations/ + out_filename: path to save instance bounding boxes for each point, + plus the point's XYZRGBL + each line is XYZRGBL offsetX offsetY offsetZ a b c, + where cx = X+offsetX, cy=X+offsetY, cz=Z+offsetZ + where (cx,cy,cz) is center of the box, a,b,c are distances from center + to the surfaces of the box, i.e. x1 = cx-a, x2 = cx+a, y1=cy-b etc. + file_format: output file format, txt or numpy + Returns: + None + + Note: + room points are shifted, the most negative point is now at origin. + """ + point_bbox_list = [] + + for f in glob.glob(os.path.join(anno_path, '*.txt')): + cls = os.path.basename(f).split('_')[0] + if cls not in g_classes: # note: in some room there is 'staris' class.. + cls = 'clutter' + points = np.loadtxt(f) # Nx6 + label = g_class2label[cls] # N, + # Compute tightest axis aligned bounding box + xyz_min = np.amin(points[:, 0:3], axis=0) # 3, + xyz_max = np.amax(points[:, 0:3], axis=0) # 3, + xyz_center = (xyz_min + xyz_max) / 2 + dimension = (xyz_max - xyz_min) / 2 + + xyz_offsets = xyz_center - points[:,0:3] # Nx3 + dimensions = np.ones((points.shape[0],3)) * dimension # Nx3 + labels = np.ones((points.shape[0],1)) * label # N + point_bbox_list.append(np.concatenate([points, labels, + xyz_offsets, dimensions], 1)) # Nx13 + + point_bbox = np.concatenate(point_bbox_list, 0) # KxNx13 + room_xyz_min = np.amin(point_bbox[:, 0:3], axis=0) + point_bbox[:, 0:3] -= room_xyz_min + + if file_format == 'txt': + fout = open(out_filename, 'w') + for i in range(point_bbox.shape[0]): + fout.write('%f %f %f %d %d %d %d %f %f %f %f %f %f\n' % \ + (point_bbox[i,0], point_bbox[i,1], point_bbox[i,2], + point_bbox[i,3], point_bbox[i,4], point_bbox[i,5], + point_bbox[i,6], + point_bbox[i,7], point_bbox[i,8], point_bbox[i,9], + point_bbox[i,10], point_bbox[i,11], point_bbox[i,12])) + + fout.close() + elif file_format == 'numpy': + np.save(out_filename, point_bbox) + else: + print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \ + (file_format)) + exit() + + -- Gitee From 1c4df1d5f4d4f22d67d61dc9cb74d6fc884b7328 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 01:59:05 +0000 Subject: [PATCH 19/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data=5Futils/Mode?= =?UTF-8?q?lNetDataLoader.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data_utils/ModelNetDataLoader.py" | 103 ++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/ModelNetDataLoader.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/ModelNetDataLoader.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/ModelNetDataLoader.py" new file mode 100644 index 0000000..3e343ef --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/ModelNetDataLoader.py" @@ -0,0 +1,103 @@ +import numpy as np +import warnings +import os +from torch.utils.data import Dataset +warnings.filterwarnings('ignore') + + + +def pc_normalize(pc): + centroid = np.mean(pc, axis=0) + pc = pc - centroid + m = np.max(np.sqrt(np.sum(pc**2, axis=1))) + pc = pc / m + return pc + +def farthest_point_sample(point, npoint): + """ + Input: + xyz: pointcloud data, [N, D] + npoint: number of samples + Return: + centroids: sampled pointcloud index, [npoint, D] + """ + N, D = point.shape + xyz = point[:,:3] + centroids = np.zeros((npoint,)) + distance = np.ones((N,)) * 1e10 + farthest = np.random.randint(0, N) + for i in range(npoint): + centroids[i] = farthest + centroid = xyz[farthest, :] + dist = np.sum((xyz - centroid) ** 2, -1) + mask = dist < distance + distance[mask] = dist[mask] + farthest = np.argmax(distance, -1) + point = point[centroids.astype(np.int32)] + return point + +class ModelNetDataLoader(Dataset): + def __init__(self, root, npoint=1024, split='train', uniform=False, normal_channel=True, cache_size=15000): + self.root = root + self.npoints = npoint + self.uniform = uniform + self.catfile = os.path.join(self.root, 'modelnet40_shape_names.txt') + + self.cat = [line.rstrip() for line in open(self.catfile)] + self.classes = dict(zip(self.cat, range(len(self.cat)))) + self.normal_channel = normal_channel + + shape_ids = {} + shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_train.txt'))] + shape_ids['test'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_test.txt'))] + + assert (split == 'train' or split == 'test') + shape_names = ['_'.join(x.split('_')[0:-1]) for x in shape_ids[split]] + # list of (shape_name, shape_txt_file_path) tuple + self.datapath = [(shape_names[i], os.path.join(self.root, shape_names[i], shape_ids[split][i]) + '.txt') for i + in range(len(shape_ids[split]))] + print('The size of %s data is %d'%(split,len(self.datapath))) + + self.cache_size = cache_size # how many data points to cache in memory + self.cache = {} # from index to (point_set, cls) tuple + + def __len__(self): + return len(self.datapath) + + def _get_item(self, index): + if index in self.cache: + point_set, cls = self.cache[index] + else: + fn = self.datapath[index] + cls = self.classes[self.datapath[index][0]] + cls = np.array([cls]).astype(np.int32) + point_set = np.loadtxt(fn[1], delimiter=',').astype(np.float32) + if self.uniform: + point_set = farthest_point_sample(point_set, self.npoints) + else: + point_set = point_set[0:self.npoints,:] + + point_set[:, 0:3] = pc_normalize(point_set[:, 0:3]) + + if not self.normal_channel: + point_set = point_set[:, 0:3] + + if len(self.cache) < self.cache_size: + self.cache[index] = (point_set, cls) + + return point_set, cls + + def __getitem__(self, index): + return self._get_item(index) + + + + +if __name__ == '__main__': + import torch + + data = ModelNetDataLoader('/data/modelnet40_normal_resampled/',split='train', uniform=False, normal_channel=True,) + DataLoader = torch.utils.data.DataLoader(data, batch_size=12, shuffle=True) + for point,label in DataLoader: + print(point.shape) + print(label.shape) \ No newline at end of file -- Gitee From 50f2d3418174cc1609b443476dd02d26f6b647c9 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:00:47 +0000 Subject: [PATCH 20/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data=5Futils.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data_utils/extract and save point cloud .py" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" new file mode 100644 index 0000000..e69de29 -- Gitee From fb4f22317adbcf6cb34110a16cb440b686571e61 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:01:10 +0000 Subject: [PATCH 21/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data=5Futils/extr?= =?UTF-8?q?act=20and=20save=20point=20cloud=20.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../extract and save point cloud .py" | 157 ++++++++++++++++++ 1 file changed, 157 insertions(+) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" index e69de29..c761dfd 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" @@ -0,0 +1,157 @@ +# -*- coding: utf-8 -*- +# 注意此时的laspy，有版本问题，2.0以上的版本就会出现问题，此时的版本是1.7.0 +from laspy.file import File +import os +import arcpy +import shutil +import numpy as np +np.set_printoptions(suppress=True) + +# 获取txt文件中的各种文件 +def GetData_fromFile(list_filename): + return [line.rstrip() for line in open(list_filename,'r')] + +# 只获取文件里面的shpfile中的shp文件 +def Get_specific_File(dir, ext): + need_file = [] + allfiles = os.listdir(dir) + for i in range(len(allfiles)): + name,extend = os.path.splitext(allfiles[i]) + if extend[1:] == ext: + root_shp_path = os.path.join(dir,allfiles[i]) + need_file.append(root_shp_path) + return need_file + +#分割对应点云块的shp文件，以便于下一步提取 +def split_move_shp(shp_path, out_path, label,splitfield): + arcpy.Split_analysis(in_features = shp_path, split_features = shp_path, + split_field = splitfield, out_workspace = out_path) + all_files = os.listdir(out_path) + for k in range(len(label)): + move_path_dir = os.path.join(out_path, label[k]) + for file in all_files: + name_first, ext_first = os.path.splitext(file) # 分离文件名和后缀 + f_name, ext_second = os.path.splitext(name_first) # 再次分离文件名和后缀，因为出现了.shp.xml的文件，对于.shp的不影响 + if f_name[:-4] == label[k]: + shutil.move( os.path.join(out_path,file), move_path_dir) + # 点云规范化 +def PC_NORMLIZE(pc): + centroid = np.mean(pc, axis=0) + pc = pc - centroid + m = np.max(np.sqrt(np.sum(pc**2, axis=1))) + pc = pc / m + return pc + +# 采样到2048 +def sample_data(data, num_sample): + """ data is in N x ... + we want to keep num_samplexC of them. + if N > num_sample, we will randomly keep num_sample of them. + if N < num_sample, we will randomly duplicate samples. + """ + N = data.shape[0] + if (N == num_sample): + return data + elif (N > num_sample): + sample = np.random.choice(N, num_sample) + return data[sample, ...] + else: + # 相当于从N中抽取 num_sample - N个随机数组成一维数组array，成为data的下标索引值 + sample = np.random.choice(N, num_sample - N) + dup_data = data[sample, ...] # 取数据 + # 按行拼接 + return np.concatenate([data, dup_data], axis=0) + # return np.concatenate([data, dup_data], 0), list(range(N)) + list(sample) + + +if __name__ == "__main__": + + las_name = GetData_fromFile('E:\CITYtest\FilelistAdd/lasname.txt') + label_name = GetData_fromFile('E:\CITYtest\FilelistAdd\shapelable.txt') + #自定义需要得到什么类型的文件数据 + file_class = ['Classshpfile', 'Classlasfile','Classtxtfile'] + #输入文件·路径 + input_shp_dir = 'E:\CITYtest\PCinput//' + input_las_dir = 'E:\CITYtest/NDSMlasdata//' + save_dir = 'E:\CITYtest\savepath/' + + + net_num = 2048 + for m in range(len(las_name)): + # 每个类别shp文件的路径 + shp_save_path = save_dir + '/'+ las_name[m] + '/'+ file_class[0] + las_save_path = save_dir + '/'+ las_name[m] + '/'+ file_class[1] + txt_save_path = save_dir + '/'+ las_name[m] + '/'+ file_class[2] + # 获取每整个点云块的shp文件 + # 注意shp_full 是一个列表文件，不是直接变量，引用时需要用shp_full[0,1,2] + shp_full = Get_specific_File(os.path.join(input_shp_dir, las_name[m]), 'shp') + # # # 开始对整个shp文件分割和移动 + if len(shp_full) != 0: + split_move_shp( shp_full[0], shp_save_path, label_name,'newname') + # 数据分割完毕，开始进行las点云提取 + # 得到每个label文件下的shp文件 + # 定义要提取的las路径，开始提取 + all_las_list = os.listdir(input_las_dir) + + las_ndsm_name, extend = os.path.splitext(all_las_list[m]) + + origin_las = os.path.join(input_las_dir, all_las_list[m]) + + for i in range(len(label_name)): + # 获取每个类别的单个样本shp + single_shp_path = os.path.join(shp_save_path, label_name[i]) + shp_list = Get_specific_File(single_shp_path, 'shp') + # 提取单个las的路径 + single_las_save = os.path.join(las_save_path, label_name[i]) + + if len(shp_list) != 0: + for j in range(len(shp_list)): + stir = str(j + 1) + st = stir.zfill(4) # 补零补够四位数 + arcpy.ddd.ExtractLas(in_las_dataset = origin_las, + target_folder = single_las_save, + boundary= shp_list[j], + name_suffix=label_name[i] + st, remove_vlr=True, + rearrange_points='REARRANGE_POINTS', ) + + las_list_path = os.path.join(single_las_save, las_ndsm_name + label_name[i] + st + '.las') + + # 只提取样本las中的X,Y,Z和intensity,raw_classification信息 + f = File(las_list_path, mode='rw') + # 改变样本标签为 0,1,2 + # print(f.x.shape) + point_cloud = np.vstack((f.x, f.y, f.z, f.intensity, f.raw_classification)).T + + point_cloud[:,4] = i + # 给点云加上底面，获取行数 + row_num = point_cloud.shape[0] + # 生成和行数一 样的一维0数组 + z_back = np.zeros(row_num) + # 底面 + point_back = np.vstack((f.x, f.y, z_back, f.intensity, f.raw_classification)).T + # 屋顶和底面放一起 + # print(point_back.shape) + point_cloud_add = np.vstack((point_cloud, point_back)) + # point_cloud_add = np.concatenate((point_cloud, point_back),axis=0) + # 采样到2048 + sample_pc = sample_data(point_cloud_add, net_num) + # 只取数据坐标信息归一化，中心化 + pc_norm = PC_NORMLIZE(sample_pc[:, 0:3]) + # 规范后数据代替原来的坐标 + sample_pc[:, 0:3] = pc_norm + # 存储为txt格式 + single_txt_save = os.path.join(txt_save_path,label_name[i]) + + np.savetxt( os.path.join(single_txt_save , las_name[m] + label_name[i] + st + '.txt'), + sample_pc, fmt="%.6f", delimiter=" ") + else: + print ("该类别无样本数据") + + else: + print('该块las的样本shp为空') + + print('successful') +print('over') + + + -- Gitee From 773bfa0aa6e9830cf87f9588d37ca8c7a26eda05 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:01:32 +0000 Subject: [PATCH 22/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data=5Futils.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data_utils/make hdf5_file.py" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" new file mode 100644 index 0000000..e69de29 -- Gitee From 53230781023359bde51ecb58feaeb673cb08a964 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:01:49 +0000 Subject: [PATCH 23/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data=5Futils/make?= =?UTF-8?q?=20hdf5=5Ffile.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data_utils/make hdf5_file.py" | 58 +++++++++++++++++++ 1 file changed, 58 insertions(+) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" index e69de29..8fad1d8 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" @@ -0,0 +1,58 @@ +# -*- coding: UTF-8 -*- +import os +import numpy as np +import h5py +np.set_printoptions(suppress=True) + +def getDataFiles(path_root): + filelist = os.listdir(path_root) + for i in range(len(filelist)): + filelist[i] = os.path.join(path_root,filelist[i]) + return filelist + +#得到的返回值是一维字符串数组 +def loadDataFile(path): + data = np.loadtxt(path) + point_xyz = data[:, 0:3] + label=data[:,4] + label_int = label.astype(int) + return point_xyz, label_int + +if __name__ == "__main__": + + train_file = ['train0', 'test0'] + net_num = 2048 + train_dir = 'E:\CITYtest\Trainfile//' + + for i in range(len(train_file)): + root_file = os.path.join(train_dir,train_file[i]) + DATA_FILES = getDataFiles(root_file) + + DATA_ALL = [] + LABEL_ALL = [] + for fn in range(len(DATA_FILES)): + pre_data, his_label = loadDataFile(DATA_FILES[fn]) + pre_label = his_label.reshape(net_num, 1) # 重塑为num行1列的数据 得到num个点云的标签 + + # data_label = np.hstack((pre_data, pre_label)) + DATA_ALL.append(pre_data) #列表元素 + # label一个样本中的类别是一样的，取一个数字就可以了 + LABEL_ALL.append(pre_label[0]) + + # 把DATA_ALL和LABEL_ALL的列表转换成数组格式， + out_data = np.vstack(DATA_ALL) + out_label = np.vstack(LABEL_ALL) + + # 重塑为三维数组，2048 个 2048*4 的三个数组 + data_reshape = out_data.reshape(net_num, net_num, 3) + # 写入训练数据 + filename= train_dir +'/'+ 'point_data_' + train_file[i] + '.h5' + if not os.path.exists(filename): + with h5py.File(filename, 'w') as f: + f['data'] = data_reshape + f['label'] = out_label + f.close() + else: + print('hdf5文件已存在') + +print("over") -- Gitee From cd7c404ea590c2a3274a3f423f3fbacec576354c Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:02:41 +0000 Subject: [PATCH 24/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../provider.py" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/provider.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/provider.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/provider.py" new file mode 100644 index 0000000..e69de29 -- Gitee From 649877a61b29450d9a2c8c33c65fef3bd4ec1aa4 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:02:50 +0000 Subject: [PATCH 25/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/provider.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../provider.py" | 251 ++++++++++++++++++ 1 file changed, 251 insertions(+) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/provider.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/provider.py" index e69de29..5604691 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/provider.py" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/provider.py" @@ -0,0 +1,251 @@ +import numpy as np + +def normalize_data(batch_data): + """ Normalize the batch data, use coordinates of the block centered at origin, + Input: + BxNxC array + Output: + BxNxC array + """ + B, N, C = batch_data.shape + normal_data = np.zeros((B, N, C)) + for b in range(B): + pc = batch_data[b] + centroid = np.mean(pc, axis=0) + pc = pc - centroid + m = np.max(np.sqrt(np.sum(pc ** 2, axis=1))) + pc = pc / m + normal_data[b] = pc + return normal_data + + +def shuffle_data(data, labels): + """ Shuffle data and labels. + Input: + data: B,N,... numpy array + label: B,... numpy array + Return: + shuffled data, label and shuffle indices + """ + idx = np.arange(len(labels)) + np.random.shuffle(idx) + return data[idx, ...], labels[idx], idx + +def shuffle_points(batch_data): + """ Shuffle orders of points in each point cloud -- changes FPS behavior. + Use the same shuffling idx for the entire batch. + Input: + BxNxC array + Output: + BxNxC array + """ + idx = np.arange(batch_data.shape[1]) + np.random.shuffle(idx) + return batch_data[:,idx,:] + +def rotate_point_cloud(batch_data): + """ Randomly rotate the point clouds to augument the dataset + rotation is per shape based along up direction + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, 0, sinval], + [0, 1, 0], + [-sinval, 0, cosval]]) + shape_pc = batch_data[k, ...] + rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + return rotated_data + +def rotate_point_cloud_z(batch_data): + """ Randomly rotate the point clouds to augument the dataset + rotation is per shape based along up direction + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, sinval, 0], + [-sinval, cosval, 0], + [0, 0, 1]]) + shape_pc = batch_data[k, ...] + rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + return rotated_data + +def rotate_point_cloud_with_normal(batch_xyz_normal): + ''' Randomly rotate XYZ, normal point cloud. + Input: + batch_xyz_normal: B,N,6, first three channels are XYZ, last 3 all normal + Output: + B,N,6, rotated XYZ, normal point cloud + ''' + for k in range(batch_xyz_normal.shape[0]): + rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, 0, sinval], + [0, 1, 0], + [-sinval, 0, cosval]]) + shape_pc = batch_xyz_normal[k,:,0:3] + shape_normal = batch_xyz_normal[k,:,3:6] + batch_xyz_normal[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + batch_xyz_normal[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), rotation_matrix) + return batch_xyz_normal + +def rotate_perturbation_point_cloud_with_normal(batch_data, angle_sigma=0.06, angle_clip=0.18): + """ Randomly perturb the point clouds by small rotations + Input: + BxNx6 array, original batch of point clouds and point normals + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip) + Rx = np.array([[1,0,0], + [0,np.cos(angles[0]),-np.sin(angles[0])], + [0,np.sin(angles[0]),np.cos(angles[0])]]) + Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])], + [0,1,0], + [-np.sin(angles[1]),0,np.cos(angles[1])]]) + Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0], + [np.sin(angles[2]),np.cos(angles[2]),0], + [0,0,1]]) + R = np.dot(Rz, np.dot(Ry,Rx)) + shape_pc = batch_data[k,:,0:3] + shape_normal = batch_data[k,:,3:6] + rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), R) + rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), R) + return rotated_data + + +def rotate_point_cloud_by_angle(batch_data, rotation_angle): + """ Rotate the point cloud along up direction with certain angle. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + #rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, 0, sinval], + [0, 1, 0], + [-sinval, 0, cosval]]) + shape_pc = batch_data[k,:,0:3] + rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + return rotated_data + +def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle): + """ Rotate the point cloud along up direction with certain angle. + Input: + BxNx6 array, original batch of point clouds with normal + scalar, angle of rotation + Return: + BxNx6 array, rotated batch of point clouds iwth normal + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + #rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, 0, sinval], + [0, 1, 0], + [-sinval, 0, cosval]]) + shape_pc = batch_data[k,:,0:3] + shape_normal = batch_data[k,:,3:6] + rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1,3)), rotation_matrix) + return rotated_data + + + +def rotate_perturbation_point_cloud(batch_data, angle_sigma=0.06, angle_clip=0.18): + """ Randomly perturb the point clouds by small rotations + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip) + Rx = np.array([[1,0,0], + [0,np.cos(angles[0]),-np.sin(angles[0])], + [0,np.sin(angles[0]),np.cos(angles[0])]]) + Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])], + [0,1,0], + [-np.sin(angles[1]),0,np.cos(angles[1])]]) + Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0], + [np.sin(angles[2]),np.cos(angles[2]),0], + [0,0,1]]) + R = np.dot(Rz, np.dot(Ry,Rx)) + shape_pc = batch_data[k, ...] + rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), R) + return rotated_data + + +def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05): + """ Randomly jitter points. jittering is per point. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, jittered batch of point clouds + """ + B, N, C = batch_data.shape + assert(clip > 0) + jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip) + jittered_data += batch_data + return jittered_data + +def shift_point_cloud(batch_data, shift_range=0.1): + """ Randomly shift point cloud. Shift is per point cloud. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, shifted batch of point clouds + """ + B, N, C = batch_data.shape + shifts = np.random.uniform(-shift_range, shift_range, (B,3)) + for batch_index in range(B): + batch_data[batch_index,:,:] += shifts[batch_index,:] + return batch_data + + +def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25): + """ Randomly scale the point cloud. Scale is per point cloud. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, scaled batch of point clouds + """ + B, N, C = batch_data.shape + scales = np.random.uniform(scale_low, scale_high, B) + for batch_index in range(B): + batch_data[batch_index,:,:] *= scales[batch_index] + return batch_data + +def random_point_dropout(batch_pc, max_dropout_ratio=0.875): + ''' batch_pc: BxNx3 ''' + for b in range(batch_pc.shape[0]): + dropout_ratio = np.random.random()*max_dropout_ratio # 0~0.875 + drop_idx = np.where(np.random.random((batch_pc.shape[1]))<=dropout_ratio)[0] + if len(drop_idx)>0: + batch_pc[b,drop_idx,:] = batch_pc[b,0,:] # set to the first point + return batch_pc + + + -- Gitee From 00108bb7c8a243e975adc92cee04bef5bb2f022f Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:03:20 +0000 Subject: [PATCH 26/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../train_cls.py" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" new file mode 100644 index 0000000..e69de29 -- Gitee From b45325a410a92b0ea1afc53921cf15d1870a5ea2 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:03:51 +0000 Subject: [PATCH 27/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/train=5Fcls.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../train_cls.py" | 229 ++++++++++++++++++ 1 file changed, 229 insertions(+) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" index e69de29..2eee725 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" @@ -0,0 +1,229 @@ +""" +Author: Benny +Date: Nov 2019 +""" +from data_utils.ModelNetDataLoader import ModelNetDataLoader +import argparse +import numpy as np +import os +import torch +import datetime +import logging +from pathlib import Path +from tqdm import tqdm +import sys +import provider +import importlib +import shutil + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = BASE_DIR +sys.path.append(os.path.join(ROOT_DIR, 'models')) + +torch.backends.cudnn.enabled = False +# torch.backends.cudnn.enabled = True +# torch.backends.cudnn.benchmark = False + + +# num_class = 40 +def parse_args(): + '''PARAMETERS''' + parser = argparse.ArgumentParser('PointNet') + parser.add_argument('--batch_size', type=int, default = 8, help='batch size in training [default: 24]') + + parser.add_argument('--model', default='pointnet_cls', help='model name [default: pointnet_cls]') + + parser.add_argument('--num_class', type=int, default= 3, help='class number [default: 40]') + + parser.add_argument('--epoch', default= 150, type=int, help='number of epoch in training [default: 200]') + + parser.add_argument('--learning_rate', default=0.001, type=float, help='learning rate in training [default: 0.001]') + parser.add_argument('--gpu', type=str, default='0', help='specify gpu device [default: 0]') + + parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]') + + parser.add_argument('--optimizer', type=str, default='Adam', help='optimizer for training [default: Adam]') + parser.add_argument('--log_dir', type=str, default=None, help='experiment root') + parser.add_argument('--decay_rate', type=float, default=1e-4, help='decay rate [default: 1e-4]') + parser.add_argument('--normal', action='store_true', default=False, help='Whether to use normal information [default: False]') + args = parser.parse_args() + return args +# https://whttps://www.cnblogs.com/yibeimingyue/p/13800159.html +# argparse.ArgumentParser函数的解释。 + +def test(model, loader,class_number): + class_number =args.num_class + # num_class = args.num_class + + mean_correct = [] + class_acc = np.zeros((class_number,3)) + for j, data in tqdm(enumerate(loader), total=len(loader)): + points, target = data + target = target[:, 0] + points = points.transpose(2, 1) + points, target = points.cuda(), target.cuda() + classifier = model.eval() + pred, _ = classifier(points) + pred_choice = pred.data.max(1)[1] + for cat in np.unique(target.cpu()): + classacc = pred_choice[target==cat].eq(target[target==cat].long().data).cpu().sum() + class_acc[cat,0]+= classacc.item()/float(points[target==cat].size()[0]) + class_acc[cat,1]+=1 + correct = pred_choice.eq(target.long().data).cpu().sum() + mean_correct.append(correct.item()/float(points.size()[0])) + class_acc[:,2] = class_acc[:,0]/ class_acc[:,1] + class_acc = np.mean(class_acc[:,2]) + instance_acc = np.mean(mean_correct) + return instance_acc, class_acc + + +def main(args): + def log_string(str): + logger.info(str) + print(str) + + '''HYPER PARAMETER''' + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu + + '''CREATE DIR''' + timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')) + experiment_dir = Path('./log/') + experiment_dir.mkdir(exist_ok=True) + experiment_dir = experiment_dir.joinpath('classification') + experiment_dir.mkdir(exist_ok=True) + if args.log_dir is None: + experiment_dir = experiment_dir.joinpath(timestr) + else: + experiment_dir = experiment_dir.joinpath(args.log_dir) + experiment_dir.mkdir(exist_ok=True) + checkpoints_dir = experiment_dir.joinpath('checkpoints/') + checkpoints_dir.mkdir(exist_ok=True) + log_dir = experiment_dir.joinpath('logs/') + log_dir.mkdir(exist_ok=True) + + '''LOG''' + args = parse_args() + logger = logging.getLogger("Model") + logger.setLevel(logging.INFO) + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model)) + file_handler.setLevel(logging.INFO) + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + log_string('PARAMETER ...') + log_string(args) + + '''DATA LOADING''' + log_string('Load dataset ...') + + DATA_PATH = '/home/cc/PNtorch-test/PointNet-torch1.1-two/data/modelnet40_normal_resampled/' + + TRAIN_DATASET = ModelNetDataLoader(root=DATA_PATH, npoint=args.num_point, split='train', + normal_channel=args.normal) + TEST_DATASET = ModelNetDataLoader(root=DATA_PATH, npoint=args.num_point, split='test', + normal_channel=args.normal) + + trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=4) + testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=4) + + '''MODEL LOADING''' + # num_class = 40 + MODEL = importlib.import_module(args.model) + shutil.copy('./models/%s.py' % args.model, str(experiment_dir)) + shutil.copy('./models/pointnet_util.py', str(experiment_dir)) + + classifier = MODEL.get_model(args.num_class,normal_channel=args.normal).cuda() + criterion = MODEL.get_loss().cuda() + + try: + checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth') + start_epoch = checkpoint['epoch'] + classifier.load_state_dict(checkpoint['model_state_dict']) + log_string('Use pretrain model') + except: + log_string('No existing model, starting training from scratch...') + start_epoch = 0 + + + if args.optimizer == 'Adam': + optimizer = torch.optim.Adam( + classifier.parameters(), + lr=args.learning_rate, + betas=(0.9, 0.999), + eps=1e-08, + weight_decay=args.decay_rate + ) + else: + optimizer = torch.optim.SGD(classifier.parameters(), lr=0.01, momentum=0.9) + + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.7) + global_epoch = 0 + global_step = 0 + best_instance_acc = 0.0 + best_class_acc = 0.0 + mean_correct = [] + + '''TRANING''' + logger.info('Start training...') + for epoch in range(start_epoch,args.epoch): + log_string('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch)) + + scheduler.step() + for batch_id, data in tqdm(enumerate(trainDataLoader, 0), total=len(trainDataLoader), smoothing=0.9): + points, target = data + points = points.data.numpy() + points = provider.random_point_dropout(points) + points[:,:, 0:3] = provider.random_scale_point_cloud(points[:,:, 0:3]) + points[:,:, 0:3] = provider.shift_point_cloud(points[:,:, 0:3]) + points = torch.Tensor(points) + target = target[:, 0] + + points = points.transpose(2, 1) + points, target = points.cuda(), target.cuda() + optimizer.zero_grad() + + classifier = classifier.train() + pred, trans_feat = classifier(points) + loss = criterion(pred, target.long(), trans_feat) + pred_choice = pred.data.max(1)[1] + correct = pred_choice.eq(target.long().data).cpu().sum() + mean_correct.append(correct.item() / float(points.size()[0])) + loss.backward() + optimizer.step() + global_step += 1 + + train_instance_acc = np.mean(mean_correct) + log_string('Train Instance Accuracy: %f' % train_instance_acc) + + + with torch.no_grad(): + instance_acc, class_acc = test(classifier.eval(), testDataLoader) + + if (instance_acc >= best_instance_acc): + best_instance_acc = instance_acc + best_epoch = epoch + 1 + + if (class_acc >= best_class_acc): + best_class_acc = class_acc + log_string('Test Instance Accuracy: %f, Class Accuracy: %f'% (instance_acc, class_acc)) + log_string('Best Instance Accuracy: %f, Class Accuracy: %f'% (best_instance_acc, best_class_acc)) + + if (instance_acc >= best_instance_acc): + logger.info('Save model...') + savepath = str(checkpoints_dir) + '/best_model.pth' + log_string('Saving at %s'% savepath) + state = { + 'epoch': best_epoch, + 'instance_acc': instance_acc, + 'class_acc': class_acc, + 'model_state_dict': classifier.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + } + torch.save(state, savepath) + global_epoch += 1 + + logger.info('End of training...') + +if __name__ == '__main__': + args = parse_args() + main(args) -- Gitee From 19745ee48bc8912f38c446d5ff1cdf07a5653f67 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:13:27 +0000 Subject: [PATCH 28/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/train=5Fcls.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../train_cls.py" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" index 2eee725..94bd1f6 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" @@ -116,7 +116,7 @@ def main(args): '''DATA LOADING''' log_string('Load dataset ...') - DATA_PATH = '/home/cc/PNtorch-test/PointNet-torch1.1-two/data/modelnet40_normal_resampled/' + DATA_PATH = os.path.join(BASE_DIR,'data/modelnet40/') TRAIN_DATASET = ModelNetDataLoader(root=DATA_PATH, npoint=args.num_point, split='train', normal_channel=args.normal) -- Gitee From 9724d096ee7261c70331d7fa288e600a35a207e6 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:13:46 +0000 Subject: [PATCH 29/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../test_cls.py" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/test_cls.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/test_cls.py" 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"b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/test_cls.py" @@ -0,0 +1,105 @@ +""" +Author: Benny +Date: Nov 2019 +""" +from data_utils.ModelNetDataLoader import ModelNetDataLoader +import argparse +import numpy as np +import os +import torch +import logging +from tqdm import tqdm +import sys +import importlib + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = BASE_DIR +sys.path.append(os.path.join(ROOT_DIR, 'models')) + + +def parse_args(): + '''PARAMETERS''' + parser = argparse.ArgumentParser('PointNet') + parser.add_argument('--batch_size', type=int, default=8, help='batch size in training') + parser.add_argument('--gpu', type=str, default='0', help='specify gpu device') + parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]') + parser.add_argument('--log_dir', type=str, default='pointnet2_ssg_normal', help='Experiment root') + parser.add_argument('--normal', action='store_true', default=True, help='Whether to use normal information [default: False]') + parser.add_argument('--num_votes', type=int, default=3, help='Aggregate classification scores with voting [default: 3]') + return parser.parse_args() + +def test(model, loader, num_class=40, vote_num=1): + mean_correct = [] + class_acc = np.zeros((num_class,3)) + for j, data in tqdm(enumerate(loader), total=len(loader)): + points, target = data + target = target[:, 0] + points = points.transpose(2, 1) + points, target = points.cuda(), target.cuda() + classifier = model.eval() + vote_pool = torch.zeros(target.size()[0],num_class).cuda() + for _ in range(vote_num): + pred, _ = classifier(points) + vote_pool += pred + pred = vote_pool/vote_num + pred_choice = pred.data.max(1)[1] + for cat in np.unique(target.cpu()): + classacc = pred_choice[target==cat].eq(target[target==cat].long().data).cpu().sum() + class_acc[cat,0]+= classacc.item()/float(points[target==cat].size()[0]) + class_acc[cat,1]+=1 + correct = pred_choice.eq(target.long().data).cpu().sum() + mean_correct.append(correct.item()/float(points.size()[0])) + class_acc[:,2] = class_acc[:,0]/ class_acc[:,1] + class_acc = np.mean(class_acc[:,2]) + instance_acc = np.mean(mean_correct) + return instance_acc, class_acc + + +def main(args): + def log_string(str): + logger.info(str) + print(str) + + '''HYPER PARAMETER''' + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu + + '''CREATE DIR''' + experiment_dir = 'log/classification/' + args.log_dir + + '''LOG''' + args = parse_args() + logger = logging.getLogger("Model") + logger.setLevel(logging.INFO) + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + file_handler = logging.FileHandler('%s/eval.txt' % experiment_dir) + file_handler.setLevel(logging.INFO) + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + log_string('PARAMETER ...') + log_string(args) + + '''DATA LOADING''' + log_string('Load dataset ...') + DATA_PATH = 'data/modelnet40/' + TEST_DATASET = ModelNetDataLoader(root=DATA_PATH, npoint=args.num_point, split='test', normal_channel=args.normal) + testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=4) + + '''MODEL LOADING''' + num_class = 40 + model_name = os.listdir(experiment_dir+'/logs')[0].split('.')[0] + MODEL = importlib.import_module(model_name) + + classifier = MODEL.get_model(num_class,normal_channel=args.normal).cuda() + + checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth') + classifier.load_state_dict(checkpoint['model_state_dict']) + + with torch.no_grad(): + instance_acc, class_acc = test(classifier.eval(), testDataLoader, vote_num=args.num_votes) + log_string('Test Instance Accuracy: %f, Class Accuracy: %f' % (instance_acc, class_acc)) + + + +if __name__ == '__main__': + args = parse_args() + main(args) -- Gitee From d8283a82a4448f036ec419e2f1fb5a3e6ba21517 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:14:58 +0000 Subject: [PATCH 31/69] =?UTF-8?q?=E9=87=8D=E5=91=BD=E5=90=8D=20code/2022?= =?UTF-8?q?=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D?= =?UTF-8?q?=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84?= =?UTF-8?q?=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB?= =?UTF-8?q?/data/modelnet40=5Fply=5Fhdf5=5F2048=20=E4=B8=BA=20code/2022=5F?= =?UTF-8?q?autumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D?= =?UTF-8?q?=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84?= =?UTF-8?q?=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB?= =?UTF-8?q?/data/modelnet40?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 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"b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/.keep" similarity index 100% rename from "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40_ply_hdf5_2048/.keep" rename to "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/.keep" -- Gitee From da6132d1fa8d6638a5c2a9e08f3a5bf44d00fbd9 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:15:11 +0000 Subject: [PATCH 32/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data/modelnet40.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data/modelnet40/download" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/download" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/download" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/download" new file mode 100644 index 0000000..e69de29 -- Gitee From 195a675877f13b295b02fd893eaa62abc56038a0 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Thu, 27 Oct 2022 02:15:19 +0000 Subject: [PATCH 33/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data/modelnet40/d?= =?UTF-8?q?ownload.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data/modelnet40/download" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/download" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/download" index e69de29..d850c61 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/download" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/download" @@ -0,0 +1,2 @@ +链接：https://pan.baidu.com/s/1J1svTCLW_Iy7blMan0YiPQ +提取码：20yl \ No newline at end of file -- Gitee From e9050f21fe21731e9519ae688d4192eb54d99191 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:14:03 +0000 Subject: [PATCH 34/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/models/pointnet2?= =?UTF-8?q?=5Fcls=5Fssg.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../models/pointnet2_cls_ssg.py" | 62 +++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_ssg.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_ssg.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_ssg.py" new file mode 100644 index 0000000..1e845ff --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_ssg.py" @@ -0,0 +1,62 @@ +""" + PointNet++ Model for point clouds classification +""" + +import os +import sys +BASE_DIR = os.path.dirname(__file__) +sys.path.append(BASE_DIR) +sys.path.append(os.path.join(BASE_DIR, '../utils')) +import tensorflow as tf +import numpy as np +import tf_util +from pointnet_util import pointnet_sa_module + +def placeholder_inputs(batch_size, num_point): + pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) + labels_pl = tf.placeholder(tf.int32, shape=(batch_size)) + return pointclouds_pl, labels_pl + +def get_model(point_cloud, is_training, bn_decay=None): + """ Classification PointNet, input is BxNx3, output Bx40 """ + batch_size = point_cloud.get_shape()[0].value + num_point = point_cloud.get_shape()[1].value + end_points = {} + l0_xyz = point_cloud + l0_points = None + end_points['l0_xyz'] = l0_xyz + + # Set abstraction layers + # Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4). + # So we only use NCHW for layer 1 until this issue can be resolved. + l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_nchw=True) + l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2') + l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3') + + # Fully connected layers + net = tf.reshape(l3_points, [batch_size, -1]) + net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) + net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1') + net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay) + net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2') + net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3') + + return net, end_points + + +def get_loss(pred, label, end_points): + """ pred: B*NUM_CLASSES, + label: B, """ + loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) + classify_loss = tf.reduce_mean(loss) + tf.summary.scalar('classify loss', classify_loss) + tf.add_to_collection('losses', classify_loss) + return classify_loss + + +if __name__=='__main__': + with tf.Graph().as_default(): + inputs = tf.zeros((32,1024,3)) + output, _ = get_model(inputs, tf.constant(True)) + print(output) + -- Gitee From 574a26a7dd6b84b0268b54a1cb85fe94b096328f Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:14:31 +0000 Subject: [PATCH 35/69] =?UTF-8?q?=E9=87=8D=E5=91=BD=E5=90=8D=20code/2022?= 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"b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py for torch" similarity index 100% rename from "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py" rename to "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py for torch" -- Gitee From ce79e4b03069f8c7c945627ea0757c7046b6c487 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:14:43 +0000 Subject: [PATCH 36/69] =?UTF-8?q?=E9=87=8D=E5=91=BD=E5=90=8D=20code/2022?= =?UTF-8?q?=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D?= =?UTF-8?q?=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84?= =?UTF-8?q?=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB?= =?UTF-8?q?/models/pointnet2=5Fcls=5Fssg.py=20=E4=B8=BA=20code/2022=5Fautu?= =?UTF-8?q?mn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9?= =?UTF-8?q?=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87?= =?UTF-8?q?=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/models/p?= =?UTF-8?q?ointnet2=5Fcls=5Fssg.py=20for=20tf?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../models/pointnet2_cls_ssg.py for tf" | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename 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"b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_ssg.py for tf" similarity index 100% rename from "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_ssg.py" rename to "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet2_cls_ssg.py for tf" -- Gitee From 9b5d415559c30952ca3347014d14b5d5cb2ddfe4 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:26:36 +0000 Subject: [PATCH 37/69] pointnet-tf Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../evaluate.py" | 166 ++++++++ .../modelnet_dataset.py" | 144 +++++++ .../modelnet_h5_dataset.py" | 126 ++++++ .../tf_interpolate_compile.sh" | 7 + ...347\232\204gpu\350\256\276\345\244\207.py" | 12 + .../train.py" | 307 +++++++++++++++ .../train_multi_gpu.py" | 364 ++++++++++++++++++ ...57\345\220\246\345\217\257\347\224\250.py" | 3 + 8 files changed, 1129 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" create mode 100644 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"code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/\346\265\213\350\257\225cudnn\346\230\257\345\220\246\345\217\257\347\224\250.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" new file mode 100644 index 0000000..224d65b --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" @@ -0,0 +1,166 @@ +''' + Evaluate classification performance with optional voting. + Will use H5 dataset in default. If using normal, will shift to the normal dataset. +''' +import tensorflow as tf +import numpy as np +import argparse +import socket +import importlib +import time +import os +import scipy.misc +import sys +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = BASE_DIR +sys.path.append(BASE_DIR) +sys.path.append(os.path.join(ROOT_DIR, 'models')) +sys.path.append(os.path.join(ROOT_DIR, 'utils')) +import provider +import modelnet_dataset +import modelnet_h5_dataset + +parser = argparse.ArgumentParser() +parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') +parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name. [default: pointnet2_cls_ssg]') +parser.add_argument('--batch_size', type=int, default=4, help='Batch Size during training [default: 16]') + + +parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]') +parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]') +parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]') +parser.add_argument('--normal', action='store_true', help='Whether to use normal information') +parser.add_argument('--num_votes', type=int, default=1, help='Aggregate classification scores from multiple rotations [default: 1]') +FLAGS = parser.parse_args() + + +BATCH_SIZE = FLAGS.batch_size +NUM_POINT = FLAGS.num_point +MODEL_PATH = FLAGS.model_path +GPU_INDEX = FLAGS.gpu +MODEL = importlib.import_module(FLAGS.model) # import network module +DUMP_DIR = FLAGS.dump_dir +if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR) +LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w') +LOG_FOUT.write(str(FLAGS)+'\n') + +NUM_CLASSES = 40 +SHAPE_NAMES = [line.rstrip() for line in \ + open(os.path.join(ROOT_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))] + +HOSTNAME = socket.gethostname() + +# Shapenet official train/test split +if FLAGS.normal: + assert(NUM_POINT<=10000) + DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled') + TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) + TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) +else: + assert(NUM_POINT<=2048) + TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True) + TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False) + +def log_string(out_str): + LOG_FOUT.write(out_str+'\n') + LOG_FOUT.flush() + print(out_str) + +def evaluate(num_votes): + is_training = False + + with tf.device('/gpu:'+str(GPU_INDEX)): + pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) + is_training_pl = tf.placeholder(tf.bool, shape=()) + + # simple model + pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl) + MODEL.get_loss(pred, labels_pl, end_points) + losses = tf.get_collection('losses') + total_loss = tf.add_n(losses, name='total_loss') + + # Add ops to save and restore all the variables. + saver = tf.train.Saver() + + # Create a session + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + config.allow_soft_placement = True + config.log_device_placement = False + sess = tf.Session(config=config) + + # Restore variables from disk. + saver.restore(sess, MODEL_PATH) + log_string("Model restored.") + + ops = {'pointclouds_pl': pointclouds_pl, + 'labels_pl': labels_pl, + 'is_training_pl': is_training_pl, + 'pred': pred, + 'loss': total_loss} + + eval_one_epoch(sess, ops, num_votes) + +def eval_one_epoch(sess, ops, num_votes=1, topk=1): + is_training = False + + # Make sure batch data is of same size + cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel())) + cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) + + total_correct = 0 + total_seen = 0 + loss_sum = 0 + batch_idx = 0 + shape_ious = [] + total_seen_class = [0 for _ in range(NUM_CLASSES)] + total_correct_class = [0 for _ in range(NUM_CLASSES)] + + while TEST_DATASET.has_next_batch(): + batch_data, batch_label = TEST_DATASET.next_batch(augment=False) + bsize = batch_data.shape[0] + print('Batch: %03d, batch size: %d'%(batch_idx, bsize)) + # for the last batch in the epoch, the bsize:end are from last batch + cur_batch_data[0:bsize,...] = batch_data + cur_batch_label[0:bsize] = batch_label + + batch_pred_sum = np.zeros((BATCH_SIZE, NUM_CLASSES)) # score for classes + for vote_idx in range(num_votes): + # Shuffle point order to achieve different farthest samplings + shuffled_indices = np.arange(NUM_POINT) + np.random.shuffle(shuffled_indices) + if FLAGS.normal: + rotated_data = provider.rotate_point_cloud_by_angle_with_normal(cur_batch_data[:, shuffled_indices, :], + vote_idx/float(num_votes) * np.pi * 2) + else: + rotated_data = provider.rotate_point_cloud_by_angle(cur_batch_data[:, shuffled_indices, :], + vote_idx/float(num_votes) * np.pi * 2) + feed_dict = {ops['pointclouds_pl']: rotated_data, + ops['labels_pl']: cur_batch_label, + ops['is_training_pl']: is_training} + loss_val, pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict) + batch_pred_sum += pred_val + pred_val = np.argmax(batch_pred_sum, 1) + correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) + total_correct += correct + total_seen += bsize + loss_sum += loss_val + batch_idx += 1 + for i in range(bsize): + l = batch_label[i] + total_seen_class[l] += 1 + total_correct_class[l] += (pred_val[i] == l) + + log_string('eval mean loss: %f' % (loss_sum / float(batch_idx))) + log_string('eval accuracy: %f'% (total_correct / float(total_seen))) + log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) + + class_accuracies = np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float) + for i, name in enumerate(SHAPE_NAMES): + log_string('%10s:\t%0.3f' % (name, class_accuracies[i])) + + +if __name__=='__main__': + with tf.Graph().as_default(): + evaluate(num_votes=FLAGS.num_votes) + LOG_FOUT.close() diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/modelnet_dataset.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/modelnet_dataset.py" new file mode 100644 index 0000000..78f326e --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/modelnet_dataset.py" @@ -0,0 +1,144 @@ +''' + ModelNet dataset. Support ModelNet40, ModelNet10, XYZ and normal channels. Up to 10000 points. +''' + +import os +import os.path +import json +import numpy as np +import sys +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = BASE_DIR +sys.path.append(os.path.join(ROOT_DIR, 'utils')) +import provider + +def pc_normalize(pc): + l = pc.shape[0] + centroid = np.mean(pc, axis=0) + pc = pc - centroid + m = np.max(np.sqrt(np.sum(pc**2, axis=1))) + pc = pc / m + return pc + +class ModelNetDataset(): + def __init__(self, root, batch_size = 32, npoints = 1024, split='train', normalize=True, normal_channel=False, modelnet10=False, cache_size=15000, shuffle=None): + self.root = root + self.batch_size = batch_size + self.npoints = npoints + self.normalize = normalize + if modelnet10: + self.catfile = os.path.join(self.root, 'modelnet10_shape_names.txt') + else: + self.catfile = os.path.join(self.root, 'shape_names.txt') + self.cat = [line.rstrip() for line in open(self.catfile)] + self.classes = dict(zip(self.cat, range(len(self.cat)))) + self.normal_channel = normal_channel + + shape_ids = {} + if modelnet10: + shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet10_train.txt'))] + shape_ids['test']= [line.rstrip() for line in open(os.path.join(self.root, 'modelnet10_test.txt'))] + else: + shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_train.txt'))] + shape_ids['test']= [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_test.txt'))] + assert(split=='train' or split=='test') + shape_names = ['_'.join(x.split('_')[0:-1]) for x in shape_ids[split]] + # list of (shape_name, shape_txt_file_path) tuple + self.datapath = [(shape_names[i], os.path.join(self.root, shape_names[i], shape_ids[split][i])+'.txt') for i in range(len(shape_ids[split]))] + + self.cache_size = cache_size # how many data points to cache in memory + self.cache = {} # from index to (point_set, cls) tuple + + if shuffle is None: + if split == 'train': self.shuffle = True + else: self.shuffle = False + else: + self.shuffle = shuffle + + self.reset() + + def _augment_batch_data(self, batch_data): + if self.normal_channel: + rotated_data = provider.rotate_point_cloud_with_normal(batch_data) + rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data) + else: + rotated_data = provider.rotate_point_cloud(batch_data) + rotated_data = provider.rotate_perturbation_point_cloud(rotated_data) + + jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3]) + jittered_data = provider.shift_point_cloud(jittered_data) + jittered_data = provider.jitter_point_cloud(jittered_data) + rotated_data[:,:,0:3] = jittered_data + return provider.shuffle_points(rotated_data) + + + def _get_item(self, index): + if index in self.cache: + point_set, cls = self.cache[index] + else: + fn = self.datapath[index] + cls = self.classes[self.datapath[index][0]] + cls = np.array([cls]).astype(np.int32) + point_set = np.loadtxt(fn[1],delimiter=',').astype(np.float32) + # Take the first npoints + point_set = point_set[0:self.npoints,:] + if self.normalize: + point_set[:,0:3] = pc_normalize(point_set[:,0:3]) + if not self.normal_channel: + point_set = point_set[:,0:3] + if len(self.cache) < self.cache_size: + self.cache[index] = (point_set, cls) + return point_set, cls + + def __getitem__(self, index): + return self._get_item(index) + + def __len__(self): + return len(self.datapath) + + def num_channel(self): + if self.normal_channel: + return 6 + else: + return 3 + + def reset(self): + self.idxs = np.arange(0, len(self.datapath)) + if self.shuffle: + np.random.shuffle(self.idxs) + self.num_batches = (len(self.datapath)+self.batch_size-1) // self.batch_size + self.batch_idx = 0 + + def has_next_batch(self): + return self.batch_idx < self.num_batches + + def next_batch(self, augment=False): + ''' returned dimension may be smaller than self.batch_size ''' + start_idx = self.batch_idx * self.batch_size + end_idx = min((self.batch_idx+1) * self.batch_size, len(self.datapath)) + bsize = end_idx - start_idx + batch_data = np.zeros((bsize, self.npoints, self.num_channel())) + batch_label = np.zeros((bsize), dtype=np.int32) + for i in range(bsize): + ps,cls = self._get_item(self.idxs[i+start_idx]) + batch_data[i] = ps + batch_label[i] = cls + self.batch_idx += 1 + if augment: batch_data = self._augment_batch_data(batch_data) + return batch_data, batch_label + +if __name__ == '__main__': + d = ModelNetDataset(root = '../data/modelnet40_normal_resampled', split='test') + print(d.shuffle) + print(len(d)) + import time + tic = time.time() + for i in range(10): + ps, cls = d[i] + print(time.time() - tic) + print(ps.shape, type(ps), cls) + + print(d.has_next_batch()) + ps_batch, cls_batch = d.next_batch(True) + print(ps_batch.shape) + print(cls_batch.shape) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/modelnet_h5_dataset.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/modelnet_h5_dataset.py" new file mode 100644 index 0000000..ecd6e16 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/modelnet_h5_dataset.py" @@ -0,0 +1,126 @@ +''' + ModelNet dataset. Support ModelNet40, XYZ channels. Up to 2048 points. + Faster IO than ModelNetDataset in the first epoch. +''' + +import os +import sys +import numpy as np +import h5py +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) +ROOT_DIR = BASE_DIR +sys.path.append(os.path.join(ROOT_DIR, 'utils')) +import provider + + +# Download dataset for point cloud classification +DATA_DIR = os.path.join(ROOT_DIR, 'data') +if not os.path.exists(DATA_DIR): + os.mkdir(DATA_DIR) +if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')): + www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip' + zipfile = os.path.basename(www) + os.system('wget %s; unzip %s' % (www, zipfile)) + os.system('mv %s %s' % (zipfile[:-4], DATA_DIR)) + os.system('rm %s' % (zipfile)) + + +def shuffle_data(data, labels): + """ Shuffle data and labels. + Input: + data: B,N,... numpy array + label: B,... numpy array + Return: + shuffled data, label and shuffle indices + """ + idx = np.arange(len(labels)) + np.random.shuffle(idx) + return data[idx, ...], labels[idx], idx + +def getDataFiles(list_filename): + return [line.rstrip() for line in open(list_filename)] + +def load_h5(h5_filename): + f = h5py.File(h5_filename) + data = f['data'][:] + label = f['label'][:] + return (data, label) + +def loadDataFile(filename): + return load_h5(filename) + + +class ModelNetH5Dataset(object): + def __init__(self, list_filename, batch_size = 32, npoints = 1024, shuffle=True): + self.list_filename = list_filename + self.batch_size = batch_size + self.npoints = npoints + self.shuffle = shuffle + self.h5_files = getDataFiles(self.list_filename) + self.reset() + + def reset(self): + ''' reset order of h5 files ''' + self.file_idxs = np.arange(0, len(self.h5_files)) + if self.shuffle: np.random.shuffle(self.file_idxs) + self.current_data = None + self.current_label = None + self.current_file_idx = 0 + self.batch_idx = 0 + + def _augment_batch_data(self, batch_data): + rotated_data = provider.rotate_point_cloud(batch_data) + rotated_data = provider.rotate_perturbation_point_cloud(rotated_data) + jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3]) + jittered_data = provider.shift_point_cloud(jittered_data) + jittered_data = provider.jitter_point_cloud(jittered_data) + rotated_data[:,:,0:3] = jittered_data + return provider.shuffle_points(rotated_data) + + + def _get_data_filename(self): + return self.h5_files[self.file_idxs[self.current_file_idx]] + + def _load_data_file(self, filename): + self.current_data,self.current_label = load_h5(filename) + self.current_label = np.squeeze(self.current_label) + self.batch_idx = 0 + if self.shuffle: + self.current_data, self.current_label, _ = shuffle_data(self.current_data,self.current_label) + + def _has_next_batch_in_file(self): + return self.batch_idx*self.batch_size < self.current_data.shape[0] + + def num_channel(self): + return 3 + + def has_next_batch(self): + # TODO: add backend thread to load data + if (self.current_data is None) or (not self._has_next_batch_in_file()): + if self.current_file_idx >= len(self.h5_files): + return False + self._load_data_file(self._get_data_filename()) + self.batch_idx = 0 + self.current_file_idx += 1 + return self._has_next_batch_in_file() + + def next_batch(self, augment=False): + ''' returned dimension may be smaller than self.batch_size ''' + start_idx = self.batch_idx * self.batch_size + end_idx = min((self.batch_idx+1) * self.batch_size, self.current_data.shape[0]) + bsize = end_idx - start_idx + batch_label = np.zeros((bsize), dtype=np.int32) + data_batch = self.current_data[start_idx:end_idx, 0:self.npoints, :].copy() + label_batch = self.current_label[start_idx:end_idx].copy() + self.batch_idx += 1 + if augment: data_batch = self._augment_batch_data(data_batch) + return data_batch, label_batch + +if __name__=='__main__': + d = ModelNetH5Dataset('data/modelnet40_ply_hdf5_2048/train_files.txt') + print(d.shuffle) + print(d.has_next_batch()) + ps_batch, cls_batch = d.next_batch(True) + print(ps_batch.shape) + print(cls_batch.shape) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_interpolate_compile.sh" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_interpolate_compile.sh" new file mode 100644 index 0000000..8af3cf7 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_interpolate_compile.sh" @@ -0,0 +1,7 @@ +# TF1.2 +#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +# TF1.4 +#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include -I /usr/local/cuda-9.0/include -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-9.0/lib64/ -L/home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow -l:libtensorflow_framework.so -O2 -D_GLIBCXX_USE_CXX11_ABI=0 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf\346\243\200\346\237\245\345\217\257\347\224\250\347\232\204gpu\350\256\276\345\244\207.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf\346\243\200\346\237\245\345\217\257\347\224\250\347\232\204gpu\350\256\276\345\244\207.py" new file mode 100644 index 0000000..8bd0d15 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf\346\243\200\346\237\245\345\217\257\347\224\250\347\232\204gpu\350\256\276\345\244\207.py" @@ -0,0 +1,12 @@ +#--coding=utf8 +from tensorflow.python.client import device_lib + +print(device_lib.list_local_devices()) + +from tensorflow.python.client import device_lib + +# 列出全部的本地机器设备 +local_device_protos = device_lib.list_local_devices() +# 打印 +print(local_device_protos) + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train.py" new file mode 100644 index 0000000..0c03076 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train.py" @@ -0,0 +1,307 @@ +''' + Single-GPU training. + Will use H5 dataset in default. If using normal, will shift to the normal dataset. +''' +import argparse +import math +from datetime import datetime +import h5py +import numpy as np +import tensorflow as tf +import socket +import importlib +import os +import sys +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = BASE_DIR +sys.path.append(BASE_DIR) +sys.path.append(os.path.join(ROOT_DIR, 'models')) +sys.path.append(os.path.join(ROOT_DIR, 'utils')) +import provider +import tf_util +import modelnet_dataset +import modelnet_h5_dataset + +# os.environ["CUDA_VISIBLE_DEVICES"] = "0" +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +# from tensorflow.compat.v1 import ConfigProto +# from tensorflow.compat.v1 import InteractiveSession +# config = tf.compat.v1.ConfigProto() +# config.gpu_options.allow_growth = True +# session =tf.compat.v1.InteractiveSession(config=config) + + + +parser = argparse.ArgumentParser() +parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') +parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name [default: pointnet2_cls_ssg]') + +parser.add_argument('--log_dir', default='log', help='Log dir [default: log]') +parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]') +parser.add_argument('--max_epoch', type=int, default=250, help='Epoch to run [default: 251]') + + +parser.add_argument('--batch_size', type=int, default=8, help='Batch Size during training [default: 16]') + +parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]') +parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]') +parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]') +parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]') + + +parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]') +parser.add_argument('--normal', action='store_true', help='Whether to use normal information') +FLAGS = parser.parse_args() + +EPOCH_CNT = 0 + +BATCH_SIZE = FLAGS.batch_size +NUM_POINT = FLAGS.num_point +MAX_EPOCH = FLAGS.max_epoch +BASE_LEARNING_RATE = FLAGS.learning_rate +GPU_INDEX = FLAGS.gpu +MOMENTUM = FLAGS.momentum +OPTIMIZER = FLAGS.optimizer +DECAY_STEP = FLAGS.decay_step +DECAY_RATE = FLAGS.decay_rate + +MODEL = importlib.import_module(FLAGS.model) # import network module + + + +MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py') + +LOG_DIR = FLAGS.log_dir +if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) +os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def +os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure +LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') +LOG_FOUT.write(str(FLAGS)+'\n') + +BN_INIT_DECAY = 0.5 +BN_DECAY_DECAY_RATE = 0.5 +BN_DECAY_DECAY_STEP = float(DECAY_STEP) +BN_DECAY_CLIP = 0.99 + +HOSTNAME = socket.gethostname() + +NUM_CLASSES = 40 + +# Shapenet official train/test split + +TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), + batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True) +TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), + batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False) + +def log_string(out_str): + LOG_FOUT.write(out_str+'\n') + LOG_FOUT.flush() + print(out_str) + +def get_learning_rate(batch): + learning_rate = tf.train.exponential_decay( + BASE_LEARNING_RATE, # Base learning rate. + batch * BATCH_SIZE, # Current index into the dataset. + DECAY_STEP, # Decay step. + DECAY_RATE, # Decay rate. + staircase=True) + learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE! + return learning_rate + +def get_bn_decay(batch): + bn_momentum = tf.train.exponential_decay( + BN_INIT_DECAY, + batch*BATCH_SIZE, + BN_DECAY_DECAY_STEP, + BN_DECAY_DECAY_RATE, + staircase=True) + bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) + return bn_decay + +def train(): + with tf.Graph().as_default(): + with tf.device('/gpu:'+str(GPU_INDEX)): + pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) + is_training_pl = tf.placeholder(tf.bool, shape=()) + + # Note the global_step=batch parameter to minimize. + # That tells the optimizer to helpfully increment the 'batch' parameter + # for you every time it trains. + batch = tf.get_variable('batch', [], + initializer=tf.constant_initializer(0), trainable=False) + bn_decay = get_bn_decay(batch) + tf.summary.scalar('bn_decay', bn_decay) + + # Get model and loss + pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay) + MODEL.get_loss(pred, labels_pl, end_points) + losses = tf.get_collection('losses') + total_loss = tf.add_n(losses, name='total_loss') + tf.summary.scalar('total_loss', total_loss) + for l in losses + [total_loss]: + tf.summary.scalar(l.op.name, l) + + correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl)) + # correct = tf.equal(tf.argmax(pred, 1), tf.cast(labels_pl)) + accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE) + tf.summary.scalar('accuracy', accuracy) + + print("--- Get training operator") + # Get training operator + learning_rate = get_learning_rate(batch) + tf.summary.scalar('learning_rate', learning_rate) + if OPTIMIZER == 'momentum': + optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM) + elif OPTIMIZER == 'adam': + optimizer = tf.train.AdamOptimizer(learning_rate) + train_op = optimizer.minimize(total_loss, global_step=batch) + + # Add ops to save and restore all the variables. + saver = tf.train.Saver() + + # Create a session + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + config.allow_soft_placement = True + config.log_device_placement = False + # config.graph_options.optimizer_options.global_jit_level =tf.OptimizerOptions.ON_1 + sess = tf.Session(config=config) + + + + + # Add summary writers + merged = tf.summary.merge_all() + train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph) + test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph) + + # Init variables + init = tf.global_variables_initializer() + sess.run(init) + + ops = {'pointclouds_pl': pointclouds_pl, + 'labels_pl': labels_pl, + 'is_training_pl': is_training_pl, + 'pred': pred, + 'loss': total_loss, + 'train_op': train_op, + 'merged': merged, + 'step': batch, + 'end_points': end_points} + + best_acc = -1 + for epoch in range(MAX_EPOCH): + log_string('**** EPOCH %03d ****' % (epoch)) + sys.stdout.flush() + + train_one_epoch(sess, ops, train_writer) + eval_one_epoch(sess, ops, test_writer) + + # Save the variables to disk. + if epoch % 10 == 0: + save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt")) + log_string("Model saved in file: %s" % save_path) + + +def train_one_epoch(sess, ops, train_writer): + """ ops: dict mapping from string to tf ops """ + is_training = True + + log_string(str(datetime.now())) + + # Make sure batch data is of same size + cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TRAIN_DATASET.num_channel())) + cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) + + total_correct = 0 + total_seen = 0 + loss_sum = 0 + batch_idx = 0 + while TRAIN_DATASET.has_next_batch(): + batch_data, batch_label = TRAIN_DATASET.next_batch(augment=True) + #batch_data = provider.random_point_dropout(batch_data) + bsize = batch_data.shape[0] + cur_batch_data[0:bsize,...] = batch_data + cur_batch_label[0:bsize] = batch_label + + feed_dict = {ops['pointclouds_pl']: cur_batch_data, + ops['labels_pl']: cur_batch_label, + ops['is_training_pl']: is_training,} + summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], + ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict) + train_writer.add_summary(summary, step) + pred_val = np.argmax(pred_val, 1) + correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) + total_correct += correct + total_seen += bsize + loss_sum += loss_val + if (batch_idx+1)%50 == 0: + log_string(' ---- batch: %03d ----' % (batch_idx+1)) + log_string('mean loss: %f' % (loss_sum / 50)) + log_string('accuracy: %f' % (total_correct / float(total_seen))) + total_correct = 0 + total_seen = 0 + loss_sum = 0 + batch_idx += 1 + + TRAIN_DATASET.reset() + +def eval_one_epoch(sess, ops, test_writer): + """ ops: dict mapping from string to tf ops """ + global EPOCH_CNT + is_training = False + + # Make sure batch data is of same size + cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel())) + cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) + + total_correct = 0 + total_seen = 0 + loss_sum = 0 + batch_idx = 0 + shape_ious = [] + total_seen_class = [0 for _ in range(NUM_CLASSES)] + total_correct_class = [0 for _ in range(NUM_CLASSES)] + + log_string(str(datetime.now())) + log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT)) + + while TEST_DATASET.has_next_batch(): + batch_data, batch_label = TEST_DATASET.next_batch(augment=False) + bsize = batch_data.shape[0] + # for the last batch in the epoch, the bsize:end are from last batch + cur_batch_data[0:bsize,...] = batch_data + cur_batch_label[0:bsize] = batch_label + + feed_dict = {ops['pointclouds_pl']: cur_batch_data, + ops['labels_pl']: cur_batch_label, + ops['is_training_pl']: is_training} + summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'], + ops['loss'], ops['pred']], feed_dict=feed_dict) + test_writer.add_summary(summary, step) + pred_val = np.argmax(pred_val, 1) + correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) + total_correct += correct + total_seen += bsize + loss_sum += loss_val + batch_idx += 1 + for i in range(0, bsize): + l = batch_label[i] + total_seen_class[l] += 1 + total_correct_class[l] += (pred_val[i] == l) + + log_string('eval mean loss: %f' % (loss_sum / float(batch_idx))) + log_string('eval accuracy: %f'% (total_correct / float(total_seen))) + log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) + EPOCH_CNT += 1 + + TEST_DATASET.reset() + return total_correct/float(total_seen) + + +if __name__ == "__main__": + log_string('pid: %s'%(str(os.getpid()))) + train() + LOG_FOUT.close() diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_multi_gpu.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_multi_gpu.py" new file mode 100644 index 0000000..51d0f82 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_multi_gpu.py" @@ -0,0 +1,364 @@ +''' + Multi-GPU training. + Near linear scale acceleration for multi-gpus on a single machine. + Will use H5 dataset in default. If using normal, will shift to the normal dataset. +''' + +import argparse +import math +from datetime import datetime +import h5py +import numpy as np +import tensorflow as tf +import socket +import importlib +import os +import sys +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = BASE_DIR +sys.path.append(BASE_DIR) +sys.path.append(os.path.join(ROOT_DIR, 'models')) +sys.path.append(os.path.join(ROOT_DIR, 'utils')) +import provider +import tf_util +import modelnet_dataset +import modelnet_h5_dataset + +parser = argparse.ArgumentParser() +parser.add_argument('--num_gpus', type=int, default=1, help='How many gpus to use [default: 1]') +parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name [default: pointnet2_cls_ssg]') +parser.add_argument('--log_dir', default='log', help='Log dir [default: log]') +parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]') +parser.add_argument('--max_epoch', type=int, default=5, help='Epoch to run [default: 251]') + + + +parser.add_argument('--batch_size', type=int, default=8, help='Batch Size during training [default: 32]') +parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]') +parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]') +parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]') +parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]') +parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]') +parser.add_argument('--normal', action='store_true', help='Whether to use normal information') +FLAGS = parser.parse_args() + +EPOCH_CNT = 0 + +NUM_GPUS = FLAGS.num_gpus +BATCH_SIZE = FLAGS.batch_size +assert(BATCH_SIZE % NUM_GPUS == 0) +DEVICE_BATCH_SIZE = BATCH_SIZE / NUM_GPUS + +NUM_POINT = FLAGS.num_point +MAX_EPOCH = FLAGS.max_epoch +BASE_LEARNING_RATE = FLAGS.learning_rate +MOMENTUM = FLAGS.momentum +OPTIMIZER = FLAGS.optimizer +DECAY_STEP = FLAGS.decay_step +DECAY_RATE = FLAGS.decay_rate + +MODEL = importlib.import_module(FLAGS.model) # import network module +MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py') +LOG_DIR = FLAGS.log_dir +if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) +os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def +os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure +LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') +LOG_FOUT.write(str(FLAGS)+'\n') + +BN_INIT_DECAY = 0.5 +BN_DECAY_DECAY_RATE = 0.5 +BN_DECAY_DECAY_STEP = float(DECAY_STEP) +BN_DECAY_CLIP = 0.99 + +HOSTNAME = socket.gethostname() + +NUM_CLASSES = 40 + +# # Shapenet official train/test split +# if FLAGS.normal: +# assert(NUM_POINT<=10000) +# DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled') +# TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) +# TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) +# else: +# assert(NUM_POINT<=2048) +TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), + batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True) +TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), + batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False) + +def log_string(out_str): + LOG_FOUT.write(out_str+'\n') + LOG_FOUT.flush() + print(out_str) + +def average_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + From tensorflow tutorial: cifar10/cifar10_multi_gpu_train.py + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + #for g, _ in grad_and_vars: + for g, v in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + + +def get_learning_rate(batch): + learning_rate = tf.train.exponential_decay( + BASE_LEARNING_RATE, # Base learning rate. + batch * BATCH_SIZE, # Current index into the dataset. + DECAY_STEP, # Decay step. + DECAY_RATE, # Decay rate. + staircase=True) + learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE! + return learning_rate + +def get_bn_decay(batch): + bn_momentum = tf.train.exponential_decay( + BN_INIT_DECAY, + batch*BATCH_SIZE, + BN_DECAY_DECAY_STEP, + BN_DECAY_DECAY_RATE, + staircase=True) + bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) + return bn_decay + +def train(): + with tf.Graph().as_default(): + with tf.device('/cpu:0'): + pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) + is_training_pl = tf.placeholder(tf.bool, shape=()) + + # Note the global_step=batch parameter to minimize. + # That tells the optimizer to helpfully increment the 'batch' parameter + # for you every time it trains. + batch = tf.get_variable('batch', [], + initializer=tf.constant_initializer(0), trainable=False) + bn_decay = get_bn_decay(batch) + tf.summary.scalar('bn_decay', bn_decay) + + # Set learning rate and optimizer + learning_rate = get_learning_rate(batch) + tf.summary.scalar('learning_rate', learning_rate) + if OPTIMIZER == 'momentum': + optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM) + elif OPTIMIZER == 'adam': + optimizer = tf.train.AdamOptimizer(learning_rate) + + # ------------------------------------------- + # Get model and loss on multiple GPU devices + # ------------------------------------------- + # Allocating variables on CPU first will greatly accelerate multi-gpu training. + # Ref: https://github.com/kuza55/keras-extras/issues/21 + MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay) + + tower_grads = [] + pred_gpu = [] + total_loss_gpu = [] + for i in range(NUM_GPUS): + with tf.variable_scope(tf.get_variable_scope(), reuse=True): + with tf.device('/gpu:%d'%(i)), tf.name_scope('gpu_%d'%(i)) as scope: + # Evenly split input data to each GPU + pc_batch = tf.slice(pointclouds_pl, + [i*DEVICE_BATCH_SIZE,0,0], [DEVICE_BATCH_SIZE,-1,-1]) + label_batch = tf.slice(labels_pl, + [i*DEVICE_BATCH_SIZE], [DEVICE_BATCH_SIZE]) + + pred, end_points = MODEL.get_model(pc_batch, + is_training=is_training_pl, bn_decay=bn_decay) + + MODEL.get_loss(pred, label_batch, end_points) + losses = tf.get_collection('losses', scope) + total_loss = tf.add_n(losses, name='total_loss') + for l in losses + [total_loss]: + tf.summary.scalar(l.op.name, l) + + grads = optimizer.compute_gradients(total_loss) + tower_grads.append(grads) + + pred_gpu.append(pred) + total_loss_gpu.append(total_loss) + + # Merge pred and losses from multiple GPUs + pred = tf.concat(pred_gpu, 0) + total_loss = tf.reduce_mean(total_loss_gpu) + + # Get training operator + grads = average_gradients(tower_grads) + train_op = optimizer.apply_gradients(grads, global_step=batch) + + correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl)) + accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE) + tf.summary.scalar('accuracy', accuracy) + + # Add ops to save and restore all the variables. + saver = tf.train.Saver() + + # Create a session + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + config.allow_soft_placement = True + config.log_device_placement = False + sess = tf.Session(config=config) + + # Add summary writers + merged = tf.summary.merge_all() + train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph) + test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph) + + # Init variables + init = tf.global_variables_initializer() + sess.run(init) + + ops = {'pointclouds_pl': pointclouds_pl, + 'labels_pl': labels_pl, + 'is_training_pl': is_training_pl, + 'pred': pred, + 'loss': total_loss, + 'train_op': train_op, + 'merged': merged, + 'step': batch, + 'end_points': end_points} + + best_acc = -1 + for epoch in range(MAX_EPOCH): + log_string('**** EPOCH %03d ****' % (epoch)) + sys.stdout.flush() + + train_one_epoch(sess, ops, train_writer) + eval_one_epoch(sess, ops, test_writer) + + # Save the variables to disk. + if epoch % 10 == 0: + save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt")) + log_string("Model saved in file: %s" % save_path) + + +def train_one_epoch(sess, ops, train_writer): + """ ops: dict mapping from string to tf ops """ + is_training = True + + log_string(str(datetime.now())) + + # Make sure batch data is of same size + cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TRAIN_DATASET.num_channel())) + cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) + + total_correct = 0 + total_seen = 0 + loss_sum = 0 + batch_idx = 0 + while TRAIN_DATASET.has_next_batch(): + batch_data, batch_label = TRAIN_DATASET.next_batch(augment=True) + #batch_data = provider.random_point_dropout(batch_data) + bsize = batch_data.shape[0] + cur_batch_data[0:bsize,...] = batch_data + cur_batch_label[0:bsize] = batch_label + + feed_dict = {ops['pointclouds_pl']: cur_batch_data, + ops['labels_pl']: cur_batch_label, + ops['is_training_pl']: is_training,} + summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], + ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict) + train_writer.add_summary(summary, step) + pred_val = np.argmax(pred_val, 1) + correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) + total_correct += correct + total_seen += bsize + loss_sum += loss_val + if (batch_idx+1)%50 == 0: + log_string(' ---- batch: %03d ----' % (batch_idx+1)) + log_string('mean loss: %f' % (loss_sum / 50)) + log_string('accuracy: %f' % (total_correct / float(total_seen))) + total_correct = 0 + total_seen = 0 + loss_sum = 0 + batch_idx += 1 + + TRAIN_DATASET.reset() + +def eval_one_epoch(sess, ops, test_writer): + """ ops: dict mapping from string to tf ops """ + global EPOCH_CNT + is_training = False + + # Make sure batch data is of same size + cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel())) + cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) + + total_correct = 0 + total_seen = 0 + loss_sum = 0 + batch_idx = 0 + shape_ious = [] + total_seen_class = [0 for _ in range(NUM_CLASSES)] + total_correct_class = [0 for _ in range(NUM_CLASSES)] + + log_string(str(datetime.now())) + log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT)) + + while TEST_DATASET.has_next_batch(): + batch_data, batch_label = TEST_DATASET.next_batch(augment=False) + bsize = batch_data.shape[0] + # for the last batch in the epoch, the bsize:end are from last batch + cur_batch_data[0:bsize,...] = batch_data + cur_batch_label[0:bsize] = batch_label + + feed_dict = {ops['pointclouds_pl']: cur_batch_data, + ops['labels_pl']: cur_batch_label, + ops['is_training_pl']: is_training} + summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'], + ops['loss'], ops['pred']], feed_dict=feed_dict) + test_writer.add_summary(summary, step) + pred_val = np.argmax(pred_val, 1) + correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) + total_correct += correct + total_seen += bsize + loss_sum += loss_val + batch_idx += 1 + for i in range(0, bsize): + l = batch_label[i] + total_seen_class[l] += 1 + total_correct_class[l] += (pred_val[i] == l) + + log_string('eval mean loss: %f' % (loss_sum / float(batch_idx))) + log_string('eval accuracy: %f'% (total_correct / float(total_seen))) + log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) + EPOCH_CNT += 1 + + TEST_DATASET.reset() + return total_correct/float(total_seen) + + +if __name__ == "__main__": + log_string('pid: %s'%(str(os.getpid()))) + train() + LOG_FOUT.close() diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/\346\265\213\350\257\225cudnn\346\230\257\345\220\246\345\217\257\347\224\250.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/\346\265\213\350\257\225cudnn\346\230\257\345\220\246\345\217\257\347\224\250.py" new file mode 100644 index 0000000..02b665f --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/\346\265\213\350\257\225cudnn\346\230\257\345\220\246\345\217\257\347\224\250.py" @@ -0,0 +1,3 @@ +#coding = utf-8 +import tensorflow as tf +tf.Session() \ No newline at end of file -- Gitee From 497fbd829de44294ecbd6f3fa0698a6e4e33226c Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:26:55 +0000 Subject: [PATCH 38/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/train=5Fcls.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../train_cls.py" | 229 ------------------ 1 file changed, 229 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" deleted file mode 100644 index 94bd1f6..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train_cls.py" +++ /dev/null @@ -1,229 +0,0 @@ -""" -Author: Benny -Date: Nov 2019 -""" -from data_utils.ModelNetDataLoader import ModelNetDataLoader -import argparse -import numpy as np -import os -import torch -import datetime -import logging -from pathlib import Path -from tqdm import tqdm -import sys -import provider -import importlib -import shutil - -BASE_DIR = os.path.dirname(os.path.abspath(__file__)) -ROOT_DIR = BASE_DIR -sys.path.append(os.path.join(ROOT_DIR, 'models')) - -torch.backends.cudnn.enabled = False -# torch.backends.cudnn.enabled = True -# torch.backends.cudnn.benchmark = False - - -# num_class = 40 -def parse_args(): - '''PARAMETERS''' - parser = argparse.ArgumentParser('PointNet') - parser.add_argument('--batch_size', type=int, default = 8, help='batch size in training [default: 24]') - - parser.add_argument('--model', default='pointnet_cls', help='model name [default: pointnet_cls]') - - parser.add_argument('--num_class', type=int, default= 3, help='class number [default: 40]') - - parser.add_argument('--epoch', default= 150, type=int, help='number of epoch in training [default: 200]') - - parser.add_argument('--learning_rate', default=0.001, type=float, help='learning rate in training [default: 0.001]') - parser.add_argument('--gpu', type=str, default='0', help='specify gpu device [default: 0]') - - parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]') - - parser.add_argument('--optimizer', type=str, default='Adam', help='optimizer for training [default: Adam]') - parser.add_argument('--log_dir', type=str, default=None, help='experiment root') - parser.add_argument('--decay_rate', type=float, default=1e-4, help='decay rate [default: 1e-4]') - parser.add_argument('--normal', action='store_true', default=False, help='Whether to use normal information [default: False]') - args = parser.parse_args() - return args -# https://whttps://www.cnblogs.com/yibeimingyue/p/13800159.html -# argparse.ArgumentParser函数的解释。 - -def test(model, loader,class_number): - class_number =args.num_class - # num_class = args.num_class - - mean_correct = [] - class_acc = np.zeros((class_number,3)) - for j, data in tqdm(enumerate(loader), total=len(loader)): - points, target = data - target = target[:, 0] - points = points.transpose(2, 1) - points, target = points.cuda(), target.cuda() - classifier = model.eval() - pred, _ = classifier(points) - pred_choice = pred.data.max(1)[1] - for cat in np.unique(target.cpu()): - classacc = pred_choice[target==cat].eq(target[target==cat].long().data).cpu().sum() - class_acc[cat,0]+= classacc.item()/float(points[target==cat].size()[0]) - class_acc[cat,1]+=1 - correct = pred_choice.eq(target.long().data).cpu().sum() - mean_correct.append(correct.item()/float(points.size()[0])) - class_acc[:,2] = class_acc[:,0]/ class_acc[:,1] - class_acc = np.mean(class_acc[:,2]) - instance_acc = np.mean(mean_correct) - return instance_acc, class_acc - - -def main(args): - def log_string(str): - logger.info(str) - print(str) - - '''HYPER PARAMETER''' - os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu - - '''CREATE DIR''' - timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')) - experiment_dir = Path('./log/') - experiment_dir.mkdir(exist_ok=True) - experiment_dir = experiment_dir.joinpath('classification') - experiment_dir.mkdir(exist_ok=True) - if args.log_dir is None: - experiment_dir = experiment_dir.joinpath(timestr) - else: - experiment_dir = experiment_dir.joinpath(args.log_dir) - experiment_dir.mkdir(exist_ok=True) - checkpoints_dir = experiment_dir.joinpath('checkpoints/') - checkpoints_dir.mkdir(exist_ok=True) - log_dir = experiment_dir.joinpath('logs/') - log_dir.mkdir(exist_ok=True) - - '''LOG''' - args = parse_args() - logger = logging.getLogger("Model") - logger.setLevel(logging.INFO) - formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') - file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model)) - file_handler.setLevel(logging.INFO) - file_handler.setFormatter(formatter) - logger.addHandler(file_handler) - log_string('PARAMETER ...') - log_string(args) - - '''DATA LOADING''' - log_string('Load dataset ...') - - DATA_PATH = os.path.join(BASE_DIR,'data/modelnet40/') - - TRAIN_DATASET = ModelNetDataLoader(root=DATA_PATH, npoint=args.num_point, split='train', - normal_channel=args.normal) - TEST_DATASET = ModelNetDataLoader(root=DATA_PATH, npoint=args.num_point, split='test', - normal_channel=args.normal) - - trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=4) - testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=4) - - '''MODEL LOADING''' - # num_class = 40 - MODEL = importlib.import_module(args.model) - shutil.copy('./models/%s.py' % args.model, str(experiment_dir)) - shutil.copy('./models/pointnet_util.py', str(experiment_dir)) - - classifier = MODEL.get_model(args.num_class,normal_channel=args.normal).cuda() - criterion = MODEL.get_loss().cuda() - - try: - checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth') - start_epoch = checkpoint['epoch'] - classifier.load_state_dict(checkpoint['model_state_dict']) - log_string('Use pretrain model') - except: - log_string('No existing model, starting training from scratch...') - start_epoch = 0 - - - if args.optimizer == 'Adam': - optimizer = torch.optim.Adam( - classifier.parameters(), - lr=args.learning_rate, - betas=(0.9, 0.999), - eps=1e-08, - weight_decay=args.decay_rate - ) - else: - optimizer = torch.optim.SGD(classifier.parameters(), lr=0.01, momentum=0.9) - - scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.7) - global_epoch = 0 - global_step = 0 - best_instance_acc = 0.0 - best_class_acc = 0.0 - mean_correct = [] - - '''TRANING''' - logger.info('Start training...') - for epoch in range(start_epoch,args.epoch): - log_string('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch)) - - scheduler.step() - for batch_id, data in tqdm(enumerate(trainDataLoader, 0), total=len(trainDataLoader), smoothing=0.9): - points, target = data - points = points.data.numpy() - points = provider.random_point_dropout(points) - points[:,:, 0:3] = provider.random_scale_point_cloud(points[:,:, 0:3]) - points[:,:, 0:3] = provider.shift_point_cloud(points[:,:, 0:3]) - points = torch.Tensor(points) - target = target[:, 0] - - points = points.transpose(2, 1) - points, target = points.cuda(), target.cuda() - optimizer.zero_grad() - - classifier = classifier.train() - pred, trans_feat = classifier(points) - loss = criterion(pred, target.long(), trans_feat) - pred_choice = pred.data.max(1)[1] - correct = pred_choice.eq(target.long().data).cpu().sum() - mean_correct.append(correct.item() / float(points.size()[0])) - loss.backward() - optimizer.step() - global_step += 1 - - train_instance_acc = np.mean(mean_correct) - log_string('Train Instance Accuracy: %f' % train_instance_acc) - - - with torch.no_grad(): - instance_acc, class_acc = test(classifier.eval(), testDataLoader) - - if (instance_acc >= best_instance_acc): - best_instance_acc = instance_acc - best_epoch = epoch + 1 - - if (class_acc >= best_class_acc): - best_class_acc = class_acc - log_string('Test Instance Accuracy: %f, Class Accuracy: %f'% (instance_acc, class_acc)) - log_string('Best Instance Accuracy: %f, Class Accuracy: %f'% (best_instance_acc, best_class_acc)) - - if (instance_acc >= best_instance_acc): - logger.info('Save model...') - savepath = str(checkpoints_dir) + '/best_model.pth' - log_string('Saving at %s'% savepath) - state = { - 'epoch': best_epoch, - 'instance_acc': instance_acc, - 'class_acc': class_acc, - 'model_state_dict': classifier.state_dict(), - 'optimizer_state_dict': optimizer.state_dict(), - } - torch.save(state, savepath) - global_epoch += 1 - - logger.info('End of training...') - -if __name__ == '__main__': - args = parse_args() - main(args) -- Gitee From 20fc0bbaab153d503e177cbf59bdfc4f0d7b4635 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:27:11 +0000 Subject: [PATCH 39/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/test=5Fcls.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../test_cls.py" | 105 ------------------ 1 file changed, 105 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/test_cls.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/test_cls.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/test_cls.py" deleted file mode 100644 index 9e9fa8d..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/test_cls.py" +++ /dev/null @@ -1,105 +0,0 @@ -""" -Author: Benny -Date: Nov 2019 -""" -from data_utils.ModelNetDataLoader import ModelNetDataLoader -import argparse -import numpy as np -import os -import torch -import logging -from tqdm import tqdm -import sys -import importlib - -BASE_DIR = os.path.dirname(os.path.abspath(__file__)) -ROOT_DIR = BASE_DIR -sys.path.append(os.path.join(ROOT_DIR, 'models')) - - -def parse_args(): - '''PARAMETERS''' - parser = argparse.ArgumentParser('PointNet') - parser.add_argument('--batch_size', type=int, default=8, help='batch size in training') - parser.add_argument('--gpu', type=str, default='0', help='specify gpu device') - parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]') - parser.add_argument('--log_dir', type=str, default='pointnet2_ssg_normal', help='Experiment root') - parser.add_argument('--normal', action='store_true', default=True, help='Whether to use normal information [default: False]') - parser.add_argument('--num_votes', type=int, default=3, help='Aggregate classification scores with voting [default: 3]') - return parser.parse_args() - -def test(model, loader, num_class=40, vote_num=1): - mean_correct = [] - class_acc = np.zeros((num_class,3)) - for j, data in tqdm(enumerate(loader), total=len(loader)): - points, target = data - target = target[:, 0] - points = points.transpose(2, 1) - points, target = points.cuda(), target.cuda() - classifier = model.eval() - vote_pool = torch.zeros(target.size()[0],num_class).cuda() - for _ in range(vote_num): - pred, _ = classifier(points) - vote_pool += pred - pred = vote_pool/vote_num - pred_choice = pred.data.max(1)[1] - for cat in np.unique(target.cpu()): - classacc = pred_choice[target==cat].eq(target[target==cat].long().data).cpu().sum() - class_acc[cat,0]+= classacc.item()/float(points[target==cat].size()[0]) - class_acc[cat,1]+=1 - correct = pred_choice.eq(target.long().data).cpu().sum() - mean_correct.append(correct.item()/float(points.size()[0])) - class_acc[:,2] = class_acc[:,0]/ class_acc[:,1] - class_acc = np.mean(class_acc[:,2]) - instance_acc = np.mean(mean_correct) - return instance_acc, class_acc - - -def main(args): - def log_string(str): - logger.info(str) - print(str) - - '''HYPER PARAMETER''' - os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu - - '''CREATE DIR''' - experiment_dir = 'log/classification/' + args.log_dir - - '''LOG''' - args = parse_args() - logger = logging.getLogger("Model") - logger.setLevel(logging.INFO) - formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') - file_handler = logging.FileHandler('%s/eval.txt' % experiment_dir) - file_handler.setLevel(logging.INFO) - file_handler.setFormatter(formatter) - logger.addHandler(file_handler) - log_string('PARAMETER ...') - log_string(args) - - '''DATA LOADING''' - log_string('Load dataset ...') - DATA_PATH = 'data/modelnet40/' - TEST_DATASET = ModelNetDataLoader(root=DATA_PATH, npoint=args.num_point, split='test', normal_channel=args.normal) - testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=4) - - '''MODEL LOADING''' - num_class = 40 - model_name = os.listdir(experiment_dir+'/logs')[0].split('.')[0] - MODEL = importlib.import_module(model_name) - - classifier = MODEL.get_model(num_class,normal_channel=args.normal).cuda() - - checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth') - classifier.load_state_dict(checkpoint['model_state_dict']) - - with torch.no_grad(): - instance_acc, class_acc = test(classifier.eval(), testDataLoader, vote_num=args.num_votes) - log_string('Test Instance Accuracy: %f, Class Accuracy: %f' % (instance_acc, class_acc)) - - - -if __name__ == '__main__': - args = parse_args() - main(args) -- Gitee From 393ac42d6474585d8ee4c0219c41f9f8d91423ca Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:27:40 +0000 Subject: [PATCH 40/69] =?UTF-8?q?add=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../tf_ops" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 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-fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 - -# TF1.4 -#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 - -g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include -I /usr/local/cuda-9.0/include -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-9.0/lib64/ -L/home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow -l:libtensorflow_framework.so -O2 -D_GLIBCXX_USE_CXX11_ABI=0 -- Gitee From f8d59c5bee11671dd871a86d5d54387672dcf85b Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:29:00 +0000 Subject: [PATCH 44/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/provider.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../provider.py" | 251 ------------------ 1 file changed, 251 deletions(-) delete mode 100644 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"a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/provider.py" +++ /dev/null @@ -1,251 +0,0 @@ -import numpy as np - -def normalize_data(batch_data): - """ Normalize the batch data, use coordinates of the block centered at origin, - Input: - BxNxC array - Output: - BxNxC array - """ - B, N, C = batch_data.shape - normal_data = np.zeros((B, N, C)) - for b in range(B): - pc = batch_data[b] - centroid = np.mean(pc, axis=0) - pc = pc - centroid - m = np.max(np.sqrt(np.sum(pc ** 2, axis=1))) - pc = pc / m - normal_data[b] = pc - return normal_data - - -def shuffle_data(data, labels): - """ Shuffle data and labels. - Input: - data: B,N,... numpy array - label: B,... numpy array - Return: - shuffled data, label and shuffle indices - """ - idx = np.arange(len(labels)) - np.random.shuffle(idx) - return data[idx, ...], labels[idx], idx - -def shuffle_points(batch_data): - """ Shuffle orders of points in each point cloud -- changes FPS behavior. - Use the same shuffling idx for the entire batch. - Input: - BxNxC array - Output: - BxNxC array - """ - idx = np.arange(batch_data.shape[1]) - np.random.shuffle(idx) - return batch_data[:,idx,:] - -def rotate_point_cloud(batch_data): - """ Randomly rotate the point clouds to augument the dataset - rotation is per shape based along up direction - Input: - BxNx3 array, original batch of point clouds - Return: - BxNx3 array, rotated batch of point clouds - """ - rotated_data = np.zeros(batch_data.shape, dtype=np.float32) - for k in range(batch_data.shape[0]): - rotation_angle = np.random.uniform() * 2 * np.pi - cosval = np.cos(rotation_angle) - sinval = np.sin(rotation_angle) - rotation_matrix = np.array([[cosval, 0, sinval], - [0, 1, 0], - [-sinval, 0, cosval]]) - shape_pc = batch_data[k, ...] - rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) - return rotated_data - -def rotate_point_cloud_z(batch_data): - """ Randomly rotate the point clouds to augument the dataset - rotation is per shape based along up direction - Input: - BxNx3 array, original batch of point clouds - Return: - BxNx3 array, rotated batch of point clouds - """ - rotated_data = np.zeros(batch_data.shape, dtype=np.float32) - for k in range(batch_data.shape[0]): - rotation_angle = np.random.uniform() * 2 * np.pi - cosval = np.cos(rotation_angle) - sinval = np.sin(rotation_angle) - rotation_matrix = np.array([[cosval, sinval, 0], - [-sinval, cosval, 0], - [0, 0, 1]]) - shape_pc = batch_data[k, ...] - rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) - return rotated_data - -def rotate_point_cloud_with_normal(batch_xyz_normal): - ''' Randomly rotate XYZ, normal point cloud. - Input: - batch_xyz_normal: B,N,6, first three channels are XYZ, last 3 all normal - Output: - B,N,6, rotated XYZ, normal point cloud - ''' - for k in range(batch_xyz_normal.shape[0]): - rotation_angle = np.random.uniform() * 2 * np.pi - cosval = np.cos(rotation_angle) - sinval = np.sin(rotation_angle) - rotation_matrix = np.array([[cosval, 0, sinval], - [0, 1, 0], - [-sinval, 0, cosval]]) - shape_pc = batch_xyz_normal[k,:,0:3] - shape_normal = batch_xyz_normal[k,:,3:6] - batch_xyz_normal[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) - batch_xyz_normal[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), rotation_matrix) - return batch_xyz_normal - -def rotate_perturbation_point_cloud_with_normal(batch_data, angle_sigma=0.06, angle_clip=0.18): - """ Randomly perturb the point clouds by small rotations - Input: - BxNx6 array, original batch of point clouds and point normals - Return: - BxNx3 array, rotated batch of point clouds - """ - rotated_data = np.zeros(batch_data.shape, dtype=np.float32) - for k in range(batch_data.shape[0]): - angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip) - Rx = np.array([[1,0,0], - [0,np.cos(angles[0]),-np.sin(angles[0])], - [0,np.sin(angles[0]),np.cos(angles[0])]]) - Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])], - [0,1,0], - [-np.sin(angles[1]),0,np.cos(angles[1])]]) - Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0], - [np.sin(angles[2]),np.cos(angles[2]),0], - [0,0,1]]) - R = np.dot(Rz, np.dot(Ry,Rx)) - shape_pc = batch_data[k,:,0:3] - shape_normal = batch_data[k,:,3:6] - rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), R) - rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), R) - return rotated_data - - -def rotate_point_cloud_by_angle(batch_data, rotation_angle): - """ Rotate the point cloud along up direction with certain angle. - Input: - BxNx3 array, original batch of point clouds - Return: - BxNx3 array, rotated batch of point clouds - """ - rotated_data = np.zeros(batch_data.shape, dtype=np.float32) - for k in range(batch_data.shape[0]): - #rotation_angle = np.random.uniform() * 2 * np.pi - cosval = np.cos(rotation_angle) - sinval = np.sin(rotation_angle) - rotation_matrix = np.array([[cosval, 0, sinval], - [0, 1, 0], - [-sinval, 0, cosval]]) - shape_pc = batch_data[k,:,0:3] - rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) - return rotated_data - -def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle): - """ Rotate the point cloud along up direction with certain angle. - Input: - BxNx6 array, original batch of point clouds with normal - scalar, angle of rotation - Return: - BxNx6 array, rotated batch of point clouds iwth normal - """ - rotated_data = np.zeros(batch_data.shape, dtype=np.float32) - for k in range(batch_data.shape[0]): - #rotation_angle = np.random.uniform() * 2 * np.pi - cosval = np.cos(rotation_angle) - sinval = np.sin(rotation_angle) - rotation_matrix = np.array([[cosval, 0, sinval], - [0, 1, 0], - [-sinval, 0, cosval]]) - shape_pc = batch_data[k,:,0:3] - shape_normal = batch_data[k,:,3:6] - rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) - rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1,3)), rotation_matrix) - return rotated_data - - - -def rotate_perturbation_point_cloud(batch_data, angle_sigma=0.06, angle_clip=0.18): - """ Randomly perturb the point clouds by small rotations - Input: - BxNx3 array, original batch of point clouds - Return: - BxNx3 array, rotated batch of point clouds - """ - rotated_data = np.zeros(batch_data.shape, dtype=np.float32) - for k in range(batch_data.shape[0]): - angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip) - Rx = np.array([[1,0,0], - [0,np.cos(angles[0]),-np.sin(angles[0])], - [0,np.sin(angles[0]),np.cos(angles[0])]]) - Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])], - [0,1,0], - [-np.sin(angles[1]),0,np.cos(angles[1])]]) - Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0], - [np.sin(angles[2]),np.cos(angles[2]),0], - [0,0,1]]) - R = np.dot(Rz, np.dot(Ry,Rx)) - shape_pc = batch_data[k, ...] - rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), R) - return rotated_data - - -def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05): - """ Randomly jitter points. jittering is per point. - Input: - BxNx3 array, original batch of point clouds - Return: - BxNx3 array, jittered batch of point clouds - """ - B, N, C = batch_data.shape - assert(clip > 0) - jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip) - jittered_data += batch_data - return jittered_data - -def shift_point_cloud(batch_data, shift_range=0.1): - """ Randomly shift point cloud. Shift is per point cloud. - Input: - BxNx3 array, original batch of point clouds - Return: - BxNx3 array, shifted batch of point clouds - """ - B, N, C = batch_data.shape - shifts = np.random.uniform(-shift_range, shift_range, (B,3)) - for batch_index in range(B): - batch_data[batch_index,:,:] += shifts[batch_index,:] - return batch_data - - -def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25): - """ Randomly scale the point cloud. Scale is per point cloud. - Input: - BxNx3 array, original batch of point clouds - Return: - BxNx3 array, scaled batch of point clouds - """ - B, N, C = batch_data.shape - scales = np.random.uniform(scale_low, scale_high, B) - for batch_index in range(B): - batch_data[batch_index,:,:] *= scales[batch_index] - return batch_data - -def random_point_dropout(batch_pc, max_dropout_ratio=0.875): - ''' batch_pc: BxNx3 ''' - for b in range(batch_pc.shape[0]): - dropout_ratio = np.random.random()*max_dropout_ratio # 0~0.875 - drop_idx = np.where(np.random.random((batch_pc.shape[1]))<=dropout_ratio)[0] - if len(drop_idx)>0: - batch_pc[b,drop_idx,:] = batch_pc[b,0,:] # set to the first point - return batch_pc - - - -- Gitee From d52d19c271c85b85060b62fc555f308ac0df9cc2 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:30:33 +0000 Subject: [PATCH 45/69] 3d Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../tf_ops/interpolate.cpp" | 169 +++++++++++ .../tf_ops/tf_interpolate.cpp" | 267 ++++++++++++++++++ .../tf_ops/tf_interpolate.py" | 59 ++++ .../tf_ops/tf_interpolate_compile.sh" | 7 + .../tf_ops/tf_interpolate_op_test.py" | 24 ++ .../tf_ops/tf_interpolate_so.so" | Bin 0 -> 52296 bytes .../tf_ops/visu_interpolation.py" | 44 +++ 7 files changed, 570 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/interpolate.cpp" create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.cpp" create mode 100644 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"code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_so.so" create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/visu_interpolation.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/interpolate.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/interpolate.cpp" new file mode 100644 index 0000000..b7d0dd0 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/interpolate.cpp" @@ -0,0 +1,169 @@ +#include +#include +#include // memset +#include // rand, RAND_MAX +#include // sqrtf +#include +#include +using namespace std; +float randomf(){ + return (rand()+0.5)/(RAND_MAX+1.0); +} +static double get_time(){ + timespec tp; + clock_gettime(CLOCK_MONOTONIC,&tp); + return tp.tv_sec+tp.tv_nsec*1e-9; +} + +// Find three nearest neigbors with square distance +// input: xyz1 (b,n,3), xyz2(b,m,3) +// output: dist (b,n,3), idx (b,n,3) +void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { + for (int i=0;i +#include +#include // memset +#include // rand, RAND_MAX +#include // sqrtf +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/framework/common_shape_fns.h" + +#pragma GCC diagnostic ignored "-Wunused-result" + +using namespace tensorflow; + +REGISTER_OP("ThreeNN") + .Input("xyz1: float32") + .Input("xyz2: float32") + .Output("dist: float32") + .Output("idx: int32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + c->set_output(1, c->input(0)); + return Status::OK(); + }); +REGISTER_OP("ThreeInterpolate") + .Input("points: float32") + .Input("idx: int32") + .Input("weight: float32") + .Output("out: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle dims1; // (b,m,c) + c->WithRank(c->input(0), 3, &dims1); + ::tensorflow::shape_inference::ShapeHandle dims2; // (b,n,3) + c->WithRank(c->input(1), 3, &dims2); + // (b,n,c) + ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)}); + c->set_output(0, output); + return Status::OK(); + }); +REGISTER_OP("ThreeInterpolateGrad") + .Input("points: float32") + .Input("idx: int32") + .Input("weight: float32") + .Input("grad_out: float32") + .Output("grad_points: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + return Status::OK(); + }); + +float randomf(){ + return (rand()+0.5)/(RAND_MAX+1.0); +} +static double get_time(){ + timespec tp; + clock_gettime(CLOCK_MONOTONIC,&tp); + return tp.tv_sec+tp.tv_nsec*1e-9; +} + +// Find three nearest neigbors with square distance +// input: xyz1 (b,n,3), xyz2(b,m,3) +// output: dist (b,n,3), idx (b,n,3) +void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { + for (int i=0;iinput(0); + OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,n,3) xyz1 shape.")); + int b = xyz1_tensor.shape().dim_size(0); + int n = xyz1_tensor.shape().dim_size(1); + + const Tensor& xyz2_tensor = context->input(1); + OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,m,3) xyz2 shape.")); + int m = xyz2_tensor.shape().dim_size(1); + + Tensor *dist_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,n,3}, &dist_tensor)); + Tensor *idx_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,n,3}, &idx_tensor)); + + auto xyz1_flat = xyz1_tensor.flat(); + const float *xyz1 = &(xyz1_flat(0)); + auto xyz2_flat = xyz2_tensor.flat(); + const float *xyz2 = &(xyz2_flat(0)); + auto dist_flat = dist_tensor->flat(); + float *dist = &(dist_flat(0)); + auto idx_flat = idx_tensor->flat(); + int *idx = &(idx_flat(0)); + threenn_cpu(b,n,m,xyz1,xyz2,dist,idx); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreeNN").Device(DEVICE_CPU), ThreeNNOp); + + + +class ThreeInterpolateOp: public OpKernel{ + public: + explicit ThreeInterpolateOp(OpKernelConstruction * context):OpKernel(context){} + + void Compute(OpKernelContext * context) override { + const Tensor& points_tensor=context->input(0); + OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolate expects (b,m,c) points shape")); + int b = points_tensor.shape().dim_size(0); + int m = points_tensor.shape().dim_size(1); + int c = points_tensor.shape().dim_size(2); + + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b && idx_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) idx shape")); + int n = idx_tensor.shape().dim_size(1); + const Tensor& weight_tensor=context->input(2); + OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) weight shape")); + + Tensor * out_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &out_tensor)); + + auto points_flat = points_tensor.flat(); + const float *points = &(points_flat(0)); + auto idx_flat = idx_tensor.flat(); + const int *idx = &(idx_flat(0)); + auto weight_flat = weight_tensor.flat(); + const float *weight = &(weight_flat(0)); + auto out_flat = out_tensor->flat(); + float *out = &(out_flat(0)); + threeinterpolate_cpu(b,m,c,n,points,idx,weight,out); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreeInterpolate").Device(DEVICE_CPU),ThreeInterpolateOp); + + +class ThreeInterpolateGradOp: public OpKernel{ + public: + explicit ThreeInterpolateGradOp(OpKernelConstruction * context):OpKernel(context){} + + void Compute(OpKernelContext * context) override { + const Tensor& points_tensor=context->input(0); + OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,m,c) points shape")); + int b = points_tensor.shape().dim_size(0); + int m = points_tensor.shape().dim_size(1); + int c = points_tensor.shape().dim_size(2); + + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) idx shape")); + int n = idx_tensor.shape().dim_size(1); + const Tensor& weight_tensor=context->input(2); + OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) weight shape")); + + const Tensor& grad_out_tensor=context->input(3); + OP_REQUIRES(context,grad_out_tensor.dims()==3 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==n && grad_out_tensor.shape().dim_size(2)==c, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,c) grad_out shape")); + + Tensor * grad_points_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,c}, &grad_points_tensor)); + + auto points_flat = points_tensor.flat(); + const float *points = &(points_flat(0)); + auto idx_flat = idx_tensor.flat(); + const int *idx = &(idx_flat(0)); + auto weight_flat = weight_tensor.flat(); + const float *weight = &(weight_flat(0)); + auto grad_out_flat = grad_out_tensor.flat(); + const float *grad_out = &(grad_out_flat(0)); + auto grad_points_flat = grad_points_tensor->flat(); + float *grad_points = &(grad_points_flat(0)); + memset(grad_points, 0, sizeof(float)*b*m*c); + threeinterpolate_grad_cpu(b,n,c,m,grad_out,idx,weight,grad_points); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreeInterpolateGrad").Device(DEVICE_CPU),ThreeInterpolateGradOp); + + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.py" new file mode 100644 index 0000000..2ef1edd --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.py" @@ -0,0 +1,59 @@ +import tensorflow as tf +from tensorflow.python.framework import ops +import sys +import os +BASE_DIR = os.path.dirname(__file__) +sys.path.append(BASE_DIR) +interpolate_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_interpolate_so.so')) +def three_nn(xyz1, xyz2): + ''' + Input: + xyz1: (b,n,3) float32 array, unknown points + xyz2: (b,m,3) float32 array, known points + Output: + dist: (b,n,3) float32 array, distances to known points + idx: (b,n,3) int32 array, indices to known points + ''' + return interpolate_module.three_nn(xyz1, xyz2) +ops.NoGradient('ThreeNN') +def three_interpolate(points, idx, weight): + ''' + Input: + points: (b,m,c) float32 array, known points + idx: (b,n,3) int32 array, indices to known points + weight: (b,n,3) float32 array, weights on known points + Output: + out: (b,n,c) float32 array, interpolated point values + ''' + return interpolate_module.three_interpolate(points, idx, weight) +@tf.RegisterGradient('ThreeInterpolate') +def _three_interpolate_grad(op, grad_out): + points = op.inputs[0] + idx = op.inputs[1] + weight = op.inputs[2] + return [interpolate_module.three_interpolate_grad(points, idx, weight, grad_out), None, None] + +if __name__=='__main__': + import numpy as np + import time + np.random.seed(100) + pts = np.random.random((32,128,64)).astype('float32') + tmp1 = np.random.random((32,512,3)).astype('float32') + tmp2 = np.random.random((32,128,3)).astype('float32') + with tf.device('/cpu:0'): + points = tf.constant(pts) + xyz1 = tf.constant(tmp1) + xyz2 = tf.constant(tmp2) + dist, idx = three_nn(xyz1, xyz2) + weight = tf.ones_like(dist)/3.0 + interpolated_points = three_interpolate(points, idx, weight) + with tf.Session('') as sess: + now = time.time() + for _ in range(100): + ret = sess.run(interpolated_points) + print (time.time() - now) + print (ret.shape, ret.dtype) + #print ret + + + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_compile.sh" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_compile.sh" new file mode 100644 index 0000000..8af3cf7 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_compile.sh" @@ -0,0 +1,7 @@ +# TF1.2 +#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +# TF1.4 +#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include -I /usr/local/cuda-9.0/include -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-9.0/lib64/ -L/home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow -l:libtensorflow_framework.so -O2 -D_GLIBCXX_USE_CXX11_ABI=0 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_op_test.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_op_test.py" new file mode 100644 index 0000000..030456d --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_op_test.py" @@ -0,0 +1,24 @@ +import tensorflow as tf +import numpy as np +from tf_interpolate import three_nn, three_interpolate + +class GroupPointTest(tf.test.TestCase): + def test(self): + pass + + def test_grad(self): + with self.test_session(): + points = tf.constant(np.random.random((1,8,16)).astype('float32')) + print (points) + xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32')) + xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32')) + dist, idx = three_nn(xyz1, xyz2) + weight = tf.ones_like(dist)/3.0 + interpolated_points = three_interpolate(points, idx, weight) + print(interpolated_points) + err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16)) + print (err) + self.assertLess(err, 1e-4) + +if __name__=='__main__': + tf.test.main() diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_so.so" 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zMU-;3I4Q?zIlj57l+zSlDW^L!U<$B>Z@H&S+=J3*1-H(+X;)O-BmdtdcnpBOQzjleNW3Sa zy)U9YU!avC9uv?=r5tDYq+meHuf0#ArMtv)HQMt;+H**n0J_hQM;7snI6t)aPPB9n z9_^rm$|X@+emoAsjyn8HvbFr$`zczgy@$t`@sChPBNZ#o=$$w%zxE!BmY$ZTzs9Gf z^O2w4vD0YneHSg=BuXOvNl#6G0W0k|p=(-x?Y%xN)!yq%x1UyjzsNsR2-M!6(NgWb zLduUvcM|1m`Fnv;74g_KJ2qFPrj$oMNLTrw$CBjy+WRw+WR6}dUAzaI{${0j0~ma*WLrtQapy7a-`Rvo?D{&YweeKU#OdB z^J#_^Qj8+ilK;SooL_sdNK5aci_(!^{*Q4@&ad8Z)k@@aG~G83lNUwaOD zlgOXWpJq;_5CCB&O`u4%{8Q3!Ex(+mRgvY`rImj=7l6mflm`4XeYE&R{ZrGObMk-p fJiyb}@SK=@Xys}cJsYQ&|FNx{qa-au8masrv^e4* literal 0 HcmV?d00001 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/visu_interpolation.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/visu_interpolation.py" new file mode 100644 index 0000000..bc82820 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/visu_interpolation.py" @@ -0,0 +1,44 @@ +''' Visualize part segmentation ''' +import os +import sys +ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.append('/home/rqi/Projects/toolkits/visualization') +from show3d_balls import showpoints +import numpy as np +from tf_interpolate import three_nn, three_interpolate +import tensorflow as tf + + +pts2 = np.array([[0,0,1],[1,0,0],[0,1,0],[1,1,0]]).astype('float32') +xyz1 = np.random.random((100,3)).astype('float32') +xyz2 = np.array([[0,0,0],[1,0,0],[0,1,0],[1,1,1]]).astype('float32') + +def fun(xyz1,xyz2,pts2): + with tf.device('/cpu:0'): + points = tf.constant(np.expand_dims(pts2,0)) + xyz1 = tf.constant(np.expand_dims(xyz1,0)) + xyz2 = tf.constant(np.expand_dims(xyz2,0)) + dist, idx = three_nn(xyz1, xyz2) + #weight = tf.ones_like(dist)/3.0 + dist = tf.maximum(dist, 1e-10) + norm = tf.reduce_sum((1.0/dist),axis=2,keep_dims=True) + norm = tf.tile(norm, [1,1,3]) + print (norm) + weight = (1.0/dist) / norm + interpolated_points = three_interpolate(points, idx, weight) + with tf.Session('') as sess: + tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight]) + #print w + pts1 = pts1.squeeze() + return pts1 + +pts1 = fun(xyz1,xyz2,pts2) +all_pts = np.zeros((104,3)) +all_pts[0:100,:] = pts1 +all_pts[100:,:] = pts2 +all_xyz = np.zeros((104,3)) +all_xyz[0:100,:]=xyz1 +all_xyz[100:,:]=xyz2 +showpoints(xyz2, pts2, ballradius=8) +showpoints(xyz1, pts1, ballradius=8) +showpoints(all_xyz, all_pts, ballradius=8) -- Gitee From 0ede0e3c89ce160077fd5d66fe6c81c3b135718f Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:31:06 +0000 Subject: [PATCH 46/69] =?UTF-8?q?=E6=96=B0=E5=BB=BA=203d=5Finterpolation?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/3d_interpolation/.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/.keep" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/.keep" new file mode 100644 index 0000000..e69de29 -- Gitee From d09575750846d44014ba942e7517f9a9ec653a07 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:31:25 +0000 Subject: [PATCH 47/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/tf=5Fops/interpolate.cpp?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/interpolate.cpp" | 169 ------------------ 1 file changed, 169 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/interpolate.cpp" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/interpolate.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/interpolate.cpp" deleted file mode 100644 index b7d0dd0..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/interpolate.cpp" +++ /dev/null @@ -1,169 +0,0 @@ -#include -#include -#include // memset -#include // rand, RAND_MAX -#include // sqrtf -#include -#include -using namespace std; -float randomf(){ - return (rand()+0.5)/(RAND_MAX+1.0); -} -static double get_time(){ - timespec tp; - clock_gettime(CLOCK_MONOTONIC,&tp); - return tp.tv_sec+tp.tv_nsec*1e-9; -} - -// Find three nearest neigbors with square distance -// input: xyz1 (b,n,3), xyz2(b,m,3) -// output: dist (b,n,3), idx (b,n,3) -void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { - for (int i=0;i Date: Fri, 28 Oct 2022 11:31:31 +0000 Subject: [PATCH 48/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/tf=5Fops/tf=5Finterpolate.cpp?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/tf_interpolate.cpp" | 267 ------------------ 1 file changed, 267 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.cpp" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.cpp" deleted file mode 100644 index 75cf8c8..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.cpp" +++ /dev/null @@ -1,267 +0,0 @@ -#include -#include -#include // memset -#include // rand, RAND_MAX -#include // sqrtf -#include "tensorflow/core/framework/op.h" -#include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/framework/shape_inference.h" -#include "tensorflow/core/framework/common_shape_fns.h" - -#pragma GCC diagnostic ignored "-Wunused-result" - -using namespace tensorflow; - -REGISTER_OP("ThreeNN") - .Input("xyz1: float32") - .Input("xyz2: float32") - .Output("dist: float32") - .Output("idx: int32") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->input(0)); - c->set_output(1, c->input(0)); - return Status::OK(); - }); -REGISTER_OP("ThreeInterpolate") - .Input("points: float32") - .Input("idx: int32") - .Input("weight: float32") - .Output("out: float32") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - ::tensorflow::shape_inference::ShapeHandle dims1; // (b,m,c) - c->WithRank(c->input(0), 3, &dims1); - ::tensorflow::shape_inference::ShapeHandle dims2; // (b,n,3) - c->WithRank(c->input(1), 3, &dims2); - // (b,n,c) - ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)}); - c->set_output(0, output); - return Status::OK(); - }); -REGISTER_OP("ThreeInterpolateGrad") - .Input("points: float32") - .Input("idx: int32") - .Input("weight: float32") - .Input("grad_out: float32") - .Output("grad_points: float32") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->input(0)); - return Status::OK(); - }); - -float randomf(){ - return (rand()+0.5)/(RAND_MAX+1.0); -} -static double get_time(){ - timespec tp; - clock_gettime(CLOCK_MONOTONIC,&tp); - return tp.tv_sec+tp.tv_nsec*1e-9; -} - -// Find three nearest neigbors with square distance -// input: xyz1 (b,n,3), xyz2(b,m,3) -// output: dist (b,n,3), idx (b,n,3) -void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { - for (int i=0;iinput(0); - OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,n,3) xyz1 shape.")); - int b = xyz1_tensor.shape().dim_size(0); - int n = xyz1_tensor.shape().dim_size(1); - - const Tensor& xyz2_tensor = context->input(1); - OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,m,3) xyz2 shape.")); - int m = xyz2_tensor.shape().dim_size(1); - - Tensor *dist_tensor = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,n,3}, &dist_tensor)); - Tensor *idx_tensor = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,n,3}, &idx_tensor)); - - auto xyz1_flat = xyz1_tensor.flat(); - const float *xyz1 = &(xyz1_flat(0)); - auto xyz2_flat = xyz2_tensor.flat(); - const float *xyz2 = &(xyz2_flat(0)); - auto dist_flat = dist_tensor->flat(); - float *dist = &(dist_flat(0)); - auto idx_flat = idx_tensor->flat(); - int *idx = &(idx_flat(0)); - threenn_cpu(b,n,m,xyz1,xyz2,dist,idx); - } -}; -REGISTER_KERNEL_BUILDER(Name("ThreeNN").Device(DEVICE_CPU), ThreeNNOp); - - - -class ThreeInterpolateOp: public OpKernel{ - public: - explicit ThreeInterpolateOp(OpKernelConstruction * context):OpKernel(context){} - - void Compute(OpKernelContext * context) override { - const Tensor& points_tensor=context->input(0); - OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolate expects (b,m,c) points shape")); - int b = points_tensor.shape().dim_size(0); - int m = points_tensor.shape().dim_size(1); - int c = points_tensor.shape().dim_size(2); - - const Tensor& idx_tensor=context->input(1); - OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b && idx_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) idx shape")); - int n = idx_tensor.shape().dim_size(1); - const Tensor& weight_tensor=context->input(2); - OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) weight shape")); - - Tensor * out_tensor = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &out_tensor)); - - auto points_flat = points_tensor.flat(); - const float *points = &(points_flat(0)); - auto idx_flat = idx_tensor.flat(); - const int *idx = &(idx_flat(0)); - auto weight_flat = weight_tensor.flat(); - const float *weight = &(weight_flat(0)); - auto out_flat = out_tensor->flat(); - float *out = &(out_flat(0)); - threeinterpolate_cpu(b,m,c,n,points,idx,weight,out); - } -}; -REGISTER_KERNEL_BUILDER(Name("ThreeInterpolate").Device(DEVICE_CPU),ThreeInterpolateOp); - - -class ThreeInterpolateGradOp: public OpKernel{ - public: - explicit ThreeInterpolateGradOp(OpKernelConstruction * context):OpKernel(context){} - - void Compute(OpKernelContext * context) override { - const Tensor& points_tensor=context->input(0); - OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,m,c) points shape")); - int b = points_tensor.shape().dim_size(0); - int m = points_tensor.shape().dim_size(1); - int c = points_tensor.shape().dim_size(2); - - const Tensor& idx_tensor=context->input(1); - OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) idx shape")); - int n = idx_tensor.shape().dim_size(1); - const Tensor& weight_tensor=context->input(2); - OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) weight shape")); - - const Tensor& grad_out_tensor=context->input(3); - OP_REQUIRES(context,grad_out_tensor.dims()==3 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==n && grad_out_tensor.shape().dim_size(2)==c, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,c) grad_out shape")); - - Tensor * grad_points_tensor = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,c}, &grad_points_tensor)); - - auto points_flat = points_tensor.flat(); - const float *points = &(points_flat(0)); - auto idx_flat = idx_tensor.flat(); - const int *idx = &(idx_flat(0)); - auto weight_flat = weight_tensor.flat(); - const float *weight = &(weight_flat(0)); - auto grad_out_flat = grad_out_tensor.flat(); - const float *grad_out = &(grad_out_flat(0)); - auto grad_points_flat = grad_points_tensor->flat(); - float *grad_points = &(grad_points_flat(0)); - memset(grad_points, 0, sizeof(float)*b*m*c); - threeinterpolate_grad_cpu(b,n,c,m,grad_out,idx,weight,grad_points); - } -}; -REGISTER_KERNEL_BUILDER(Name("ThreeInterpolateGrad").Device(DEVICE_CPU),ThreeInterpolateGradOp); - - -- Gitee From b59bf3eeaae6b81ddc457cb12ef3bbd76432d858 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:31:36 +0000 Subject: [PATCH 49/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/tf=5Fops/tf=5Finterpolate.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/tf_interpolate.py" | 59 ------------------- 1 file changed, 59 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.py" deleted file mode 100644 index 2ef1edd..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate.py" +++ /dev/null @@ -1,59 +0,0 @@ -import tensorflow as tf -from tensorflow.python.framework import ops -import sys -import os -BASE_DIR = os.path.dirname(__file__) -sys.path.append(BASE_DIR) -interpolate_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_interpolate_so.so')) -def three_nn(xyz1, xyz2): - ''' - Input: - xyz1: (b,n,3) float32 array, unknown points - xyz2: (b,m,3) float32 array, known points - Output: - dist: (b,n,3) float32 array, distances to known points - idx: (b,n,3) int32 array, indices to known points - ''' - return interpolate_module.three_nn(xyz1, xyz2) -ops.NoGradient('ThreeNN') -def three_interpolate(points, idx, weight): - ''' - Input: - points: (b,m,c) float32 array, known points - idx: (b,n,3) int32 array, indices to known points - weight: (b,n,3) float32 array, weights on known points - Output: - out: (b,n,c) float32 array, interpolated point values - ''' - return interpolate_module.three_interpolate(points, idx, weight) -@tf.RegisterGradient('ThreeInterpolate') -def _three_interpolate_grad(op, grad_out): - points = op.inputs[0] - idx = op.inputs[1] - weight = op.inputs[2] - return [interpolate_module.three_interpolate_grad(points, idx, weight, grad_out), None, None] - -if __name__=='__main__': - import numpy as np - import time - np.random.seed(100) - pts = np.random.random((32,128,64)).astype('float32') - tmp1 = np.random.random((32,512,3)).astype('float32') - tmp2 = np.random.random((32,128,3)).astype('float32') - with tf.device('/cpu:0'): - points = tf.constant(pts) - xyz1 = tf.constant(tmp1) - xyz2 = tf.constant(tmp2) - dist, idx = three_nn(xyz1, xyz2) - weight = tf.ones_like(dist)/3.0 - interpolated_points = three_interpolate(points, idx, weight) - with tf.Session('') as sess: - now = time.time() - for _ in range(100): - ret = sess.run(interpolated_points) - print (time.time() - now) - print (ret.shape, ret.dtype) - #print ret - - - -- Gitee From bdd3effe9b86a742e8c755abf46ad56ae9b1014a Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:31:41 +0000 Subject: [PATCH 50/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/tf=5Fops/tf=5Finterpolate=5Fcompile.sh?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/tf_interpolate_compile.sh" | 7 ------- 1 file changed, 7 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_compile.sh" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_compile.sh" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_compile.sh" deleted file mode 100644 index 8af3cf7..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_compile.sh" +++ /dev/null @@ -1,7 +0,0 @@ -# TF1.2 -#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 - -# TF1.4 -#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 - -g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include -I /usr/local/cuda-9.0/include -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-9.0/lib64/ -L/home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow -l:libtensorflow_framework.so -O2 -D_GLIBCXX_USE_CXX11_ABI=0 -- Gitee From b28c228414f0d4693939bc73e02852d0fb757fa9 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:31:45 +0000 Subject: [PATCH 51/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/tf=5Fops/tf=5Finterpolate=5Fop=5Ftest.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/tf_interpolate_op_test.py" | 24 ------------------- 1 file changed, 24 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_op_test.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_op_test.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_op_test.py" deleted file mode 100644 index 030456d..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/tf_interpolate_op_test.py" +++ /dev/null @@ -1,24 +0,0 @@ -import tensorflow as tf -import numpy as np -from tf_interpolate import three_nn, three_interpolate - -class GroupPointTest(tf.test.TestCase): - def test(self): - pass - - def test_grad(self): - with self.test_session(): - points = tf.constant(np.random.random((1,8,16)).astype('float32')) - print (points) - xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32')) - xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32')) - dist, idx = three_nn(xyz1, xyz2) - weight = tf.ones_like(dist)/3.0 - interpolated_points = three_interpolate(points, idx, weight) - print(interpolated_points) - err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16)) - print (err) - self.assertLess(err, 1e-4) - -if __name__=='__main__': - tf.test.main() -- Gitee From 669b1a2641582e83dee981ba320ff59ba47f019b Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:31:50 +0000 Subject: [PATCH 52/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/tf=5Fops/tf=5Finterpolate=5Fso.so?= 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=?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/tf=5Fops/visu=5Finterpolation.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/visu_interpolation.py" | 44 ------------------- 1 file changed, 44 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/visu_interpolation.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/visu_interpolation.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/visu_interpolation.py" deleted file mode 100644 index bc82820..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/visu_interpolation.py" +++ /dev/null @@ -1,44 +0,0 @@ -''' Visualize part segmentation ''' -import os -import sys -ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -sys.path.append('/home/rqi/Projects/toolkits/visualization') -from show3d_balls import showpoints -import numpy as np -from tf_interpolate import three_nn, three_interpolate -import tensorflow as tf - - -pts2 = np.array([[0,0,1],[1,0,0],[0,1,0],[1,1,0]]).astype('float32') -xyz1 = np.random.random((100,3)).astype('float32') -xyz2 = np.array([[0,0,0],[1,0,0],[0,1,0],[1,1,1]]).astype('float32') - -def fun(xyz1,xyz2,pts2): - with tf.device('/cpu:0'): - points = tf.constant(np.expand_dims(pts2,0)) - xyz1 = tf.constant(np.expand_dims(xyz1,0)) - xyz2 = tf.constant(np.expand_dims(xyz2,0)) - dist, idx = three_nn(xyz1, xyz2) - #weight = tf.ones_like(dist)/3.0 - dist = tf.maximum(dist, 1e-10) - norm = tf.reduce_sum((1.0/dist),axis=2,keep_dims=True) - norm = tf.tile(norm, [1,1,3]) - print (norm) - weight = (1.0/dist) / norm - interpolated_points = three_interpolate(points, idx, weight) - with tf.Session('') as sess: - tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight]) - #print w - pts1 = pts1.squeeze() - return pts1 - -pts1 = fun(xyz1,xyz2,pts2) -all_pts = np.zeros((104,3)) -all_pts[0:100,:] = pts1 -all_pts[100:,:] = pts2 -all_xyz = np.zeros((104,3)) -all_xyz[0:100,:]=xyz1 -all_xyz[100:,:]=xyz2 -showpoints(xyz2, pts2, ballradius=8) -showpoints(xyz1, pts1, ballradius=8) -showpoints(all_xyz, all_pts, ballradius=8) -- Gitee From 79522fd140267bc981a278a0f907719c428744de Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:32:32 +0000 Subject: [PATCH 54/69] =?UTF-8?q?3D-=E6=8F=92=E5=80=BC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../tf_ops/3d_interpolation/interpolate.cpp" | 169 +++++++++++ .../3d_interpolation/tf_interpolate.cpp" | 267 ++++++++++++++++++ .../3d_interpolation/tf_interpolate.py" | 59 ++++ .../tf_interpolate_compile.sh" | 7 + .../tf_interpolate_op_test.py" | 24 ++ .../3d_interpolation/tf_interpolate_so.so" | Bin 0 -> 52296 bytes .../3d_interpolation/visu_interpolation.py" | 44 +++ 7 files changed, 570 insertions(+) create mode 100644 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"code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/visu_interpolation.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" new file mode 100644 index 0000000..b7d0dd0 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/interpolate.cpp" @@ -0,0 +1,169 @@ +#include +#include +#include // memset +#include // rand, RAND_MAX +#include // sqrtf +#include +#include +using namespace std; +float randomf(){ + return (rand()+0.5)/(RAND_MAX+1.0); +} +static double get_time(){ + timespec tp; + clock_gettime(CLOCK_MONOTONIC,&tp); + return tp.tv_sec+tp.tv_nsec*1e-9; +} + +// Find three nearest neigbors with square distance +// input: xyz1 (b,n,3), xyz2(b,m,3) +// output: dist (b,n,3), idx (b,n,3) +void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { + for (int i=0;i +#include +#include // memset +#include // rand, RAND_MAX +#include // sqrtf +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/framework/common_shape_fns.h" + +#pragma GCC diagnostic ignored "-Wunused-result" + +using namespace tensorflow; + +REGISTER_OP("ThreeNN") + .Input("xyz1: float32") + .Input("xyz2: float32") + .Output("dist: float32") + .Output("idx: int32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + c->set_output(1, c->input(0)); + return Status::OK(); + }); +REGISTER_OP("ThreeInterpolate") + .Input("points: float32") + .Input("idx: int32") + .Input("weight: float32") + .Output("out: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle dims1; // (b,m,c) + c->WithRank(c->input(0), 3, &dims1); + ::tensorflow::shape_inference::ShapeHandle dims2; // (b,n,3) + c->WithRank(c->input(1), 3, &dims2); + // (b,n,c) + ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)}); + c->set_output(0, output); + return Status::OK(); + }); +REGISTER_OP("ThreeInterpolateGrad") + .Input("points: float32") + .Input("idx: int32") + .Input("weight: float32") + .Input("grad_out: float32") + .Output("grad_points: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + return Status::OK(); + }); + +float randomf(){ + return (rand()+0.5)/(RAND_MAX+1.0); +} +static double get_time(){ + timespec tp; + clock_gettime(CLOCK_MONOTONIC,&tp); + return tp.tv_sec+tp.tv_nsec*1e-9; +} + +// Find three nearest neigbors with square distance +// input: xyz1 (b,n,3), xyz2(b,m,3) +// output: dist (b,n,3), idx (b,n,3) +void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { + for (int i=0;iinput(0); + OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,n,3) xyz1 shape.")); + int b = xyz1_tensor.shape().dim_size(0); + int n = xyz1_tensor.shape().dim_size(1); + + const Tensor& xyz2_tensor = context->input(1); + OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,m,3) xyz2 shape.")); + int m = xyz2_tensor.shape().dim_size(1); + + Tensor *dist_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,n,3}, &dist_tensor)); + Tensor *idx_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,n,3}, &idx_tensor)); + + auto xyz1_flat = xyz1_tensor.flat(); + const float *xyz1 = &(xyz1_flat(0)); + auto xyz2_flat = xyz2_tensor.flat(); + const float *xyz2 = &(xyz2_flat(0)); + auto dist_flat = dist_tensor->flat(); + float *dist = &(dist_flat(0)); + auto idx_flat = idx_tensor->flat(); + int *idx = &(idx_flat(0)); + threenn_cpu(b,n,m,xyz1,xyz2,dist,idx); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreeNN").Device(DEVICE_CPU), ThreeNNOp); + + + +class ThreeInterpolateOp: public OpKernel{ + public: + explicit ThreeInterpolateOp(OpKernelConstruction * context):OpKernel(context){} + + void Compute(OpKernelContext * context) override { + const Tensor& points_tensor=context->input(0); + OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolate expects (b,m,c) points shape")); + int b = points_tensor.shape().dim_size(0); + int m = points_tensor.shape().dim_size(1); + int c = points_tensor.shape().dim_size(2); + + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b && idx_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) idx shape")); + int n = idx_tensor.shape().dim_size(1); + const Tensor& weight_tensor=context->input(2); + OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) weight shape")); + + Tensor * out_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &out_tensor)); + + auto points_flat = points_tensor.flat(); + const float *points = &(points_flat(0)); + auto idx_flat = idx_tensor.flat(); + const int *idx = &(idx_flat(0)); + auto weight_flat = weight_tensor.flat(); + const float *weight = &(weight_flat(0)); + auto out_flat = out_tensor->flat(); + float *out = &(out_flat(0)); + threeinterpolate_cpu(b,m,c,n,points,idx,weight,out); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreeInterpolate").Device(DEVICE_CPU),ThreeInterpolateOp); + + +class ThreeInterpolateGradOp: public OpKernel{ + public: + explicit ThreeInterpolateGradOp(OpKernelConstruction * context):OpKernel(context){} + + void Compute(OpKernelContext * context) override { + const Tensor& points_tensor=context->input(0); + OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,m,c) points shape")); + int b = points_tensor.shape().dim_size(0); + int m = points_tensor.shape().dim_size(1); + int c = points_tensor.shape().dim_size(2); + + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) idx shape")); + int n = idx_tensor.shape().dim_size(1); + const Tensor& weight_tensor=context->input(2); + OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) weight shape")); + + const Tensor& grad_out_tensor=context->input(3); + OP_REQUIRES(context,grad_out_tensor.dims()==3 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==n && grad_out_tensor.shape().dim_size(2)==c, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,c) grad_out shape")); + + Tensor * grad_points_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,c}, &grad_points_tensor)); + + auto points_flat = points_tensor.flat(); + const float *points = &(points_flat(0)); + auto idx_flat = idx_tensor.flat(); + const int *idx = &(idx_flat(0)); + auto weight_flat = weight_tensor.flat(); + const float *weight = &(weight_flat(0)); + auto grad_out_flat = grad_out_tensor.flat(); + const float *grad_out = &(grad_out_flat(0)); + auto grad_points_flat = grad_points_tensor->flat(); + float *grad_points = &(grad_points_flat(0)); + memset(grad_points, 0, sizeof(float)*b*m*c); + threeinterpolate_grad_cpu(b,n,c,m,grad_out,idx,weight,grad_points); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreeInterpolateGrad").Device(DEVICE_CPU),ThreeInterpolateGradOp); + + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate.py" new file mode 100644 index 0000000..2ef1edd --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate.py" @@ -0,0 +1,59 @@ +import tensorflow as tf +from tensorflow.python.framework import ops +import sys +import os +BASE_DIR = os.path.dirname(__file__) +sys.path.append(BASE_DIR) +interpolate_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_interpolate_so.so')) +def three_nn(xyz1, xyz2): + ''' + Input: + xyz1: (b,n,3) float32 array, unknown points + xyz2: (b,m,3) float32 array, known points + Output: + dist: (b,n,3) float32 array, distances to known points + idx: (b,n,3) int32 array, indices to known points + ''' + return interpolate_module.three_nn(xyz1, xyz2) +ops.NoGradient('ThreeNN') +def three_interpolate(points, idx, weight): + ''' + Input: + points: (b,m,c) float32 array, known points + idx: (b,n,3) int32 array, indices to known points + weight: (b,n,3) float32 array, weights on known points + Output: + out: (b,n,c) float32 array, interpolated point values + ''' + return interpolate_module.three_interpolate(points, idx, weight) +@tf.RegisterGradient('ThreeInterpolate') +def _three_interpolate_grad(op, grad_out): + points = op.inputs[0] + idx = op.inputs[1] + weight = op.inputs[2] + return [interpolate_module.three_interpolate_grad(points, idx, weight, grad_out), None, None] + +if __name__=='__main__': + import numpy as np + import time + np.random.seed(100) + pts = np.random.random((32,128,64)).astype('float32') + tmp1 = np.random.random((32,512,3)).astype('float32') + tmp2 = np.random.random((32,128,3)).astype('float32') + with tf.device('/cpu:0'): + points = tf.constant(pts) + xyz1 = tf.constant(tmp1) + xyz2 = tf.constant(tmp2) + dist, idx = three_nn(xyz1, xyz2) + weight = tf.ones_like(dist)/3.0 + interpolated_points = three_interpolate(points, idx, weight) + with tf.Session('') as sess: + now = time.time() + for _ in range(100): + ret = sess.run(interpolated_points) + print (time.time() - now) + print (ret.shape, ret.dtype) + #print ret + + + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate_compile.sh" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate_compile.sh" new file mode 100644 index 0000000..8af3cf7 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate_compile.sh" @@ -0,0 +1,7 @@ +# TF1.2 +#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +# TF1.4 +#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include -I /usr/local/cuda-9.0/include -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-9.0/lib64/ -L/home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow -l:libtensorflow_framework.so -O2 -D_GLIBCXX_USE_CXX11_ABI=0 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate_op_test.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate_op_test.py" new file mode 100644 index 0000000..030456d --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate_op_test.py" @@ -0,0 +1,24 @@ +import tensorflow as tf +import numpy as np +from tf_interpolate import three_nn, three_interpolate + +class GroupPointTest(tf.test.TestCase): + def test(self): + pass + + def test_grad(self): + with self.test_session(): + points = tf.constant(np.random.random((1,8,16)).astype('float32')) + print (points) + xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32')) + xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32')) + dist, idx = three_nn(xyz1, xyz2) + weight = tf.ones_like(dist)/3.0 + interpolated_points = three_interpolate(points, idx, weight) + print(interpolated_points) + err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16)) + print (err) + self.assertLess(err, 1e-4) + +if __name__=='__main__': + tf.test.main() diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate_so.so" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/tf_interpolate_so.so" new file mode 100644 index 0000000000000000000000000000000000000000..66d67ca3c5d2c52602144e24fc4a95565690b9be GIT binary patch literal 52296 zcmeIb3wTu3)jxc4BM~tZ6>PkprM5;vpuW~prM_sbp!IUbU_n%*YRUh%_TFb^&&(OX zzW?w0p67c!I_$H4XYIAuUVH7eFXxh*E%v6|oE$|v@|3F-qV`SaNS(N_bDKCpQm51? 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"a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/visu_interpolation.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/visu_interpolation.py" new file mode 100644 index 0000000..bc82820 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/3d_interpolation/visu_interpolation.py" @@ -0,0 +1,44 @@ +''' Visualize part segmentation ''' +import os +import sys +ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.append('/home/rqi/Projects/toolkits/visualization') +from show3d_balls import showpoints +import numpy as np +from tf_interpolate import three_nn, three_interpolate +import tensorflow as tf + + +pts2 = np.array([[0,0,1],[1,0,0],[0,1,0],[1,1,0]]).astype('float32') +xyz1 = np.random.random((100,3)).astype('float32') +xyz2 = np.array([[0,0,0],[1,0,0],[0,1,0],[1,1,1]]).astype('float32') + +def fun(xyz1,xyz2,pts2): + with tf.device('/cpu:0'): + points = tf.constant(np.expand_dims(pts2,0)) + xyz1 = tf.constant(np.expand_dims(xyz1,0)) + xyz2 = tf.constant(np.expand_dims(xyz2,0)) + dist, idx = three_nn(xyz1, xyz2) + #weight = tf.ones_like(dist)/3.0 + dist = tf.maximum(dist, 1e-10) + norm = tf.reduce_sum((1.0/dist),axis=2,keep_dims=True) + norm = tf.tile(norm, [1,1,3]) + print (norm) + weight = (1.0/dist) / norm + interpolated_points = three_interpolate(points, idx, weight) + with tf.Session('') as sess: + tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight]) + #print w + pts1 = pts1.squeeze() + return pts1 + +pts1 = fun(xyz1,xyz2,pts2) +all_pts = np.zeros((104,3)) +all_pts[0:100,:] = pts1 +all_pts[100:,:] = pts2 +all_xyz = np.zeros((104,3)) +all_xyz[0:100,:]=xyz1 +all_xyz[100:,:]=xyz2 +showpoints(xyz2, pts2, ballradius=8) +showpoints(xyz1, pts1, ballradius=8) +showpoints(all_xyz, all_pts, ballradius=8) -- Gitee From 3bdc6ac2f1b0ff59070e0479a544ad74412fe1a6 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:32:52 +0000 Subject: [PATCH 55/69] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20grouping?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tf_ops/grouping/.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 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<11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:33:34 +0000 Subject: [PATCH 56/69] =?UTF-8?q?=E5=88=86=E7=BB=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../tf_ops/grouping/.gitignore" | 10 + .../tf_ops/grouping/tf_grouping.cpp" | 213 ++++++++++++++++++ .../tf_ops/grouping/tf_grouping.py" | 105 +++++++++ .../tf_ops/grouping/tf_grouping_compile.sh" | 11 + .../tf_ops/grouping/tf_grouping_g.cu" | 141 ++++++++++++ .../tf_ops/grouping/tf_grouping_g.cu.o" | Bin 0 -> 29608 bytes .../tf_ops/grouping/tf_grouping_op_test.py" | 28 +++ .../tf_ops/grouping/tf_grouping_so.so" | Bin 0 -> 84080 bytes 8 files changed, 508 insertions(+) create mode 100644 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"b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/.gitignore" new file mode 100644 index 0000000..2f08276 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/.gitignore" @@ -0,0 +1,10 @@ +a.out +query_ball_point +query_ball_point_block +query_ball_point_cuda +query_ball_point_grid +tf_grouping_g.cu.o +tf_grouping_so.so +selection_sort +selection_sort_cuda +selection_sort_const_cuda diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping.cpp" new file mode 100644 index 0000000..924be5f --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping.cpp" @@ -0,0 +1,213 @@ +#include +#include +#include // memset +#include // rand, RAND_MAX +#include // sqrtf +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/framework/common_shape_fns.h" +#include + +#pragma GCC diagnostic ignored "-Wunused-result" + +using namespace tensorflow; + +REGISTER_OP("QueryBallPoint") + .Attr("radius: float") + .Attr("nsample: int") + .Input("xyz1: float32") + .Input("xyz2: float32") + .Output("idx: int32") + .Output("pts_cnt: int32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoint * 3 + c->WithRank(c->input(1), 3, &dims2); + int nsample; + TF_RETURN_IF_ERROR(c->GetAttr("nsample", &nsample)); + ::tensorflow::shape_inference::ShapeHandle output1 = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1), nsample}); + c->set_output(0, output1); + ::tensorflow::shape_inference::ShapeHandle output2 = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1)}); + c->set_output(1, output2); + return Status::OK(); + }); +REGISTER_OP("SelectionSort") + .Attr("k: int") + .Input("dist: float32") + .Output("outi: int32") + .Output("out: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + c->set_output(1, c->input(0)); + return Status::OK(); + }); +REGISTER_OP("GroupPoint") + .Input("points: float32") + .Input("idx: int32") + .Output("out: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ndataset * channels + c->WithRank(c->input(0), 3, &dims1); + ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints * nsample + c->WithRank(c->input(1), 3, &dims2); + // batch_size * npoints * nsample * channels + ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1), c->Dim(dims2, 2), c->Dim(dims1, 2)}); + c->set_output(0, output); + return Status::OK(); + }); +REGISTER_OP("GroupPointGrad") + .Input("points: float32") + .Input("idx: int32") + .Input("grad_out: float32") + .Output("grad_points: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + return Status::OK(); + }); + + +void queryBallPointLauncher(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx, int *pts_cnt); +class QueryBallPointGpuOp : public OpKernel { + public: + explicit QueryBallPointGpuOp(OpKernelConstruction* context) : OpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("radius", &radius_)); + OP_REQUIRES(context, radius_ > 0, errors::InvalidArgument("QueryBallPoint expects positive radius")); + + OP_REQUIRES_OK(context, context->GetAttr("nsample", &nsample_)); + OP_REQUIRES(context, nsample_ > 0, errors::InvalidArgument("QueryBallPoint expects positive nsample")); + } + + void Compute(OpKernelContext* context) override { + const Tensor& xyz1_tensor = context->input(0); + OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("QueryBallPoint expects (batch_size, ndataset, 3) xyz1 shape.")); + int b = xyz1_tensor.shape().dim_size(0); + int n = xyz1_tensor.shape().dim_size(1); + + const Tensor& xyz2_tensor = context->input(1); + OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("QueryBallPoint expects (batch_size, npoint, 3) xyz2 shape.")); + int m = xyz2_tensor.shape().dim_size(1); + + Tensor *idx_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,m,nsample_}, &idx_tensor)); + Tensor *pts_cnt_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,m}, &pts_cnt_tensor)); + + auto xyz1_flat = xyz1_tensor.flat(); + const float *xyz1 = &(xyz1_flat(0)); + auto xyz2_flat = xyz2_tensor.flat(); + const float *xyz2 = &(xyz2_flat(0)); + auto idx_flat = idx_tensor->flat(); + int *idx = &(idx_flat(0)); + auto pts_cnt_flat = pts_cnt_tensor->flat(); + int *pts_cnt = &(pts_cnt_flat(0)); + queryBallPointLauncher(b,n,m,radius_,nsample_,xyz1,xyz2,idx,pts_cnt); + } + private: + float radius_; + int nsample_; +}; +REGISTER_KERNEL_BUILDER(Name("QueryBallPoint").Device(DEVICE_GPU), QueryBallPointGpuOp); + +void selectionSortLauncher(int b, int n, int m, int k, const float *dist, int *outi, float *out); +class SelectionSortGpuOp : public OpKernel { + public: + explicit SelectionSortGpuOp(OpKernelConstruction* context) : OpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("k", &k_)); + OP_REQUIRES(context, k_ > 0, errors::InvalidArgument("SelectionSort expects positive k")); + } + + void Compute(OpKernelContext* context) override { + const Tensor& dist_tensor = context->input(0); + OP_REQUIRES(context, dist_tensor.dims()==3, errors::InvalidArgument("SelectionSort expects (b,m,n) dist shape.")); + int b = dist_tensor.shape().dim_size(0); + int m = dist_tensor.shape().dim_size(1); + int n = dist_tensor.shape().dim_size(2); + + Tensor *outi_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,m,n}, &outi_tensor)); + Tensor *out_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,m,n}, &out_tensor)); + + auto dist_flat = dist_tensor.flat(); + const float *dist = &(dist_flat(0)); + auto outi_flat = outi_tensor->flat(); + int *outi = &(outi_flat(0)); + auto out_flat = out_tensor->flat(); + float *out = &(out_flat(0)); + selectionSortLauncher(b,n,m,k_,dist,outi,out); + } + private: + int k_; +}; +REGISTER_KERNEL_BUILDER(Name("SelectionSort").Device(DEVICE_GPU), SelectionSortGpuOp); + + +void groupPointLauncher(int b, int n, int c, int m, int nsample, const float *points, const int *idx, float *out); +class GroupPointGpuOp: public OpKernel{ + public: + explicit GroupPointGpuOp(OpKernelConstruction * context):OpKernel(context){} + + void Compute(OpKernelContext * context) override { + const Tensor& points_tensor=context->input(0); + OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("GroupPoint expects (batch_size, num_points, channel) points shape")); + int b = points_tensor.shape().dim_size(0); + int n = points_tensor.shape().dim_size(1); + int c = points_tensor.shape().dim_size(2); + + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("GroupPoint expects (batch_size, npoints, nsample) idx shape")); + int m = idx_tensor.shape().dim_size(1); + int nsample = idx_tensor.shape().dim_size(2); + + Tensor * out_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,nsample,c}, &out_tensor)); + + auto points_flat = points_tensor.flat(); + const float *points = &(points_flat(0)); + auto idx_flat = idx_tensor.flat(); + const int *idx = &(idx_flat(0)); + auto out_flat = out_tensor->flat(); + float *out = &(out_flat(0)); + groupPointLauncher(b,n,c,m,nsample,points,idx,out); + } +}; +REGISTER_KERNEL_BUILDER(Name("GroupPoint").Device(DEVICE_GPU),GroupPointGpuOp); + +void groupPointGradLauncher(int b, int n, int c, int m, int nsample, const float *grad_out, const int *idx, float *grad_points); +class GroupPointGradGpuOp: public OpKernel{ + public: + explicit GroupPointGradGpuOp(OpKernelConstruction * context):OpKernel(context){} + + void Compute(OpKernelContext * context) override { + const Tensor& points_tensor=context->input(0); + OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("GroupPointGrad expects (batch_size, num_points, channel) points shape")); + int b = points_tensor.shape().dim_size(0); + int n = points_tensor.shape().dim_size(1); + int c = points_tensor.shape().dim_size(2); + + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("GroupPointGrad expects (batch_size, npoints, nsample) idx shape")); + int m = idx_tensor.shape().dim_size(1); + int nsample = idx_tensor.shape().dim_size(2); + + const Tensor& grad_out_tensor=context->input(2); + OP_REQUIRES(context,grad_out_tensor.dims()==4 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==m && grad_out_tensor.shape().dim_size(2)==nsample && grad_out_tensor.shape().dim_size(3)==c, errors::InvalidArgument("GroupPointGrad expects (batch_size, npoints, nsample, channel) grad_out shape")); + + Tensor * grad_points_tensor = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &grad_points_tensor)); + + auto points_flat = points_tensor.flat(); + const float *points = &(points_flat(0)); + auto idx_flat = idx_tensor.flat(); + const int *idx = &(idx_flat(0)); + auto grad_out_flat = grad_out_tensor.flat(); + const float *grad_out = &(grad_out_flat(0)); + auto grad_points_flat = grad_points_tensor->flat(); + float *grad_points = &(grad_points_flat(0)); + cudaMemset(grad_points, 0, sizeof(float)*b*n*c); + groupPointGradLauncher(b,n,c,m,nsample,grad_out,idx,grad_points); + } +}; +REGISTER_KERNEL_BUILDER(Name("GroupPointGrad").Device(DEVICE_GPU),GroupPointGradGpuOp); + + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping.py" new file mode 100644 index 0000000..45c96f5 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping.py" @@ -0,0 +1,105 @@ +import tensorflow as tf +from tensorflow.python.framework import ops +import sys +import os +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) +grouping_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_grouping_so.so')) +def query_ball_point(radius, nsample, xyz1, xyz2): + ''' + Input: + radius: float32, ball search radius + nsample: int32, number of points selected in each ball region + xyz1: (batch_size, ndataset, 3) float32 array, input points + xyz2: (batch_size, npoint, 3) float32 array, query points + Output: + idx: (batch_size, npoint, nsample) int32 array, indices to input points + pts_cnt: (batch_size, npoint) int32 array, number of unique points in each local region + ''' + #return grouping_module.query_ball_point(radius, nsample, xyz1, xyz2) + return grouping_module.query_ball_point(xyz1, xyz2, radius, nsample) +ops.NoGradient('QueryBallPoint') +def select_top_k(k, dist): + ''' + Input: + k: int32, number of k SMALLEST elements selected + dist: (b,m,n) float32 array, distance matrix, m query points, n dataset points + Output: + idx: (b,m,n) int32 array, first k in n are indices to the top k + dist_out: (b,m,n) float32 array, first k in n are the top k + ''' + return grouping_module.selection_sort(dist, k) +ops.NoGradient('SelectionSort') +def group_point(points, idx): + ''' + Input: + points: (batch_size, ndataset, channel) float32 array, points to sample from + idx: (batch_size, npoint, nsample) int32 array, indices to points + Output: + out: (batch_size, npoint, nsample, channel) float32 array, values sampled from points + ''' + return grouping_module.group_point(points, idx) +@tf.RegisterGradient('GroupPoint') +def _group_point_grad(op, grad_out): + points = op.inputs[0] + idx = op.inputs[1] + return [grouping_module.group_point_grad(points, idx, grad_out), None] + +def knn_point(k, xyz1, xyz2): + ''' + Input: + k: int32, number of k in k-nn search + xyz1: (batch_size, ndataset, c) float32 array, input points + xyz2: (batch_size, npoint, c) float32 array, query points + Output: + val: (batch_size, npoint, k) float32 array, L2 distances + idx: (batch_size, npoint, k) int32 array, indices to input points + ''' + b = xyz1.get_shape()[0].value + n = xyz1.get_shape()[1].value + c = xyz1.get_shape()[2].value + m = xyz2.get_shape()[1].value + print (b, n, c, m) + print (xyz1, (b,1,n,c)) + xyz1 = tf.tile(tf.reshape(xyz1, (b,1,n,c)), [1,m,1,1]) + xyz2 = tf.tile(tf.reshape(xyz2, (b,m,1,c)), [1,1,n,1]) + dist = tf.reduce_sum((xyz1-xyz2)**2, -1) + print (dist, k) + outi, out = select_top_k(k, dist) + idx = tf.slice(outi, [0,0,0], [-1,-1,k]) + val = tf.slice(out, [0,0,0], [-1,-1,k]) + print (idx, val) + #val, idx = tf.nn.top_k(-dist, k=k) # ONLY SUPPORT CPU + return val, idx + +if __name__=='__main__': + knn=True + import numpy as np + import time + np.random.seed(100) + pts = np.random.random((32,512,64)).astype('float32') + tmp1 = np.random.random((32,512,3)).astype('float32') + tmp2 = np.random.random((32,128,3)).astype('float32') + with tf.device('/gpu:1'): + points = tf.constant(pts) + xyz1 = tf.constant(tmp1) + xyz2 = tf.constant(tmp2) + radius = 0.1 + nsample = 64 + if knn: + _, idx = knn_point(nsample, xyz1, xyz2) + grouped_points = group_point(points, idx) + else: + idx, _ = query_ball_point(radius, nsample, xyz1, xyz2) + grouped_points = group_point(points, idx) + #grouped_points_grad = tf.ones_like(grouped_points) + #points_grad = tf.gradients(grouped_points, points, grouped_points_grad) + with tf.Session('') as sess: + now = time.time() + for _ in range(100): + ret = sess.run(grouped_points) + print (time.time() - now) + print (ret.shape, ret.dtype) + print (ret) + + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_compile.sh" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_compile.sh" new file mode 100644 index 0000000..7bd30ad --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_compile.sh" @@ -0,0 +1,11 @@ +#/bin/bash10 +/usr/local/cuda-9.0/bin/nvcc tf_grouping_g.cu -o tf_grouping_g.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC + +# TF1.2 +#g++ -std=c++11 tf_grouping.cpp tf_grouping_g.cu.o -o tf_grouping_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +# TF1.4 +#g++ -std=c++11 tf_grouping.cpp tf_grouping_g.cu.o -o tf_grouping_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-11.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + + +g++ -std=c++11 tf_grouping.cpp tf_grouping_g.cu.o -o tf_grouping_so.so -shared -fPIC -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include -I /usr/local/cuda-9.0/include -I /home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-9.0/lib64/ -L/home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow -l:libtensorflow_framework.so -O2 -D_GLIBCXX_USE_CXX11_ABI=0 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_g.cu" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_g.cu" new file mode 100644 index 0000000..578330d --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_g.cu" @@ -0,0 +1,141 @@ +// input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3) +// output: idx (b,m,nsample), pts_cnt (b,m) +__global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx, int *pts_cnt) { + int batch_index = blockIdx.x; + xyz1 += n*3*batch_index; + xyz2 += m*3*batch_index; + idx += m*nsample*batch_index; + pts_cnt += m*batch_index; // counting how many unique points selected in local region + + int index = threadIdx.x; + int stride = blockDim.x; + + for (int j=index;j>>(b,n,m,radius,nsample,xyz1,xyz2,idx,pts_cnt); + //cudaDeviceSynchronize(); +} +void selectionSortLauncher(int b, int n, int m, int k, const float *dist, int *outi, float *out) { + selection_sort_gpu<<>>(b,n,m,k,dist,outi,out); + //cudaDeviceSynchronize(); +} +void groupPointLauncher(int b, int n, int c, int m, int nsample, const float *points, const int *idx, float *out){ + group_point_gpu<<>>(b,n,c,m,nsample,points,idx,out); + //cudaDeviceSynchronize(); +} +void groupPointGradLauncher(int b, int n, int c, int m, int nsample, const float *grad_out, const int *idx, float *grad_points){ + group_point_grad_gpu<<>>(b,n,c,m,nsample,grad_out,idx,grad_points); + //group_point_grad_gpu<<<1,1>>>(b,n,c,m,nsample,grad_out,idx,grad_points); + //cudaDeviceSynchronize(); +} diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_g.cu.o" 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z1)|SV6YIcJqPbGG{8s{rU}_e!LH}SD+>qbk)2`EJ_wRhKMwrT?&$b_-&1JtM@7D;IhIzkv@Fo{YMY-w5bj{rh%l_(ng}za1Zg&VU~Pj6}@t z|JQo|1|Cp&GU$!^hftWS|M(pJ=KgKlzS9Z;YRsv$_C;u@~BS z#%!cqBwFKX?~qMT`;Tnf0mRO9(l=wD%xcUYe*ti^P&nPg+k;nHSB@s literal 0 HcmV?d00001 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_op_test.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_op_test.py" new file mode 100644 index 0000000..939eb1f --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_op_test.py" @@ -0,0 +1,28 @@ +import tensorflow as tf +import numpy as np +from tf_grouping import query_ball_point, group_point + +class GroupPointTest(tf.test.TestCase): + def test(self): + pass + + def test_grad(self): + with tf.device('/gpu:0'): + points = tf.constant(np.random.random((1,128,16)).astype('float32')) + print points + xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32')) + xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32')) + radius = 0.3 + nsample = 32 + idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2) + grouped_points = group_point(points, idx) + print (grouped_points) + + with self.test_session(): + print ("---- Going to compute gradient error") + err = tf.test.compute_gradient_error(points, (1,128,16), grouped_points, (1,8,32,16)) + print (err) + self.assertLess(err, 1e-4) + +if __name__=='__main__': + tf.test.main() diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_so.so" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/grouping/tf_grouping_so.so" new file mode 100644 index 0000000000000000000000000000000000000000..c10efbe728472286c4855488f0c42ffc81f88ff3 GIT binary patch literal 84080 zcmeFa4S1Br)i*x7n~;D=76c6#$V~_uG=|-L5kPd45ZGwI2&g<7OhU3iG$bL}@S&)Y zWR-P|MvK}FTu^Stl- 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"a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling.cpp" new file mode 100644 index 0000000..d92d3a7 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling.cpp" @@ -0,0 +1,182 @@ +/* Furthest point sampling + * Original author: Haoqiang Fan + * Modified by Charles R. Qi + * All Rights Reserved. 2017. + */ +#pragma GCC diagnostic ignored "-Wunused-result" + +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/framework/common_shape_fns.h" +#include + +using namespace tensorflow; + +REGISTER_OP("ProbSample") + .Input("inp: float32") + .Input("inpr: float32") + .Output("out: int32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ncategory + c->WithRank(c->input(0), 2, &dims1); + ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints + c->WithRank(c->input(1), 2, &dims2); + // batch_size * npoints + ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1)}); + c->set_output(0, output); + return Status::OK(); + }); +REGISTER_OP("FarthestPointSample") + .Attr("npoint: int") + .Input("inp: float32") + .Output("out: int32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * npoint * 3 + c->WithRank(c->input(0), 3, &dims1); + int npoint; + TF_RETURN_IF_ERROR(c->GetAttr("npoint", &npoint)); + ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), npoint}); + c->set_output(0, output); + return Status::OK(); + }); +REGISTER_OP("GatherPoint") + .Input("inp: float32") + .Input("idx: int32") + .Output("out: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ndataset * 3 + c->WithRank(c->input(0), 3, &dims1); + ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints + + c->WithRank(c->input(1), 2, &dims2); + // batch_size * npoints * 3 + ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)}); + c->set_output(0, output); + return Status::OK(); + }); +REGISTER_OP("GatherPointGrad") + .Input("inp: float32") + .Input("idx: int32") + .Input("out_g: float32") + .Output("inp_g: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + return Status::OK(); + }); + +void probsampleLauncher(int b,int n,int m,const float * inp_p,const float * inp_r,float * temp,int * out); +class ProbSampleGpuOp: public OpKernel{ + public: + explicit ProbSampleGpuOp(OpKernelConstruction* context):OpKernel(context){} + void Compute(OpKernelContext * context)override{ + const Tensor& inp_tensor=context->input(0); + const Tensor& inpr_tensor=context->input(1); + auto inp_flat=inp_tensor.flat(); + auto inpr_flat=inpr_tensor.flat(); + const float * inp=&(inp_flat(0)); + const float * inpr=&(inpr_flat(0)); + OP_REQUIRES(context,inp_tensor.dims()==2,errors::InvalidArgument("ProbSample expects (batch_size,num_choices) inp shape")); + int b=inp_tensor.shape().dim_size(0); + int n=inp_tensor.shape().dim_size(1); + OP_REQUIRES(context,inpr_tensor.dims()==2 && inpr_tensor.shape().dim_size(0)==b,errors::InvalidArgument("ProbSample expects (batch_size,num_points) inpr shape")); + int m=inpr_tensor.shape().dim_size(1); + Tensor * out_tensor=NULL; + OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m},&out_tensor)); + auto out_flat=out_tensor->flat(); + int * out=&(out_flat(0)); + Tensor temp_tensor; + OP_REQUIRES_OK(context,context->allocate_temp(DataTypeToEnum::value,TensorShape{b,n},&temp_tensor)); + auto temp_flat=temp_tensor.flat(); + float * temp=&(temp_flat(0)); + probsampleLauncher(b,n,m,inp,inpr,temp,out); + } +}; +REGISTER_KERNEL_BUILDER(Name("ProbSample").Device(DEVICE_GPU), ProbSampleGpuOp); + +void farthestpointsamplingLauncher(int b,int n,int m,const float * inp,float * temp,int * out); +class FarthestPointSampleGpuOp: public OpKernel{ + public: + explicit FarthestPointSampleGpuOp(OpKernelConstruction* context):OpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("npoint", &npoint_)); + OP_REQUIRES(context, npoint_ > 0, errors::InvalidArgument("FarthestPointSample expects positive npoint")); + } + void Compute(OpKernelContext * context)override{ + int m = npoint_; + + const Tensor& inp_tensor=context->input(0); + OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument("FarthestPointSample expects (batch_size,num_points,3) inp shape")); + int b=inp_tensor.shape().dim_size(0); + int n=inp_tensor.shape().dim_size(1); + auto inp_flat=inp_tensor.flat(); + const float * inp=&(inp_flat(0)); + Tensor * out_tensor; + OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m},&out_tensor)); + auto out_flat=out_tensor->flat(); + int * out=&(out_flat(0)); + Tensor temp_tensor; + OP_REQUIRES_OK(context,context->allocate_temp(DataTypeToEnum::value,TensorShape{32,n},&temp_tensor)); + auto temp_flat=temp_tensor.flat(); + float * temp=&(temp_flat(0)); + farthestpointsamplingLauncher(b,n,m,inp,temp,out); + } + private: + int npoint_; +}; +REGISTER_KERNEL_BUILDER(Name("FarthestPointSample").Device(DEVICE_GPU),FarthestPointSampleGpuOp); + +void gatherpointLauncher(int b,int n,int m,const float * inp,const int * idx,float * out); +class GatherPointGpuOp: public OpKernel{ + public: + explicit GatherPointGpuOp(OpKernelConstruction * context):OpKernel(context){} + void Compute(OpKernelContext * context)override{ + const Tensor& inp_tensor=context->input(0); + OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument("GatherPoint expects (batch_size,num_points,3) inp shape")); + int b=inp_tensor.shape().dim_size(0); + int n=inp_tensor.shape().dim_size(1); + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==2 && idx_tensor.shape().dim_size(0)==b,errors::InvalidArgument("GatherPoint expects (batch_size,num_result) idx shape")); + int m=idx_tensor.shape().dim_size(1); + auto inp_flat=inp_tensor.flat(); + const float * inp=&(inp_flat(0)); + auto idx_flat=idx_tensor.flat(); + const int * idx=&(idx_flat(0)); + Tensor * out_tensor=NULL; + OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m,3},&out_tensor)); + auto out_flat=out_tensor->flat(); + float * out=&(out_flat(0)); + gatherpointLauncher(b,n,m,inp,idx,out); + } +}; +REGISTER_KERNEL_BUILDER(Name("GatherPoint").Device(DEVICE_GPU),GatherPointGpuOp); + +void scatteraddpointLauncher(int b,int n,int m,const float * out_g,const int * idx,float * inp_g); +class GatherPointGradGpuOp: public OpKernel{ + public: + explicit GatherPointGradGpuOp(OpKernelConstruction * context):OpKernel(context){} + void Compute(OpKernelContext * context)override{ + const Tensor& inp_tensor=context->input(0); + OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument("GatherPointGradGpuOp expects (batch_size,num_points,3) inp")); + int b=inp_tensor.shape().dim_size(0); + int n=inp_tensor.shape().dim_size(1); + const Tensor& idx_tensor=context->input(1); + OP_REQUIRES(context,idx_tensor.dims()==2 && idx_tensor.shape().dim_size(0)==b,errors::InvalidArgument("GatherPointGradGpuOp expects (batch_size,num_result) idx shape")); + int m=idx_tensor.shape().dim_size(1); + auto inp_flat=inp_tensor.flat(); + const float * inp=&(inp_flat(0)); + auto idx_flat=idx_tensor.flat(); + const int * idx=&(idx_flat(0)); + const Tensor& out_g_tensor=context->input(2); + OP_REQUIRES(context,out_g_tensor.dims()==3 && out_g_tensor.shape().dim_size(0)==b && out_g_tensor.shape().dim_size(1)==m && out_g_tensor.shape().dim_size(2)==3,errors::InvalidArgument("GatherPointGradGpuOp expects (batch_size,num_result,3) out_g shape")); + auto out_g_flat=out_g_tensor.flat(); + const float * out_g=&(out_g_flat(0)); + Tensor * inp_g_tensor=NULL; + OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,n,3},&inp_g_tensor)); + auto inp_g_flat=inp_g_tensor->flat(); + float * inp_g=&(inp_g_flat(0)); + cudaMemset(inp_g,0,b*n*3*4); + scatteraddpointLauncher(b,n,m,out_g,idx,inp_g); + } +}; +REGISTER_KERNEL_BUILDER(Name("GatherPointGrad").Device(DEVICE_GPU),GatherPointGradGpuOp); + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling.py" new file mode 100644 index 0000000..efa8428 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling.py" @@ -0,0 +1,91 @@ +''' Furthest point sampling +Original author: Haoqiang Fan +Modified by Charles R. Qi +All Rights Reserved. 2017. +''' +import tensorflow as tf +from tensorflow.python.framework import ops +import sys +import os +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) +sampling_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_sampling_so.so')) +def prob_sample(inp,inpr): + ''' +input: + batch_size * ncategory float32 + batch_size * npoints float32 +returns: + batch_size * npoints int32 + ''' + return sampling_module.prob_sample(inp,inpr) + +ops.NoGradient('ProbSample') +# TF1.0 API requires set shape in C++ +# @tf.RegisterShape('ProbSample') +# def _prob_sample_shape(op): +# shape1=op.inputs[0].get_shape().with_rank(2) +# shape2=op.inputs[1].get_shape().with_rank(2) +# return [tf.TensorShape([shape2.dims[0],shape2.dims[1]])] +def gather_point(inp,idx): + ''' +input: + batch_size * ndataset * 3 float32 + batch_size * npoints int32 +returns: + batch_size * npoints * 3 float32 + ''' + return sampling_module.gather_point(inp,idx) +#@tf.RegisterShape('GatherPoint') +# def _gather_point_shape(op): +# shape1=op.inputs[0].get_shape().with_rank(3) +# shape2=op.inputs[1].get_shape().with_rank(2) +# return [tf.TensorShape([shape1.dims[0],shape2.dims[1],shape1.dims[2]])] +@tf.RegisterGradient('GatherPoint') + +def _gather_point_grad(op,out_g): + inp=op.inputs[0] + idx=op.inputs[1] + return [sampling_module.gather_point_grad(inp,idx,out_g),None] +def farthest_point_sample(npoint,inp): + ''' +input: + int32 + batch_size * ndataset * 3 float32 +returns: + batch_size * npoint int32 + ''' + return sampling_module.farthest_point_sample(inp, npoint) +ops.NoGradient('FarthestPointSample') + + +if __name__=='__main__': + import numpy as np + np.random.seed(100) + triangles=np.random.rand(1,5,3,3).astype('float32') + with tf.device('/gpu:1'): + inp=tf.constant(triangles) + tria=inp[:,:,0,:] + trib=inp[:,:,1,:] + tric=inp[:,:,2,:] + areas=tf.sqrt(tf.reduce_sum(tf.cross(trib-tria,tric-tria)**2,2)+1e-9) + randomnumbers=tf.random_uniform((1,8192)) + triids=prob_sample(areas,randomnumbers) + tria_sample=gather_point(tria,triids) + trib_sample=gather_point(trib,triids) + tric_sample=gather_point(tric,triids) + us=tf.random_uniform((1,8192)) + vs=tf.random_uniform((1,8192)) + uplusv=1-tf.abs(us+vs-1) + uminusv=us-vs + us=(uplusv+uminusv)*0.5 + vs=(uplusv-uminusv)*0.5 + pt_sample=tria_sample+(trib_sample-tria_sample)*tf.expand_dims(us,-1)+(tric_sample-tria_sample)*tf.expand_dims(vs,-1) + print('pt_sample: ', pt_sample) + reduced_sample=gather_point(pt_sample,farthest_point_sample(1024,pt_sample)) + print(reduced_sample) + with tf.Session('') as sess: + ret=sess.run(reduced_sample) + print(ret.shape,ret.dtype) + import cPickle as pickle + pickle.dump(ret,open('1.pkl','wb'),-1) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling_compile.sh" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling_compile.sh" new file mode 100644 index 0000000..ad14627 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling_compile.sh" @@ -0,0 +1,17 @@ +#/bin/bash +/usr/local/cuda-9.0/bin/nvcc tf_sampling_g.cu -o tf_sampling_g.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC + +# TF1.2 +#g++ -std=c++11 tf_sampling.cpp tf_sampling_g.cu.o -o tf_sampling_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +# TF1.2 +#g++ -std=c++11 tf_sampling.cpp tf_sampling_g.cu.o -o tf_sampling_so.so -shared -fPIC -I /home/chendiane/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/include -I /usr/local/cuda-10.1/include -lcudart -L /usr/local/cuda-10.1/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +# TF1.4 +#g++ -std=c++11 tf_sampling.cpp tf_sampling_g.cu.o -o tf_sampling_so.so -shared -fPIC -I /home/chendiane/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 +# TF1.4 + +g++ -std=c++11 tf_sampling.cpp tf_sampling_g.cu.o -o tf_sampling_so.so -shared -fPIC -I/home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include -I /usr/local/cuda-9.0/include -I/home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-9.0/lib64/ -L/home/cc/anaconda3/envs/tf1.8.0/lib/python3.6/site-packages/tensorflow -l:libtensorflow_framework.so -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + +#.so结尾的要用l:lib......framework.so +#g++ -std=c++11 tf_sampling.cpp tf_sampling_g.cu.o -o tf_sampling_so.so -shared -fPIC -I /home/chendiane/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/include -I /usr/local/cuda-10.1/include -I /home/chendiane/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-10.1/lib64/ -L/home/chendiane/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling_g.cu" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling_g.cu" new file mode 100644 index 0000000..6e28bc7 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling_g.cu" @@ -0,0 +1,212 @@ +/* Furthest point sampling GPU implementation + * Original author: Haoqiang Fan + * Modified by Charles R. Qi + * All Rights Reserved. 2017. + */ + +__global__ void cumsumKernel(int b,int n,const float * __restrict__ inp,float * __restrict__ out){ + const int BlockSize=2048; + const int paddingLevel=5; + __shared__ float buffer4[BlockSize*4]; + __shared__ float buffer[BlockSize+(BlockSize>>paddingLevel)]; + for (int i=blockIdx.x;i>2; + for (int k=threadIdx.x*4;k>2)+(k>>(2+paddingLevel))]=v4; + }else{ + float v=0; + for (int k2=k;k2>2)+(k>>(2+paddingLevel))]=v; + } + } + int u=0; + for (;(2<>(u+1));k+=blockDim.x){ + int i1=(((k<<1)+2)<>paddingLevel; + i2+=i2>>paddingLevel; + buffer[i1]+=buffer[i2]; + } + } + u--; + for (;u>=0;u--){ + __syncthreads(); + for (int k=threadIdx.x;k>(u+1));k+=blockDim.x){ + int i1=(((k<<1)+3)<>paddingLevel; + i2+=i2>>paddingLevel; + buffer[i1]+=buffer[i2]; + } + } + __syncthreads(); + for (int k=threadIdx.x*4;k>2)-1)+(((k>>2)-1)>>paddingLevel); + buffer4[k]+=buffer[k2]; + buffer4[k+1]+=buffer[k2]; + buffer4[k+2]+=buffer[k2]; + buffer4[k+3]+=buffer[k2]; + } + } + __syncthreads(); + for (int k=threadIdx.x;k>paddingLevel)]+runningsum2; + float r2=runningsum+t; + runningsum2=t-(r2-runningsum); + runningsum=r2; + __syncthreads(); + } + } +} + +__global__ void binarysearchKernel(int b,int n,int m,const float * __restrict__ dataset,const float * __restrict__ query, int * __restrict__ result){ + int base=1; + while (base=1;k>>=1) + if (r>=k && dataset[i*n+r-k]>=q) + r-=k; + result[i*m+j]=r; + } + } +} +__global__ void farthestpointsamplingKernel(int b,int n,int m,const float * __restrict__ dataset,float * __restrict__ temp,int * __restrict__ idxs){ + if (m<=0) + return; + const int BlockSize=512; + __shared__ float dists[BlockSize]; + __shared__ int dists_i[BlockSize]; + const int BufferSize=3072; + __shared__ float buf[BufferSize*3]; + for (int i=blockIdx.x;ibest){ + best=d2; + besti=k; + } + } + dists[threadIdx.x]=best; + dists_i[threadIdx.x]=besti; + for (int u=0;(1<>(u+1))){ + int i1=(threadIdx.x*2)<>>(b,n,inp,out); +} +//require b*n working space +void probsampleLauncher(int b,int n,int m,const float * inp_p,const float * inp_r,float * temp,int * out){ + cumsumKernel<<<32,512>>>(b,n,inp_p,temp); + binarysearchKernel<<>>(b,n,m,temp,inp_r,out); +} +//require 32*n working space +void farthestpointsamplingLauncher(int b,int n,int m,const float * inp,float * temp,int * out){ + farthestpointsamplingKernel<<<32,512>>>(b,n,m,inp,temp,out); +} +void gatherpointLauncher(int b,int n,int m,const float * inp,const int * idx,float * out){ + gatherpointKernel<<>>(b,n,m,inp,idx,out); +} +void scatteraddpointLauncher(int b,int n,int m,const float * out_g,const int * idx,float * inp_g){ + scatteraddpointKernel<<>>(b,n,m,out_g,idx,inp_g); +} + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/tf_ops/sampling/tf_sampling_g.cu.o" 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=?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/data=5Futils?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../data_utils/.keep" | 0 .../data_utils/ModelNetDataLoader.py" | 103 --- .../data_utils/collect_indoor3d_data.py" | 24 - .../extract and save point cloud .py" | 157 ----- .../data_utils/indoor3d_util.py" | 598 ------------------ .../data_utils/make hdf5_file.py" | 58 -- 6 files changed, 940 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/.keep" delete mode 100644 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"b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/.keep" deleted file mode 100644 index e69de29..0000000 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/ModelNetDataLoader.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/ModelNetDataLoader.py" deleted file mode 100644 index 3e343ef..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/ModelNetDataLoader.py" +++ /dev/null @@ -1,103 +0,0 @@ -import numpy as np -import warnings -import os -from torch.utils.data import Dataset -warnings.filterwarnings('ignore') - - - -def pc_normalize(pc): - centroid = np.mean(pc, axis=0) - pc = pc - centroid - m = np.max(np.sqrt(np.sum(pc**2, axis=1))) - pc = pc / m - return pc - -def farthest_point_sample(point, npoint): - """ - Input: - xyz: pointcloud data, [N, D] - npoint: number of samples - Return: - centroids: sampled pointcloud index, [npoint, D] - """ - N, D = point.shape - xyz = point[:,:3] - centroids = np.zeros((npoint,)) - distance = np.ones((N,)) * 1e10 - farthest = np.random.randint(0, N) - for i in range(npoint): - centroids[i] = farthest - centroid = xyz[farthest, :] - dist = np.sum((xyz - centroid) ** 2, -1) - mask = dist < distance - distance[mask] = dist[mask] - farthest = np.argmax(distance, -1) - point = point[centroids.astype(np.int32)] - return point - -class ModelNetDataLoader(Dataset): - def __init__(self, root, npoint=1024, split='train', uniform=False, normal_channel=True, cache_size=15000): - self.root = root - self.npoints = npoint - self.uniform = uniform - self.catfile = os.path.join(self.root, 'modelnet40_shape_names.txt') - - self.cat = [line.rstrip() for line in open(self.catfile)] - self.classes = dict(zip(self.cat, range(len(self.cat)))) - self.normal_channel = normal_channel - - shape_ids = {} - shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_train.txt'))] - shape_ids['test'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_test.txt'))] - - assert (split == 'train' or split == 'test') - shape_names = ['_'.join(x.split('_')[0:-1]) for x in shape_ids[split]] - # list of (shape_name, shape_txt_file_path) tuple - self.datapath = [(shape_names[i], os.path.join(self.root, shape_names[i], shape_ids[split][i]) + '.txt') for i - in range(len(shape_ids[split]))] - print('The size of %s data is %d'%(split,len(self.datapath))) - - self.cache_size = cache_size # how many data points to cache in memory - self.cache = {} # from index to (point_set, cls) tuple - - def __len__(self): - return len(self.datapath) - - def _get_item(self, index): - if index in self.cache: - point_set, cls = self.cache[index] - else: - fn = self.datapath[index] - cls = self.classes[self.datapath[index][0]] - cls = np.array([cls]).astype(np.int32) - point_set = np.loadtxt(fn[1], delimiter=',').astype(np.float32) - if self.uniform: - point_set = farthest_point_sample(point_set, self.npoints) - else: - point_set = point_set[0:self.npoints,:] - - point_set[:, 0:3] = pc_normalize(point_set[:, 0:3]) - - if not self.normal_channel: - point_set = point_set[:, 0:3] - - if len(self.cache) < self.cache_size: - self.cache[index] = (point_set, cls) - - return point_set, cls - - def __getitem__(self, index): - return self._get_item(index) - - - - -if __name__ == '__main__': - import torch - - data = ModelNetDataLoader('/data/modelnet40_normal_resampled/',split='train', uniform=False, normal_channel=True,) - DataLoader = torch.utils.data.DataLoader(data, batch_size=12, shuffle=True) - for point,label in DataLoader: - print(point.shape) - print(label.shape) \ No newline at end of file diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/collect_indoor3d_data.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/collect_indoor3d_data.py" deleted file mode 100644 index f1bdeeb..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/collect_indoor3d_data.py" +++ /dev/null @@ -1,24 +0,0 @@ -import os -import sys -from indoor3d_util import DATA_PATH, collect_point_label - -BASE_DIR = os.path.dirname(os.path.abspath(__file__)) -ROOT_DIR = os.path.dirname(BASE_DIR) -sys.path.append(BASE_DIR) - -anno_paths = [line.rstrip() for line in open(os.path.join(BASE_DIR, 'meta/anno_paths.txt'))] -anno_paths = [os.path.join(DATA_PATH, p) for p in anno_paths] - -output_folder = os.path.join(ROOT_DIR, 'data/stanford_indoor3d') -if not os.path.exists(output_folder): - os.mkdir(output_folder) - -# Note: there is an extra character in the v1.2 data in Area_5/hallway_6. It's fixed manually. -for anno_path in anno_paths: - print(anno_path) - try: - elements = anno_path.split('/') - out_filename = elements[-3]+'_'+elements[-2]+'.npy' # Area_1_hallway_1.npy - collect_point_label(anno_path, os.path.join(output_folder, out_filename), 'numpy') - except: - print(anno_path, 'ERROR!!') diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" deleted file mode 100644 index c761dfd..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/extract and save point cloud .py" +++ /dev/null @@ -1,157 +0,0 @@ -# -*- coding: utf-8 -*- -# 注意此时的laspy，有版本问题，2.0以上的版本就会出现问题，此时的版本是1.7.0 -from laspy.file import File -import os -import arcpy -import shutil -import numpy as np -np.set_printoptions(suppress=True) - -# 获取txt文件中的各种文件 -def GetData_fromFile(list_filename): - return [line.rstrip() for line in open(list_filename,'r')] - -# 只获取文件里面的shpfile中的shp文件 -def Get_specific_File(dir, ext): - need_file = [] - allfiles = os.listdir(dir) - for i in range(len(allfiles)): - name,extend = os.path.splitext(allfiles[i]) - if extend[1:] == ext: - root_shp_path = os.path.join(dir,allfiles[i]) - need_file.append(root_shp_path) - return need_file - -#分割对应点云块的shp文件，以便于下一步提取 -def split_move_shp(shp_path, out_path, label,splitfield): - arcpy.Split_analysis(in_features = shp_path, split_features = shp_path, - split_field = splitfield, out_workspace = out_path) - all_files = os.listdir(out_path) - for k in range(len(label)): - move_path_dir = os.path.join(out_path, label[k]) - for file in all_files: - name_first, ext_first = os.path.splitext(file) # 分离文件名和后缀 - f_name, ext_second = os.path.splitext(name_first) # 再次分离文件名和后缀，因为出现了.shp.xml的文件，对于.shp的不影响 - if f_name[:-4] == label[k]: - shutil.move( os.path.join(out_path,file), move_path_dir) - # 点云规范化 -def PC_NORMLIZE(pc): - centroid = np.mean(pc, axis=0) - pc = pc - centroid - m = np.max(np.sqrt(np.sum(pc**2, axis=1))) - pc = pc / m - return pc - -# 采样到2048 -def sample_data(data, num_sample): - """ data is in N x ... - we want to keep num_samplexC of them. - if N > num_sample, we will randomly keep num_sample of them. - if N < num_sample, we will randomly duplicate samples. - """ - N = data.shape[0] - if (N == num_sample): - return data - elif (N > num_sample): - sample = np.random.choice(N, num_sample) - return data[sample, ...] - else: - # 相当于从N中抽取 num_sample - N个随机数组成一维数组array，成为data的下标索引值 - sample = np.random.choice(N, num_sample - N) - dup_data = data[sample, ...] # 取数据 - # 按行拼接 - return np.concatenate([data, dup_data], axis=0) - # return np.concatenate([data, dup_data], 0), list(range(N)) + list(sample) - - -if __name__ == "__main__": - - las_name = GetData_fromFile('E:\CITYtest\FilelistAdd/lasname.txt') - label_name = GetData_fromFile('E:\CITYtest\FilelistAdd\shapelable.txt') - #自定义需要得到什么类型的文件数据 - file_class = ['Classshpfile', 'Classlasfile','Classtxtfile'] - #输入文件·路径 - input_shp_dir = 'E:\CITYtest\PCinput//' - input_las_dir = 'E:\CITYtest/NDSMlasdata//' - save_dir = 'E:\CITYtest\savepath/' - - - net_num = 2048 - for m in range(len(las_name)): - # 每个类别shp文件的路径 - shp_save_path = save_dir + '/'+ las_name[m] + '/'+ file_class[0] - las_save_path = save_dir + '/'+ las_name[m] + '/'+ file_class[1] - txt_save_path = save_dir + '/'+ las_name[m] + '/'+ file_class[2] - # 获取每整个点云块的shp文件 - # 注意shp_full 是一个列表文件，不是直接变量，引用时需要用shp_full[0,1,2] - shp_full = Get_specific_File(os.path.join(input_shp_dir, las_name[m]), 'shp') - # # # 开始对整个shp文件分割和移动 - if len(shp_full) != 0: - split_move_shp( shp_full[0], shp_save_path, label_name,'newname') - # 数据分割完毕，开始进行las点云提取 - # 得到每个label文件下的shp文件 - # 定义要提取的las路径，开始提取 - all_las_list = os.listdir(input_las_dir) - - las_ndsm_name, extend = os.path.splitext(all_las_list[m]) - - origin_las = os.path.join(input_las_dir, all_las_list[m]) - - for i in range(len(label_name)): - # 获取每个类别的单个样本shp - single_shp_path = os.path.join(shp_save_path, label_name[i]) - shp_list = Get_specific_File(single_shp_path, 'shp') - # 提取单个las的路径 - single_las_save = os.path.join(las_save_path, label_name[i]) - - if len(shp_list) != 0: - for j in range(len(shp_list)): - stir = str(j + 1) - st = stir.zfill(4) # 补零补够四位数 - arcpy.ddd.ExtractLas(in_las_dataset = origin_las, - target_folder = single_las_save, - boundary= shp_list[j], - name_suffix=label_name[i] + st, remove_vlr=True, - rearrange_points='REARRANGE_POINTS', ) - - las_list_path = os.path.join(single_las_save, las_ndsm_name + label_name[i] + st + '.las') - - # 只提取样本las中的X,Y,Z和intensity,raw_classification信息 - f = File(las_list_path, mode='rw') - # 改变样本标签为 0,1,2 - # print(f.x.shape) - point_cloud = np.vstack((f.x, f.y, f.z, f.intensity, f.raw_classification)).T - - point_cloud[:,4] = i - # 给点云加上底面，获取行数 - row_num = point_cloud.shape[0] - # 生成和行数一 样的一维0数组 - z_back = np.zeros(row_num) - # 底面 - point_back = np.vstack((f.x, f.y, z_back, f.intensity, f.raw_classification)).T - # 屋顶和底面放一起 - # print(point_back.shape) - point_cloud_add = np.vstack((point_cloud, point_back)) - # point_cloud_add = np.concatenate((point_cloud, point_back),axis=0) - # 采样到2048 - sample_pc = sample_data(point_cloud_add, net_num) - # 只取数据坐标信息归一化，中心化 - pc_norm = PC_NORMLIZE(sample_pc[:, 0:3]) - # 规范后数据代替原来的坐标 - sample_pc[:, 0:3] = pc_norm - # 存储为txt格式 - single_txt_save = os.path.join(txt_save_path,label_name[i]) - - np.savetxt( os.path.join(single_txt_save , las_name[m] + label_name[i] + st + '.txt'), - sample_pc, fmt="%.6f", delimiter=" ") - else: - print ("该类别无样本数据") - - else: - print('该块las的样本shp为空') - - print('successful') -print('over') - - - diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/indoor3d_util.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/indoor3d_util.py" deleted file mode 100644 index 42a7d97..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/indoor3d_util.py" +++ /dev/null @@ -1,598 +0,0 @@ -import numpy as np -import glob -import os -import sys - -BASE_DIR = os.path.dirname(os.path.abspath(__file__)) -ROOT_DIR = os.path.dirname(BASE_DIR) -sys.path.append(BASE_DIR) - -DATA_PATH = os.path.join(ROOT_DIR, 'data','s3dis', 'Stanford3dDataset_v1.2_Aligned_Version') -g_classes = [x.rstrip() for x in open(os.path.join(BASE_DIR, 'meta/class_names.txt'))] -g_class2label = {cls: i for i,cls in enumerate(g_classes)} -g_class2color = {'ceiling': [0,255,0], - 'floor': [0,0,255], - 'wall': [0,255,255], - 'beam': [255,255,0], - 'column': [255,0,255], - 'window': [100,100,255], - 'door': [200,200,100], - 'table': [170,120,200], - 'chair': [255,0,0], - 'sofa': [200,100,100], - 'bookcase': [10,200,100], - 'board': [200,200,200], - 'clutter': [50,50,50]} -g_easy_view_labels = [7,8,9,10,11,1] -g_label2color = {g_classes.index(cls): g_class2color[cls] for cls in g_classes} - - -# ----------------------------------------------------------------------------- -# CONVERT ORIGINAL DATA TO OUR DATA_LABEL FILES -# ----------------------------------------------------------------------------- - -def collect_point_label(anno_path, out_filename, file_format='txt'): - """ Convert original dataset files to data_label file (each line is XYZRGBL). - We aggregated all the points from each instance in the room. - - Args: - anno_path: path to annotations. e.g. Area_1/office_2/Annotations/ - out_filename: path to save collected points and labels (each line is XYZRGBL) - file_format: txt or numpy, determines what file format to save. - Returns: - None - Note: - the points are shifted before save, the most negative point is now at origin. - """ - points_list = [] - for f in glob.glob(os.path.join(anno_path, '*.txt')): - cls = os.path.basename(f).split('_')[0] - print(f) - if cls not in g_classes: # note: in some room there is 'staris' class.. - cls = 'clutter' - - points = np.loadtxt(f) - labels = np.ones((points.shape[0],1)) * g_class2label[cls] - points_list.append(np.concatenate([points, labels], 1)) # Nx7 - - data_label = np.concatenate(points_list, 0) - xyz_min = np.amin(data_label, axis=0)[0:3] - data_label[:, 0:3] -= xyz_min - - if file_format=='txt': - fout = open(out_filename, 'w') - for i in range(data_label.shape[0]): - fout.write('%f %f %f %d %d %d %d\n' % \ - (data_label[i,0], data_label[i,1], data_label[i,2], - data_label[i,3], data_label[i,4], data_label[i,5], - data_label[i,6])) - fout.close() - elif file_format=='numpy': - np.save(out_filename, data_label) - else: - print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \ - (file_format)) - exit() - -def data_to_obj(data,name='example.obj',no_wall=True): - fout = open(name, 'w') - label = data[:, -1].astype(int) - for i in range(data.shape[0]): - if no_wall and ((label[i] == 2) or (label[i]==0)): - continue - fout.write('v %f %f %f %d %d %d\n' % \ - (data[i, 0], data[i, 1], data[i, 2], data[i, 3], data[i, 4], data[i, 5])) - fout.close() - -def point_label_to_obj(input_filename, out_filename, label_color=True, easy_view=False, no_wall=False): - """ For visualization of a room from data_label file, - input_filename: each line is X Y Z R G B L - out_filename: OBJ filename, - visualize input file by coloring point with label color - easy_view: only visualize furnitures and floor - """ - data_label = np.loadtxt(input_filename) - data = data_label[:, 0:6] - label = data_label[:, -1].astype(int) - fout = open(out_filename, 'w') - for i in range(data.shape[0]): - color = g_label2color[label[i]] - if easy_view and (label[i] not in g_easy_view_labels): - continue - if no_wall and ((label[i] == 2) or (label[i]==0)): - continue - if label_color: - fout.write('v %f %f %f %d %d %d\n' % \ - (data[i,0], data[i,1], data[i,2], color[0], color[1], color[2])) - else: - fout.write('v %f %f %f %d %d %d\n' % \ - (data[i,0], data[i,1], data[i,2], data[i,3], data[i,4], data[i,5])) - fout.close() - - - -# ----------------------------------------------------------------------------- -# PREPARE BLOCK DATA FOR DEEPNETS TRAINING/TESTING -# ----------------------------------------------------------------------------- - -def sample_data(data, num_sample): - """ data is in N x ... - we want to keep num_samplexC of them. - if N > num_sample, we will randomly keep num_sample of them. - if N < num_sample, we will randomly duplicate samples. - """ - N = data.shape[0] - if (N == num_sample): - return data, range(N) - elif (N > num_sample): - sample = np.random.choice(N, num_sample) - return data[sample, ...], sample - else: - sample = np.random.choice(N, num_sample-N) - dup_data = data[sample, ...] - return np.concatenate([data, dup_data], 0), list(range(N))+list(sample) - -def sample_data_label(data, label, num_sample): - new_data, sample_indices = sample_data(data, num_sample) - new_label = label[sample_indices] - return new_data, new_label - -def room2blocks(data, label, num_point, block_size=1.0, stride=1.0, - random_sample=False, sample_num=None, sample_aug=1): - """ Prepare block training data. - Args: - data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] - assumes the data is shifted (min point is origin) and aligned - (aligned with XYZ axis) - label: N size uint8 numpy array from 0-12 - num_point: int, how many points to sample in each block - block_size: float, physical size of the block in meters - stride: float, stride for block sweeping - random_sample: bool, if True, we will randomly sample blocks in the room - sample_num: int, if random sample, how many blocks to sample - [default: room area] - sample_aug: if random sample, how much aug - Returns: - block_datas: K x num_point x 6 np array of XYZRGB, RGB is in [0,1] - block_labels: K x num_point x 1 np array of uint8 labels - - TODO: for this version, blocking is in fixed, non-overlapping pattern. - """ - assert(stride<=block_size) - - limit = np.amax(data, 0)[0:3] - - # Get the corner location for our sampling blocks - xbeg_list = [] - ybeg_list = [] - if not random_sample: - num_block_x = int(np.ceil((limit[0] - block_size) / stride)) + 1 - num_block_y = int(np.ceil(collect_point_label(limit[1] - block_size) / stride)) + 1 - for i in range(num_block_x): - for j in range(num_block_y): - xbeg_list.append(i*stride) - ybeg_list.append(j*stride) - else: - num_block_x = int(np.ceil(limit[0] / block_size)) - num_block_y = int(np.ceil(limit[1] / block_size)) - if sample_num is None: - sample_num = num_block_x * num_block_y * sample_aug - for _ in range(sample_num): - xbeg = np.random.uniform(-block_size, limit[0]) - ybeg = np.random.uniform(-block_size, limit[1]) - xbeg_list.append(xbeg) - ybeg_list.append(ybeg) - - # Collect blocks - block_data_list = [] - block_label_list = [] - idx = 0 - for idx in range(len(xbeg_list)): - xbeg = xbeg_list[idx] - ybeg = ybeg_list[idx] - xcond = (data[:,0]<=xbeg+block_size) & (data[:,0]>=xbeg) - ycond = (data[:,1]<=ybeg+block_size) & (data[:,1]>=ybeg) - cond = xcond & ycond - if np.sum(cond) < 100: # discard block if there are less than 100 pts. - continue - - block_data = data[cond, :] - block_label = label[cond] - - # randomly subsample data - block_data_sampled, block_label_sampled = \ - sample_data_label(block_data, block_label, num_point) - block_data_list.append(np.expand_dims(block_data_sampled, 0)) - block_label_list.append(np.expand_dims(block_label_sampled, 0)) - - return np.concatenate(block_data_list, 0), \ - np.concatenate(block_label_list, 0) - - -def room2blocks_plus(data_label, num_point, block_size, stride, - random_sample, sample_num, sample_aug): - """ room2block with input filename and RGB preprocessing. - """ - data = data_label[:,0:6] - data[:,3:6] /= 255.0 - label = data_label[:,-1].astype(np.uint8) - - return room2blocks(data, label, num_point, block_size, stride, - random_sample, sample_num, sample_aug) - -def room2blocks_wrapper(data_label_filename, num_point, block_size=1.0, stride=1.0, - random_sample=False, sample_num=None, sample_aug=1): - if data_label_filename[-3:] == 'txt': - data_label = np.loadtxt(data_label_filename) - elif data_label_filename[-3:] == 'npy': - data_label = np.load(data_label_filename) - else: - print('Unknown file type! exiting.') - exit() - return room2blocks_plus(data_label, num_point, block_size, stride, - random_sample, sample_num, sample_aug) - -def room2blocks_plus_normalized(data_label, num_point, block_size, stride, - random_sample, sample_num, sample_aug): - """ room2block, with input filename and RGB preprocessing. - for each block centralize XYZ, add normalized XYZ as 678 channels - """ - data = data_label[:,0:6] - data[:,3:6] /= 255.0 - label = data_label[:,-1].astype(np.uint8) - max_room_x = max(data[:,0]) - max_room_y = max(data[:,1]) - max_room_z = max(data[:,2]) - - data_batch, label_batch = room2blocks(data, label, num_point, block_size, stride, - random_sample, sample_num, sample_aug) - new_data_batch = np.zeros((data_batch.shape[0], num_point, 9)) - for b in range(data_batch.shape[0]): - new_data_batch[b, :, 6] = data_batch[b, :, 0]/max_room_x - new_data_batch[b, :, 7] = data_batch[b, :, 1]/max_room_y - new_data_batch[b, :, 8] = data_batch[b, :, 2]/max_room_z - minx = min(data_batch[b, :, 0]) - miny = min(data_batch[b, :, 1]) - data_batch[b, :, 0] -= (minx+block_size/2) - data_batch[b, :, 1] -= (miny+block_size/2) - new_data_batch[:, :, 0:6] = data_batch - return new_data_batch, label_batch - - -def room2blocks_wrapper_normalized(data_label_filename, num_point, block_size=1.0, stride=1.0, - random_sample=False, sample_num=None, sample_aug=1): - if data_label_filename[-3:] == 'txt': - data_label = np.loadtxt(data_label_filename) - elif data_label_filename[-3:] == 'npy': - data_label = np.load(data_label_filename) - else: - print('Unknown file type! exiting.') - exit() - return room2blocks_plus_normalized(data_label, num_point, block_size, stride, - random_sample, sample_num, sample_aug) - -def room2samples(data, label, sample_num_point): - """ Prepare whole room samples. - - Args: - data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] - assumes the data is shifted (min point is origin) and - aligned (aligned with XYZ axis) - label: N size uint8 numpy array from 0-12 - sample_num_point: int, how many points to sample in each sample - Returns: - sample_datas: K x sample_num_point x 9 - numpy array of XYZRGBX'Y'Z', RGB is in [0,1] - sample_labels: K x sample_num_point x 1 np array of uint8 labels - """ - N = data.shape[0] - order = np.arange(N) - np.random.shuffle(order) - data = data[order, :] - label = label[order] - - batch_num = int(np.ceil(N / float(sample_num_point))) - sample_datas = np.zeros((batch_num, sample_num_point, 6)) - sample_labels = np.zeros((batch_num, sample_num_point, 1)) - - for i in range(batch_num): - beg_idx = i*sample_num_point - end_idx = min((i+1)*sample_num_point, N) - num = end_idx - beg_idx - sample_datas[i,0:num,:] = data[beg_idx:end_idx, :] - sample_labels[i,0:num,0] = label[beg_idx:end_idx] - if num < sample_num_point: - makeup_indices = np.random.choice(N, sample_num_point - num) - sample_datas[i,num:,:] = data[makeup_indices, :] - sample_labels[i,num:,0] = label[makeup_indices] - return sample_datas, sample_labels - -def room2samples_plus_normalized(data_label, num_point): - """ room2sample, with input filename and RGB preprocessing. - for each block centralize XYZ, add normalized XYZ as 678 channels - """ - data = data_label[:,0:6] - data[:,3:6] /= 255.0 - label = data_label[:,-1].astype(np.uint8) - max_room_x = max(data[:,0]) - max_room_y = max(data[:,1]) - max_room_z = max(data[:,2]) - #print(max_room_x, max_room_y, max_room_z) - - data_batch, label_batch = room2samples(data, label, num_point) - new_data_batch = np.zeros((data_batch.shape[0], num_point, 9)) - for b in range(data_batch.shape[0]): - new_data_batch[b, :, 6] = data_batch[b, :, 0]/max_room_x - new_data_batch[b, :, 7] = data_batch[b, :, 1]/max_room_y - new_data_batch[b, :, 8] = data_batch[b, :, 2]/max_room_z - #minx = min(data_batch[b, :, 0]) - #miny = min(data_batch[b, :, 1]) - #data_batch[b, :, 0] -= (minx+block_size/2) - #data_batch[b, :, 1] -= (miny+block_size/2) - new_data_batch[:, :, 0:6] = data_batch - return new_data_batch, label_batch - - -def room2samples_wrapper_normalized(data_label_filename, num_point): - if data_label_filename[-3:] == 'txt': - data_label = np.loadtxt(data_label_filename) - elif data_label_filename[-3:] == 'npy': - data_label = np.load(data_label_filename) - else: - print('Unknown file type! exiting.') - exit() - return room2samples_plus_normalized(data_label, num_point) - - -# ----------------------------------------------------------------------------- -# EXTRACT INSTANCE BBOX FROM ORIGINAL DATA (for detection evaluation) -# ----------------------------------------------------------------------------- - -def collect_bounding_box(anno_path, out_filename): - """ Compute bounding boxes from each instance in original dataset files on - one room. **We assume the bbox is aligned with XYZ coordinate.** - - Args: - anno_path: path to annotations. e.g. Area_1/office_2/Annotations/ - out_filename: path to save instance bounding boxes for that room. - each line is x1 y1 z1 x2 y2 z2 label, - where (x1,y1,z1) is the point on the diagonal closer to origin - Returns: - None - Note: - room points are shifted, the most negative point is now at origin. - """ - bbox_label_list = [] - - for f in glob.glob(os.path.join(anno_path, '*.txt')): - cls = os.path.basename(f).split('_')[0] - if cls not in g_classes: # note: in some room there is 'staris' class.. - cls = 'clutter' - points = np.loadtxt(f) - label = g_class2label[cls] - # Compute tightest axis aligned bounding box - xyz_min = np.amin(points[:, 0:3], axis=0) - xyz_max = np.amax(points[:, 0:3], axis=0) - ins_bbox_label = np.expand_dims( - np.concatenate([xyz_min, xyz_max, np.array([label])], 0), 0) - bbox_label_list.append(ins_bbox_label) - - bbox_label = np.concatenate(bbox_label_list, 0) - room_xyz_min = np.amin(bbox_label[:, 0:3], axis=0) - bbox_label[:, 0:3] -= room_xyz_min - bbox_label[:, 3:6] -= room_xyz_min - - fout = open(out_filename, 'w') - for i in range(bbox_label.shape[0]): - fout.write('%f %f %f %f %f %f %d\n' % \ - (bbox_label[i,0], bbox_label[i,1], bbox_label[i,2], - bbox_label[i,3], bbox_label[i,4], bbox_label[i,5], - bbox_label[i,6])) - fout.close() - -def bbox_label_to_obj(input_filename, out_filename_prefix, easy_view=False): - """ Visualization of bounding boxes. - - Args: - input_filename: each line is x1 y1 z1 x2 y2 z2 label - out_filename_prefix: OBJ filename prefix, - visualize object by g_label2color - easy_view: if True, only visualize furniture and floor - Returns: - output a list of OBJ file and MTL files with the same prefix - """ - bbox_label = np.loadtxt(input_filename) - bbox = bbox_label[:, 0:6] - label = bbox_label[:, -1].astype(int) - v_cnt = 0 # count vertex - ins_cnt = 0 # count instance - for i in range(bbox.shape[0]): - if easy_view and (label[i] not in g_easy_view_labels): - continue - obj_filename = out_filename_prefix+'_'+g_classes[label[i]]+'_'+str(ins_cnt)+'.obj' - mtl_filename = out_filename_prefix+'_'+g_classes[label[i]]+'_'+str(ins_cnt)+'.mtl' - fout_obj = open(obj_filename, 'w') - fout_mtl = open(mtl_filename, 'w') - fout_obj.write('mtllib %s\n' % (os.path.basename(mtl_filename))) - - length = bbox[i, 3:6] - bbox[i, 0:3] - a = length[0] - b = length[1] - c = length[2] - x = bbox[i, 0] - y = bbox[i, 1] - z = bbox[i, 2] - color = np.array(g_label2color[label[i]], dtype=float) / 255.0 - - material = 'material%d' % (ins_cnt) - fout_obj.write('usemtl %s\n' % (material)) - fout_obj.write('v %f %f %f\n' % (x,y,z+c)) - fout_obj.write('v %f %f %f\n' % (x,y+b,z+c)) - fout_obj.write('v %f %f %f\n' % (x+a,y+b,z+c)) - fout_obj.write('v %f %f %f\n' % (x+a,y,z+c)) - fout_obj.write('v %f %f %f\n' % (x,y,z)) - fout_obj.write('v %f %f %f\n' % (x,y+b,z)) - fout_obj.write('v %f %f %f\n' % (x+a,y+b,z)) - fout_obj.write('v %f %f %f\n' % (x+a,y,z)) - fout_obj.write('g default\n') - v_cnt = 0 # for individual box - fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 3+v_cnt, 2+v_cnt, 1+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (1+v_cnt, 2+v_cnt, 6+v_cnt, 5+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (7+v_cnt, 6+v_cnt, 2+v_cnt, 3+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 8+v_cnt, 7+v_cnt, 3+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 8+v_cnt, 4+v_cnt, 1+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 6+v_cnt, 7+v_cnt, 8+v_cnt)) - fout_obj.write('\n') - - fout_mtl.write('newmtl %s\n' % (material)) - fout_mtl.write('Kd %f %f %f\n' % (color[0], color[1], color[2])) - fout_mtl.write('\n') - fout_obj.close() - fout_mtl.close() - - v_cnt += 8 - ins_cnt += 1 - -def bbox_label_to_obj_room(input_filename, out_filename_prefix, easy_view=False, permute=None, center=False, exclude_table=False): - """ Visualization of bounding boxes. - - Args: - input_filename: each line is x1 y1 z1 x2 y2 z2 label - out_filename_prefix: OBJ filename prefix, - visualize object by g_label2color - easy_view: if True, only visualize furniture and floor - permute: if not None, permute XYZ for rendering, e.g. [0 2 1] - center: if True, move obj to have zero origin - Returns: - output a list of OBJ file and MTL files with the same prefix - """ - bbox_label = np.loadtxt(input_filename) - bbox = bbox_label[:, 0:6] - if permute is not None: - assert(len(permute)==3) - permute = np.array(permute) - bbox[:,0:3] = bbox[:,permute] - bbox[:,3:6] = bbox[:,permute+3] - if center: - xyz_max = np.amax(bbox[:,3:6], 0) - bbox[:,0:3] -= (xyz_max/2.0) - bbox[:,3:6] -= (xyz_max/2.0) - bbox /= np.max(xyz_max/2.0) - label = bbox_label[:, -1].astype(int) - obj_filename = out_filename_prefix+'.obj' - mtl_filename = out_filename_prefix+'.mtl' - - fout_obj = open(obj_filename, 'w') - fout_mtl = open(mtl_filename, 'w') - fout_obj.write('mtllib %s\n' % (os.path.basename(mtl_filename))) - v_cnt = 0 # count vertex - ins_cnt = 0 # count instance - for i in range(bbox.shape[0]): - if easy_view and (label[i] not in g_easy_view_labels): - continue - if exclude_table and label[i] == g_classes.index('table'): - continue - - length = bbox[i, 3:6] - bbox[i, 0:3] - a = length[0] - b = length[1] - c = length[2] - x = bbox[i, 0] - y = bbox[i, 1] - z = bbox[i, 2] - color = np.array(g_label2color[label[i]], dtype=float) / 255.0 - - material = 'material%d' % (ins_cnt) - fout_obj.write('usemtl %s\n' % (material)) - fout_obj.write('v %f %f %f\n' % (x,y,z+c)) - fout_obj.write('v %f %f %f\n' % (x,y+b,z+c)) - fout_obj.write('v %f %f %f\n' % (x+a,y+b,z+c)) - fout_obj.write('v %f %f %f\n' % (x+a,y,z+c)) - fout_obj.write('v %f %f %f\n' % (x,y,z)) - fout_obj.write('v %f %f %f\n' % (x,y+b,z)) - fout_obj.write('v %f %f %f\n' % (x+a,y+b,z)) - fout_obj.write('v %f %f %f\n' % (x+a,y,z)) - fout_obj.write('g default\n') - fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 3+v_cnt, 2+v_cnt, 1+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (1+v_cnt, 2+v_cnt, 6+v_cnt, 5+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (7+v_cnt, 6+v_cnt, 2+v_cnt, 3+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 8+v_cnt, 7+v_cnt, 3+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 8+v_cnt, 4+v_cnt, 1+v_cnt)) - fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 6+v_cnt, 7+v_cnt, 8+v_cnt)) - fout_obj.write('\n') - - fout_mtl.write('newmtl %s\n' % (material)) - fout_mtl.write('Kd %f %f %f\n' % (color[0], color[1], color[2])) - fout_mtl.write('\n') - - v_cnt += 8 - ins_cnt += 1 - - fout_obj.close() - fout_mtl.close() - - -def collect_point_bounding_box(anno_path, out_filename, file_format): - """ Compute bounding boxes from each instance in original dataset files on - one room. **We assume the bbox is aligned with XYZ coordinate.** - Save both the point XYZRGB and the bounding box for the point's - parent element. - - Args: - anno_path: path to annotations. e.g. Area_1/office_2/Annotations/ - out_filename: path to save instance bounding boxes for each point, - plus the point's XYZRGBL - each line is XYZRGBL offsetX offsetY offsetZ a b c, - where cx = X+offsetX, cy=X+offsetY, cz=Z+offsetZ - where (cx,cy,cz) is center of the box, a,b,c are distances from center - to the surfaces of the box, i.e. x1 = cx-a, x2 = cx+a, y1=cy-b etc. - file_format: output file format, txt or numpy - Returns: - None - - Note: - room points are shifted, the most negative point is now at origin. - """ - point_bbox_list = [] - - for f in glob.glob(os.path.join(anno_path, '*.txt')): - cls = os.path.basename(f).split('_')[0] - if cls not in g_classes: # note: in some room there is 'staris' class.. - cls = 'clutter' - points = np.loadtxt(f) # Nx6 - label = g_class2label[cls] # N, - # Compute tightest axis aligned bounding box - xyz_min = np.amin(points[:, 0:3], axis=0) # 3, - xyz_max = np.amax(points[:, 0:3], axis=0) # 3, - xyz_center = (xyz_min + xyz_max) / 2 - dimension = (xyz_max - xyz_min) / 2 - - xyz_offsets = xyz_center - points[:,0:3] # Nx3 - dimensions = np.ones((points.shape[0],3)) * dimension # Nx3 - labels = np.ones((points.shape[0],1)) * label # N - point_bbox_list.append(np.concatenate([points, labels, - xyz_offsets, dimensions], 1)) # Nx13 - - point_bbox = np.concatenate(point_bbox_list, 0) # KxNx13 - room_xyz_min = np.amin(point_bbox[:, 0:3], axis=0) - point_bbox[:, 0:3] -= room_xyz_min - - if file_format == 'txt': - fout = open(out_filename, 'w') - for i in range(point_bbox.shape[0]): - fout.write('%f %f %f %d %d %d %d %f %f %f %f %f %f\n' % \ - (point_bbox[i,0], point_bbox[i,1], point_bbox[i,2], - point_bbox[i,3], point_bbox[i,4], point_bbox[i,5], - point_bbox[i,6], - point_bbox[i,7], point_bbox[i,8], point_bbox[i,9], - point_bbox[i,10], point_bbox[i,11], point_bbox[i,12])) - - fout.close() - elif file_format == 'numpy': - np.save(out_filename, point_bbox) - else: - print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \ - (file_format)) - exit() - - diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" deleted file mode 100644 index 8fad1d8..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data_utils/make hdf5_file.py" +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: UTF-8 -*- -import os -import numpy as np -import h5py -np.set_printoptions(suppress=True) - -def getDataFiles(path_root): - filelist = os.listdir(path_root) - for i in range(len(filelist)): - filelist[i] = os.path.join(path_root,filelist[i]) - return filelist - -#得到的返回值是一维字符串数组 -def loadDataFile(path): - data = np.loadtxt(path) - point_xyz = data[:, 0:3] - label=data[:,4] - label_int = label.astype(int) - return point_xyz, label_int - -if __name__ == "__main__": - - train_file = ['train0', 'test0'] - net_num = 2048 - train_dir = 'E:\CITYtest\Trainfile//' - - for i in range(len(train_file)): - root_file = os.path.join(train_dir,train_file[i]) - DATA_FILES = getDataFiles(root_file) - - DATA_ALL = [] - LABEL_ALL = [] - for fn in range(len(DATA_FILES)): - pre_data, his_label = loadDataFile(DATA_FILES[fn]) - pre_label = his_label.reshape(net_num, 1) # 重塑为num行1列的数据 得到num个点云的标签 - - # data_label = np.hstack((pre_data, pre_label)) - DATA_ALL.append(pre_data) #列表元素 - # label一个样本中的类别是一样的，取一个数字就可以了 - LABEL_ALL.append(pre_label[0]) - - # 把DATA_ALL和LABEL_ALL的列表转换成数组格式， - out_data = np.vstack(DATA_ALL) - out_label = np.vstack(LABEL_ALL) - - # 重塑为三维数组，2048 个 2048*4 的三个数组 - data_reshape = out_data.reshape(net_num, net_num, 3) - # 写入训练数据 - filename= train_dir +'/'+ 'point_data_' + train_file[i] + '.h5' - if not os.path.exists(filename): - with h5py.File(filename, 'w') as f: - f['data'] = data_reshape - f['label'] = out_label - f.close() - else: - print('hdf5文件已存在') - -print("over") -- Gitee From 14bb6385e4f9360141958362f368fa2ff630471c Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:35:19 +0000 Subject: [PATCH 60/69] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20utils?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../utils/.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/.keep" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/.keep" new file mode 100644 index 0000000..e69de29 -- Gitee From 7c1571fdf1de4d56e5d0bcbafc2df9f2235e7158 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:35:58 +0000 Subject: [PATCH 61/69] =?UTF-8?q?=E7=89=B9=E5=BE=81=E6=8F=90=E5=8F=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../utils/README.md" | 6 + .../utils/compile_render_balls_so.sh" | 2 + .../utils/eulerangles.py" | 418 ++++++++ .../utils/pc_util.py" | 315 ++++++ .../utils/plyfile.py" | 916 ++++++++++++++++++ .../utils/pointnet_util.py" | 230 +++++ .../utils/provider.py" | 247 +++++ .../utils/render_balls_so.cpp" | 58 ++ .../utils/show3d_balls.py" | 161 +++ .../utils/tf_util.py" | 616 ++++++++++++ 10 files changed, 2969 insertions(+) create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/README.md" create mode 100644 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"code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/render_balls_so.cpp" create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/show3d_balls.py" create mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/tf_util.py" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/README.md" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/README.md" new file mode 100644 index 0000000..6d2bfad --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/README.md" @@ -0,0 +1,6 @@ +## Utilility Functions for 3D Point Cloud Deep Learning + +### visualization tool + + sh compile_render_balls_so.sh + python show3d_balls.py diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/compile_render_balls_so.sh" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/compile_render_balls_so.sh" new file mode 100644 index 0000000..dc493f6 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/compile_render_balls_so.sh" @@ -0,0 +1,2 @@ +g++ -std=c++11 render_balls_so.cpp -o render_balls_so.so -shared -fPIC -O2 -D_GLIBCXX_USE_CXX11_ABI=0 + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/eulerangles.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/eulerangles.py" new file mode 100644 index 0000000..87bd605 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/eulerangles.py" @@ -0,0 +1,418 @@ +# emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## +# +# See COPYING file distributed along with the NiBabel package for the +# copyright and license terms. +# +### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## +''' Module implementing Euler angle rotations and their conversions + +See: + +* http://en.wikipedia.org/wiki/Rotation_matrix +* http://en.wikipedia.org/wiki/Euler_angles +* http://mathworld.wolfram.com/EulerAngles.html + +See also: *Representing Attitude with Euler Angles and Quaternions: A +Reference* (2006) by James Diebel. A cached PDF link last found here: + +http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.5134 + +Euler's rotation theorem tells us that any rotation in 3D can be +described by 3 angles. Let's call the 3 angles the *Euler angle vector* +and call the angles in the vector :math:`alpha`, :math:`beta` and +:math:`gamma`. The vector is [ :math:`alpha`, +:math:`beta`. :math:`gamma` ] and, in this description, the order of the +parameters specifies the order in which the rotations occur (so the +rotation corresponding to :math:`alpha` is applied first). + +In order to specify the meaning of an *Euler angle vector* we need to +specify the axes around which each of the rotations corresponding to +:math:`alpha`, :math:`beta` and :math:`gamma` will occur. + +There are therefore three axes for the rotations :math:`alpha`, +:math:`beta` and :math:`gamma`; let's call them :math:`i` :math:`j`, +:math:`k`. + +Let us express the rotation :math:`alpha` around axis `i` as a 3 by 3 +rotation matrix `A`. Similarly :math:`beta` around `j` becomes 3 x 3 +matrix `B` and :math:`gamma` around `k` becomes matrix `G`. Then the +whole rotation expressed by the Euler angle vector [ :math:`alpha`, +:math:`beta`. :math:`gamma` ], `R` is given by:: + + R = np.dot(G, np.dot(B, A)) + +See http://mathworld.wolfram.com/EulerAngles.html + +The order :math:`G B A` expresses the fact that the rotations are +performed in the order of the vector (:math:`alpha` around axis `i` = +`A` first). + +To convert a given Euler angle vector to a meaningful rotation, and a +rotation matrix, we need to define: + +* the axes `i`, `j`, `k` +* whether a rotation matrix should be applied on the left of a vector to + be transformed (vectors are column vectors) or on the right (vectors + are row vectors). +* whether the rotations move the axes as they are applied (intrinsic + rotations) - compared the situation where the axes stay fixed and the + vectors move within the axis frame (extrinsic) +* the handedness of the coordinate system + +See: http://en.wikipedia.org/wiki/Rotation_matrix#Ambiguities + +We are using the following conventions: + +* axes `i`, `j`, `k` are the `z`, `y`, and `x` axes respectively. Thus + an Euler angle vector [ :math:`alpha`, :math:`beta`. :math:`gamma` ] + in our convention implies a :math:`alpha` radian rotation around the + `z` axis, followed by a :math:`beta` rotation around the `y` axis, + followed by a :math:`gamma` rotation around the `x` axis. +* the rotation matrix applies on the left, to column vectors on the + right, so if `R` is the rotation matrix, and `v` is a 3 x N matrix + with N column vectors, the transformed vector set `vdash` is given by + ``vdash = np.dot(R, v)``. +* extrinsic rotations - the axes are fixed, and do not move with the + rotations. +* a right-handed coordinate system + +The convention of rotation around ``z``, followed by rotation around +``y``, followed by rotation around ``x``, is known (confusingly) as +"xyz", pitch-roll-yaw, Cardan angles, or Tait-Bryan angles. +''' + +import math + +import sys +if sys.version_info >= (3,0): + from functools import reduce + +import numpy as np + + +_FLOAT_EPS_4 = np.finfo(float).eps * 4.0 + + +def euler2mat(z=0, y=0, x=0): + ''' Return matrix for rotations around z, y and x axes + + Uses the z, then y, then x convention above + + Parameters + ---------- + z : scalar + Rotation angle in radians around z-axis (performed first) + y : scalar + Rotation angle in radians around y-axis + x : scalar + Rotation angle in radians around x-axis (performed last) + + Returns + ------- + M : array shape (3,3) + Rotation matrix giving same rotation as for given angles + + Examples + -------- + >>> zrot = 1.3 # radians + >>> yrot = -0.1 + >>> xrot = 0.2 + >>> M = euler2mat(zrot, yrot, xrot) + >>> M.shape == (3, 3) + True + + The output rotation matrix is equal to the composition of the + individual rotations + + >>> M1 = euler2mat(zrot) + >>> M2 = euler2mat(0, yrot) + >>> M3 = euler2mat(0, 0, xrot) + >>> composed_M = np.dot(M3, np.dot(M2, M1)) + >>> np.allclose(M, composed_M) + True + + You can specify rotations by named arguments + + >>> np.all(M3 == euler2mat(x=xrot)) + True + + When applying M to a vector, the vector should column vector to the + right of M. If the right hand side is a 2D array rather than a + vector, then each column of the 2D array represents a vector. + + >>> vec = np.array([1, 0, 0]).reshape((3,1)) + >>> v2 = np.dot(M, vec) + >>> vecs = np.array([[1, 0, 0],[0, 1, 0]]).T # giving 3x2 array + >>> vecs2 = np.dot(M, vecs) + + Rotations are counter-clockwise. + + >>> zred = np.dot(euler2mat(z=np.pi/2), np.eye(3)) + >>> np.allclose(zred, [[0, -1, 0],[1, 0, 0], [0, 0, 1]]) + True + >>> yred = np.dot(euler2mat(y=np.pi/2), np.eye(3)) + >>> np.allclose(yred, [[0, 0, 1],[0, 1, 0], [-1, 0, 0]]) + True + >>> xred = np.dot(euler2mat(x=np.pi/2), np.eye(3)) + >>> np.allclose(xred, [[1, 0, 0],[0, 0, -1], [0, 1, 0]]) + True + + Notes + ----- + The direction of rotation is given by the right-hand rule (orient + the thumb of the right hand along the axis around which the rotation + occurs, with the end of the thumb at the positive end of the axis; + curl your fingers; the direction your fingers curl is the direction + of rotation). Therefore, the rotations are counterclockwise if + looking along the axis of rotation from positive to negative. + ''' + Ms = [] + if z: + cosz = math.cos(z) + sinz = math.sin(z) + Ms.append(np.array( + [[cosz, -sinz, 0], + [sinz, cosz, 0], + [0, 0, 1]])) + if y: + cosy = math.cos(y) + siny = math.sin(y) + Ms.append(np.array( + [[cosy, 0, siny], + [0, 1, 0], + [-siny, 0, cosy]])) + if x: + cosx = math.cos(x) + sinx = math.sin(x) + Ms.append(np.array( + [[1, 0, 0], + [0, cosx, -sinx], + [0, sinx, cosx]])) + if Ms: + return reduce(np.dot, Ms[::-1]) + return np.eye(3) + + +def mat2euler(M, cy_thresh=None): + ''' Discover Euler angle vector from 3x3 matrix + + Uses the conventions above. + + Parameters + ---------- + M : array-like, shape (3,3) + cy_thresh : None or scalar, optional + threshold below which to give up on straightforward arctan for + estimating x rotation. If None (default), estimate from + precision of input. + + Returns + ------- + z : scalar + y : scalar + x : scalar + Rotations in radians around z, y, x axes, respectively + + Notes + ----- + If there was no numerical error, the routine could be derived using + Sympy expression for z then y then x rotation matrix, which is:: + + [ cos(y)*cos(z), -cos(y)*sin(z), sin(y)], + [cos(x)*sin(z) + cos(z)*sin(x)*sin(y), cos(x)*cos(z) - sin(x)*sin(y)*sin(z), -cos(y)*sin(x)], + [sin(x)*sin(z) - cos(x)*cos(z)*sin(y), cos(z)*sin(x) + cos(x)*sin(y)*sin(z), cos(x)*cos(y)] + + with the obvious derivations for z, y, and x + + z = atan2(-r12, r11) + y = asin(r13) + x = atan2(-r23, r33) + + Problems arise when cos(y) is close to zero, because both of:: + + z = atan2(cos(y)*sin(z), cos(y)*cos(z)) + x = atan2(cos(y)*sin(x), cos(x)*cos(y)) + + will be close to atan2(0, 0), and highly unstable. + + The ``cy`` fix for numerical instability below is from: *Graphics + Gems IV*, Paul Heckbert (editor), Academic Press, 1994, ISBN: + 0123361559. Specifically it comes from EulerAngles.c by Ken + Shoemake, and deals with the case where cos(y) is close to zero: + + See: http://www.graphicsgems.org/ + + The code appears to be licensed (from the website) as "can be used + without restrictions". + ''' + M = np.asarray(M) + if cy_thresh is None: + try: + cy_thresh = np.finfo(M.dtype).eps * 4 + except ValueError: + cy_thresh = _FLOAT_EPS_4 + r11, r12, r13, r21, r22, r23, r31, r32, r33 = M.flat + # cy: sqrt((cos(y)*cos(z))**2 + (cos(x)*cos(y))**2) + cy = math.sqrt(r33*r33 + r23*r23) + if cy > cy_thresh: # cos(y) not close to zero, standard form + z = math.atan2(-r12, r11) # atan2(cos(y)*sin(z), cos(y)*cos(z)) + y = math.atan2(r13, cy) # atan2(sin(y), cy) + x = math.atan2(-r23, r33) # atan2(cos(y)*sin(x), cos(x)*cos(y)) + else: # cos(y) (close to) zero, so x -> 0.0 (see above) + # so r21 -> sin(z), r22 -> cos(z) and + z = math.atan2(r21, r22) + y = math.atan2(r13, cy) # atan2(sin(y), cy) + x = 0.0 + return z, y, x + + +def euler2quat(z=0, y=0, x=0): + ''' Return quaternion corresponding to these Euler angles + + Uses the z, then y, then x convention above + + Parameters + ---------- + z : scalar + Rotation angle in radians around z-axis (performed first) + y : scalar + Rotation angle in radians around y-axis + x : scalar + Rotation angle in radians around x-axis (performed last) + + Returns + ------- + quat : array shape (4,) + Quaternion in w, x, y z (real, then vector) format + + Notes + ----- + We can derive this formula in Sympy using: + + 1. Formula giving quaternion corresponding to rotation of theta radians + about arbitrary axis: + http://mathworld.wolfram.com/EulerParameters.html + 2. Generated formulae from 1.) for quaternions corresponding to + theta radians rotations about ``x, y, z`` axes + 3. Apply quaternion multiplication formula - + http://en.wikipedia.org/wiki/Quaternions#Hamilton_product - to + formulae from 2.) to give formula for combined rotations. + ''' + z = z/2.0 + y = y/2.0 + x = x/2.0 + cz = math.cos(z) + sz = math.sin(z) + cy = math.cos(y) + sy = math.sin(y) + cx = math.cos(x) + sx = math.sin(x) + return np.array([ + cx*cy*cz - sx*sy*sz, + cx*sy*sz + cy*cz*sx, + cx*cz*sy - sx*cy*sz, + cx*cy*sz + sx*cz*sy]) + + +def quat2euler(q): + ''' Return Euler angles corresponding to quaternion `q` + + Parameters + ---------- + q : 4 element sequence + w, x, y, z of quaternion + + Returns + ------- + z : scalar + Rotation angle in radians around z-axis (performed first) + y : scalar + Rotation angle in radians around y-axis + x : scalar + Rotation angle in radians around x-axis (performed last) + + Notes + ----- + It's possible to reduce the amount of calculation a little, by + combining parts of the ``quat2mat`` and ``mat2euler`` functions, but + the reduction in computation is small, and the code repetition is + large. + ''' + # delayed import to avoid cyclic dependencies + import nibabel.quaternions as nq + return mat2euler(nq.quat2mat(q)) + + +def euler2angle_axis(z=0, y=0, x=0): + ''' Return angle, axis corresponding to these Euler angles + + Uses the z, then y, then x convention above + + Parameters + ---------- + z : scalar + Rotation angle in radians around z-axis (performed first) + y : scalar + Rotation angle in radians around y-axis + x : scalar + Rotation angle in radians around x-axis (performed last) + + Returns + ------- + theta : scalar + angle of rotation + vector : array shape (3,) + axis around which rotation occurs + + Examples + -------- + >>> theta, vec = euler2angle_axis(0, 1.5, 0) + >>> print(theta) + 1.5 + >>> np.allclose(vec, [0, 1, 0]) + True + ''' + # delayed import to avoid cyclic dependencies + import nibabel.quaternions as nq + return nq.quat2angle_axis(euler2quat(z, y, x)) + + +def angle_axis2euler(theta, vector, is_normalized=False): + ''' Convert angle, axis pair to Euler angles + + Parameters + ---------- + theta : scalar + angle of rotation + vector : 3 element sequence + vector specifying axis for rotation. + is_normalized : bool, optional + True if vector is already normalized (has norm of 1). Default + False + + Returns + ------- + z : scalar + y : scalar + x : scalar + Rotations in radians around z, y, x axes, respectively + + Examples + -------- + >>> z, y, x = angle_axis2euler(0, [1, 0, 0]) + >>> np.allclose((z, y, x), 0) + True + + Notes + ----- + It's possible to reduce the amount of calculation a little, by + combining parts of the ``angle_axis2mat`` and ``mat2euler`` + functions, but the reduction in computation is small, and the code + repetition is large. + ''' + # delayed import to avoid cyclic dependencies + import nibabel.quaternions as nq + M = nq.angle_axis2mat(theta, vector, is_normalized) + return mat2euler(M) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/pc_util.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/pc_util.py" new file mode 100644 index 0000000..81f63d8 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/pc_util.py" @@ -0,0 +1,315 @@ +""" Utility functions for processing point clouds. + +Author: Charles R. Qi, Hao Su +Date: November 2016 +""" + +import os +import sys +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) + +# Draw point cloud +from eulerangles import euler2mat + +# Point cloud IO +import numpy as np +from plyfile import PlyData, PlyElement + + +# ---------------------------------------- +# Point Cloud/Volume Conversions +# ---------------------------------------- + +def point_cloud_to_volume_batch(point_clouds, vsize=12, radius=1.0, flatten=True): + """ Input is BxNx3 batch of point cloud + Output is Bx(vsize^3) + """ + vol_list = [] + for b in range(point_clouds.shape[0]): + vol = point_cloud_to_volume(np.squeeze(point_clouds[b,:,:]), vsize, radius) + if flatten: + vol_list.append(vol.flatten()) + else: + vol_list.append(np.expand_dims(np.expand_dims(vol, -1), 0)) + if flatten: + return np.vstack(vol_list) + else: + return np.concatenate(vol_list, 0) + + +def point_cloud_to_volume(points, vsize, radius=1.0): + """ input is Nx3 points. + output is vsize*vsize*vsize + assumes points are in range [-radius, radius] + """ + vol = np.zeros((vsize,vsize,vsize)) + voxel = 2*radius/float(vsize) + locations = (points + radius)/voxel + locations = locations.astype(int) + vol[locations[:,0],locations[:,1],locations[:,2]] = 1.0 + return vol + +#a = np.zeros((16,1024,3)) +#print point_cloud_to_volume_batch(a, 12, 1.0, False).shape + +def volume_to_point_cloud(vol): + """ vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize + return Nx3 numpy array. + """ + vsize = vol.shape[0] + assert(vol.shape[1] == vsize and vol.shape[1] == vsize) + points = [] + for a in range(vsize): + for b in range(vsize): + for c in range(vsize): + if vol[a,b,c] == 1: + points.append(np.array([a,b,c])) + if len(points) == 0: + return np.zeros((0,3)) + points = np.vstack(points) + return points + +def point_cloud_to_volume_v2_batch(point_clouds, vsize=12, radius=1.0, num_sample=128): + """ Input is BxNx3 a batch of point cloud + Output is BxVxVxVxnum_samplex3 + Added on Feb 19 + """ + vol_list = [] + for b in range(point_clouds.shape[0]): + vol = point_cloud_to_volume_v2(point_clouds[b,:,:], vsize, radius, num_sample) + vol_list.append(np.expand_dims(vol, 0)) + return np.concatenate(vol_list, 0) + +def point_cloud_to_volume_v2(points, vsize, radius=1.0, num_sample=128): + """ input is Nx3 points + output is vsize*vsize*vsize*num_sample*3 + assumes points are in range [-radius, radius] + samples num_sample points in each voxel, if there are less than + num_sample points, replicate the points + Added on Feb 19 + """ + vol = np.zeros((vsize,vsize,vsize,num_sample,3)) + voxel = 2*radius/float(vsize) + locations = (points + radius)/voxel + locations = locations.astype(int) + loc2pc = {} + for n in range(points.shape[0]): + loc = tuple(locations[n,:]) + if loc not in loc2pc: + loc2pc[loc] = [] + loc2pc[loc].append(points[n,:]) + #print loc2pc + + for i in range(vsize): + for j in range(vsize): + for k in range(vsize): + if (i,j,k) not in loc2pc: + vol[i,j,k,:,:] = np.zeros((num_sample,3)) + else: + pc = loc2pc[(i,j,k)] # a list of (3,) arrays + pc = np.vstack(pc) # kx3 + # Sample/pad to num_sample points + if pc.shape[0]>num_sample: + choices = np.random.choice(pc.shape[0], num_sample, replace=False) + pc = pc[choices,:] + elif pc.shape[0]num_sample: + choices = np.random.choice(pc.shape[0], num_sample, replace=False) + pc = pc[choices,:] + elif pc.shape[0] 0) + dx = mask[:, 0] + dy = mask[:, 1] + dv = disk[disk > 0] + + # Order points by z-buffer + zorder = np.argsort(points[:, 2]) + points = points[zorder, :] + points[:, 2] = (points[:, 2] - np.min(points[:, 2])) / (np.max(points[:, 2] - np.min(points[:, 2]))) + max_depth = np.max(points[:, 2]) + + for i in range(points.shape[0]): + j = points.shape[0] - i - 1 + x = points[j, 0] + y = points[j, 1] + xc = canvasSize/2 + (x*space) + yc = canvasSize/2 + (y*space) + xc = int(np.round(xc)) + yc = int(np.round(yc)) + + px = dx + xc + py = dy + yc + + image[px, py] = image[px, py] * 0.7 + dv * (max_depth - points[j, 2]) * 0.3 + + image = image / np.max(image) + return image + +def point_cloud_three_views(points): + """ input points Nx3 numpy array (+y is up direction). + return an numpy array gray image of size 500x1500. """ + # +y is up direction + # xrot is azimuth + # yrot is in-plane + # zrot is elevation + img1 = draw_point_cloud(points, zrot=110/180.0*np.pi, xrot=45/180.0*np.pi, yrot=0/180.0*np.pi) + img2 = draw_point_cloud(points, zrot=70/180.0*np.pi, xrot=135/180.0*np.pi, yrot=0/180.0*np.pi) + img3 = draw_point_cloud(points, zrot=180.0/180.0*np.pi, xrot=90/180.0*np.pi, yrot=0/180.0*np.pi) + image_large = np.concatenate([img1, img2, img3], 1) + return image_large + + +def point_cloud_three_views_demo(): + """ Demo for draw_point_cloud function """ + from PIL import Image + points = read_ply('../third_party/mesh_sampling/piano.ply') + im_array = point_cloud_three_views(points) + img = Image.fromarray(np.uint8(im_array*255.0)) + img.save('piano.jpg') + +if __name__=="__main__": + point_cloud_three_views_demo() + + +def pyplot_draw_point_cloud(points, output_filename): + """ points is a Nx3 numpy array """ + import matplotlib.pyplot as plt + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + ax.scatter(points[:,0], points[:,1], points[:,2]) + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_zlabel('z') + #savefig(output_filename) + +def pyplot_draw_volume(vol, output_filename): + """ vol is of size vsize*vsize*vsize + output an image to output_filename + """ + points = volume_to_point_cloud(vol) + pyplot_draw_point_cloud(points, output_filename) + +def write_ply_color(points, labels, out_filename, num_classes=None): + """ Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file """ + import matplotlib.pyplot as pyplot + labels = labels.astype(int) + N = points.shape[0] + if num_classes is None: + num_classes = np.max(labels)+1 + else: + assert(num_classes>np.max(labels)) + fout = open(out_filename, 'w') + #colors = [pyplot.cm.hsv(i/float(num_classes)) for i in range(num_classes)] + colors = [pyplot.cm.jet(i/float(num_classes)) for i in range(num_classes)] + for i in range(N): + c = colors[labels[i]] + c = [int(x*255) for x in c] + fout.write('v %f %f %f %d %d %d\n' % (points[i,0],points[i,1],points[i,2],c[0],c[1],c[2])) + fout.close() diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/plyfile.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/plyfile.py" new file mode 100644 index 0000000..69c2aa9 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/plyfile.py" @@ -0,0 +1,916 @@ +# Copyright 2014 Darsh Ranjan +# +# This file is part of python-plyfile. +# +# python-plyfile is free software: you can redistribute it and/or +# modify it under the terms of the GNU General Public License as +# published by the Free Software Foundation, either version 3 of the +# License, or (at your option) any later version. +# +# python-plyfile is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with python-plyfile. If not, see +# . + +from itertools import islice as _islice + +import numpy as _np +from sys import byteorder as _byteorder + + +try: + _range = xrange +except NameError: + _range = range + + +# Many-many relation +_data_type_relation = [ + ('int8', 'i1'), + ('char', 'i1'), + ('uint8', 'u1'), + ('uchar', 'b1'), + ('uchar', 'u1'), + ('int16', 'i2'), + ('short', 'i2'), + ('uint16', 'u2'), + ('ushort', 'u2'), + ('int32', 'i4'), + ('int', 'i4'), + ('uint32', 'u4'), + ('uint', 'u4'), + ('float32', 'f4'), + ('float', 'f4'), + ('float64', 'f8'), + ('double', 'f8') +] + +_data_types = dict(_data_type_relation) +_data_type_reverse = dict((b, a) for (a, b) in _data_type_relation) + +_types_list = [] +_types_set = set() +for (_a, _b) in _data_type_relation: + if _a not in _types_set: + _types_list.append(_a) + _types_set.add(_a) + if _b not in _types_set: + _types_list.append(_b) + _types_set.add(_b) + + +_byte_order_map = { + 'ascii': '=', + 'binary_little_endian': '<', + 'binary_big_endian': '>' +} + +_byte_order_reverse = { + '<': 'binary_little_endian', + '>': 'binary_big_endian' +} + +_native_byte_order = {'little': '<', 'big': '>'}[_byteorder] + + +def _lookup_type(type_str): + if type_str not in _data_type_reverse: + try: + type_str = _data_types[type_str] + except KeyError: + raise ValueError("field type %r not in %r" % + (type_str, _types_list)) + + return _data_type_reverse[type_str] + + +def _split_line(line, n): + fields = line.split(None, n) + if len(fields) == n: + fields.append('') + + assert len(fields) == n + 1 + + return fields + + +def make2d(array, cols=None, dtype=None): + ''' + Make a 2D array from an array of arrays. The `cols' and `dtype' + arguments can be omitted if the array is not empty. + + ''' + if (cols is None or dtype is None) and not len(array): + raise RuntimeError("cols and dtype must be specified for empty " + "array") + + if cols is None: + cols = len(array[0]) + + if dtype is None: + dtype = array[0].dtype + + return _np.fromiter(array, [('_', dtype, (cols,))], + count=len(array))['_'] + + +class PlyParseError(Exception): + + ''' + Raised when a PLY file cannot be parsed. + + The attributes `element', `row', `property', and `message' give + additional information. + + ''' + + def __init__(self, message, element=None, row=None, prop=None): + self.message = message + self.element = element + self.row = row + self.prop = prop + + s = '' + if self.element: + s += 'element %r: ' % self.element.name + if self.row is not None: + s += 'row %d: ' % self.row + if self.prop: + s += 'property %r: ' % self.prop.name + s += self.message + + Exception.__init__(self, s) + + def __repr__(self): + return ('PlyParseError(%r, element=%r, row=%r, prop=%r)' % + self.message, self.element, self.row, self.prop) + + +class PlyData(object): + + ''' + PLY file header and data. + + A PlyData instance is created in one of two ways: by the static + method PlyData.read (to read a PLY file), or directly from __init__ + given a sequence of elements (which can then be written to a PLY + file). + + ''' + + def __init__(self, elements=[], text=False, byte_order='=', + comments=[], obj_info=[]): + ''' + elements: sequence of PlyElement instances. + + text: whether the resulting PLY file will be text (True) or + binary (False). + + byte_order: '<' for little-endian, '>' for big-endian, or '=' + for native. This is only relevant if `text' is False. + + comments: sequence of strings that will be placed in the header + between the 'ply' and 'format ...' lines. + + obj_info: like comments, but will be placed in the header with + "obj_info ..." instead of "comment ...". + + ''' + if byte_order == '=' and not text: + byte_order = _native_byte_order + + self.byte_order = byte_order + self.text = text + + self.comments = list(comments) + self.obj_info = list(obj_info) + self.elements = elements + + def _get_elements(self): + return self._elements + + def _set_elements(self, elements): + self._elements = tuple(elements) + self._index() + + elements = property(_get_elements, _set_elements) + + def _get_byte_order(self): + return self._byte_order + + def _set_byte_order(self, byte_order): + if byte_order not in ['<', '>', '=']: + raise ValueError("byte order must be '<', '>', or '='") + + self._byte_order = byte_order + + byte_order = property(_get_byte_order, _set_byte_order) + + def _index(self): + self._element_lookup = dict((elt.name, elt) for elt in + self._elements) + if len(self._element_lookup) != len(self._elements): + raise ValueError("two elements with same name") + + @staticmethod + def _parse_header(stream): + ''' + Parse a PLY header from a readable file-like stream. + + ''' + lines = [] + comments = {'comment': [], 'obj_info': []} + while True: + line = stream.readline().decode('ascii').strip() + fields = _split_line(line, 1) + + if fields[0] == 'end_header': + break + + elif fields[0] in comments.keys(): + lines.append(fields) + else: + lines.append(line.split()) + + a = 0 + if lines[a] != ['ply']: + raise PlyParseError("expected 'ply'") + + a += 1 + while lines[a][0] in comments.keys(): + comments[lines[a][0]].append(lines[a][1]) + a += 1 + + if lines[a][0] != 'format': + raise PlyParseError("expected 'format'") + + if lines[a][2] != '1.0': + raise PlyParseError("expected version '1.0'") + + if len(lines[a]) != 3: + raise PlyParseError("too many fields after 'format'") + + fmt = lines[a][1] + + if fmt not in _byte_order_map: + raise PlyParseError("don't understand format %r" % fmt) + + byte_order = _byte_order_map[fmt] + text = fmt == 'ascii' + + a += 1 + while a < len(lines) and lines[a][0] in comments.keys(): + comments[lines[a][0]].append(lines[a][1]) + a += 1 + + return PlyData(PlyElement._parse_multi(lines[a:]), + text, byte_order, + comments['comment'], comments['obj_info']) + + @staticmethod + def read(stream): + ''' + Read PLY data from a readable file-like object or filename. + + ''' + (must_close, stream) = _open_stream(stream, 'read') + try: + data = PlyData._parse_header(stream) + for elt in data: + elt._read(stream, data.text, data.byte_order) + finally: + if must_close: + stream.close() + + return data + + def write(self, stream): + ''' + Write PLY data to a writeable file-like object or filename. + + ''' + (must_close, stream) = _open_stream(stream, 'write') + try: + stream.write(self.header.encode('ascii')) + stream.write(b'\r\n') + for elt in self: + elt._write(stream, self.text, self.byte_order) + finally: + if must_close: + stream.close() + + @property + def header(self): + ''' + Provide PLY-formatted metadata for the instance. + + ''' + lines = ['ply'] + + if self.text: + lines.append('format ascii 1.0') + else: + lines.append('format ' + + _byte_order_reverse[self.byte_order] + + ' 1.0') + + # Some information is lost here, since all comments are placed + # between the 'format' line and the first element. + for c in self.comments: + lines.append('comment ' + c) + + for c in self.obj_info: + lines.append('obj_info ' + c) + + lines.extend(elt.header for elt in self.elements) + lines.append('end_header') + return '\r\n'.join(lines) + + def __iter__(self): + return iter(self.elements) + + def __len__(self): + return len(self.elements) + + def __contains__(self, name): + return name in self._element_lookup + + def __getitem__(self, name): + return self._element_lookup[name] + + def __str__(self): + return self.header + + def __repr__(self): + return ('PlyData(%r, text=%r, byte_order=%r, ' + 'comments=%r, obj_info=%r)' % + (self.elements, self.text, self.byte_order, + self.comments, self.obj_info)) + + +def _open_stream(stream, read_or_write): + if hasattr(stream, read_or_write): + return (False, stream) + try: + return (True, open(stream, read_or_write[0] + 'b')) + except TypeError: + raise RuntimeError("expected open file or filename") + + +class PlyElement(object): + + ''' + PLY file element. + + A client of this library doesn't normally need to instantiate this + directly, so the following is only for the sake of documenting the + internals. + + Creating a PlyElement instance is generally done in one of two ways: + as a byproduct of PlyData.read (when reading a PLY file) and by + PlyElement.describe (before writing a PLY file). + + ''' + + def __init__(self, name, properties, count, comments=[]): + ''' + This is not part of the public interface. The preferred methods + of obtaining PlyElement instances are PlyData.read (to read from + a file) and PlyElement.describe (to construct from a numpy + array). + + ''' + self._name = str(name) + self._check_name() + self._count = count + + self._properties = tuple(properties) + self._index() + + self.comments = list(comments) + + self._have_list = any(isinstance(p, PlyListProperty) + for p in self.properties) + + @property + def count(self): + return self._count + + def _get_data(self): + return self._data + + def _set_data(self, data): + self._data = data + self._count = len(data) + self._check_sanity() + + data = property(_get_data, _set_data) + + def _check_sanity(self): + for prop in self.properties: + if prop.name not in self._data.dtype.fields: + raise ValueError("dangling property %r" % prop.name) + + def _get_properties(self): + return self._properties + + def _set_properties(self, properties): + self._properties = tuple(properties) + self._check_sanity() + self._index() + + properties = property(_get_properties, _set_properties) + + def _index(self): + self._property_lookup = dict((prop.name, prop) + for prop in self._properties) + if len(self._property_lookup) != len(self._properties): + raise ValueError("two properties with same name") + + def ply_property(self, name): + return self._property_lookup[name] + + @property + def name(self): + return self._name + + def _check_name(self): + if any(c.isspace() for c in self._name): + msg = "element name %r contains spaces" % self._name + raise ValueError(msg) + + def dtype(self, byte_order='='): + ''' + Return the numpy dtype of the in-memory representation of the + data. (If there are no list properties, and the PLY format is + binary, then this also accurately describes the on-disk + representation of the element.) + + ''' + return [(prop.name, prop.dtype(byte_order)) + for prop in self.properties] + + @staticmethod + def _parse_multi(header_lines): + ''' + Parse a list of PLY element definitions. + + ''' + elements = [] + while header_lines: + (elt, header_lines) = PlyElement._parse_one(header_lines) + elements.append(elt) + + return elements + + @staticmethod + def _parse_one(lines): + ''' + Consume one element definition. The unconsumed input is + returned along with a PlyElement instance. + + ''' + a = 0 + line = lines[a] + + if line[0] != 'element': + raise PlyParseError("expected 'element'") + if len(line) > 3: + raise PlyParseError("too many fields after 'element'") + if len(line) < 3: + raise PlyParseError("too few fields after 'element'") + + (name, count) = (line[1], int(line[2])) + + comments = [] + properties = [] + while True: + a += 1 + if a >= len(lines): + break + + if lines[a][0] == 'comment': + comments.append(lines[a][1]) + elif lines[a][0] == 'property': + properties.append(PlyProperty._parse_one(lines[a])) + else: + break + + return (PlyElement(name, properties, count, comments), + lines[a:]) + + @staticmethod + def describe(data, name, len_types={}, val_types={}, + comments=[]): + ''' + Construct a PlyElement from an array's metadata. + + len_types and val_types can be given as mappings from list + property names to type strings (like 'u1', 'f4', etc., or + 'int8', 'float32', etc.). These can be used to define the length + and value types of list properties. List property lengths + always default to type 'u1' (8-bit unsigned integer), and value + types default to 'i4' (32-bit integer). + + ''' + if not isinstance(data, _np.ndarray): + raise TypeError("only numpy arrays are supported") + + if len(data.shape) != 1: + raise ValueError("only one-dimensional arrays are " + "supported") + + count = len(data) + + properties = [] + descr = data.dtype.descr + + for t in descr: + if not isinstance(t[1], str): + raise ValueError("nested records not supported") + + if not t[0]: + raise ValueError("field with empty name") + + if len(t) != 2 or t[1][1] == 'O': + # non-scalar field, which corresponds to a list + # property in PLY. + + if t[1][1] == 'O': + if len(t) != 2: + raise ValueError("non-scalar object fields not " + "supported") + + len_str = _data_type_reverse[len_types.get(t[0], 'u1')] + if t[1][1] == 'O': + val_type = val_types.get(t[0], 'i4') + val_str = _lookup_type(val_type) + else: + val_str = _lookup_type(t[1][1:]) + + prop = PlyListProperty(t[0], len_str, val_str) + else: + val_str = _lookup_type(t[1][1:]) + prop = PlyProperty(t[0], val_str) + + properties.append(prop) + + elt = PlyElement(name, properties, count, comments) + elt.data = data + + return elt + + def _read(self, stream, text, byte_order): + ''' + Read the actual data from a PLY file. + + ''' + if text: + self._read_txt(stream) + else: + if self._have_list: + # There are list properties, so a simple load is + # impossible. + self._read_bin(stream, byte_order) + else: + # There are no list properties, so loading the data is + # much more straightforward. + self._data = _np.fromfile(stream, + self.dtype(byte_order), + self.count) + + if len(self._data) < self.count: + k = len(self._data) + del self._data + raise PlyParseError("early end-of-file", self, k) + + self._check_sanity() + + def _write(self, stream, text, byte_order): + ''' + Write the data to a PLY file. + + ''' + if text: + self._write_txt(stream) + else: + if self._have_list: + # There are list properties, so serialization is + # slightly complicated. + self._write_bin(stream, byte_order) + else: + # no list properties, so serialization is + # straightforward. + self.data.astype(self.dtype(byte_order), + copy=False).tofile(stream) + + def _read_txt(self, stream): + ''' + Load a PLY element from an ASCII-format PLY file. The element + may contain list properties. + + ''' + self._data = _np.empty(self.count, dtype=self.dtype()) + + k = 0 + for line in _islice(iter(stream.readline, b''), self.count): + fields = iter(line.strip().split()) + for prop in self.properties: + try: + self._data[prop.name][k] = prop._from_fields(fields) + except StopIteration: + raise PlyParseError("early end-of-line", + self, k, prop) + except ValueError: + raise PlyParseError("malformed input", + self, k, prop) + try: + next(fields) + except StopIteration: + pass + else: + raise PlyParseError("expected end-of-line", self, k) + k += 1 + + if k < self.count: + del self._data + raise PlyParseError("early end-of-file", self, k) + + def _write_txt(self, stream): + ''' + Save a PLY element to an ASCII-format PLY file. The element may + contain list properties. + + ''' + for rec in self.data: + fields = [] + for prop in self.properties: + fields.extend(prop._to_fields(rec[prop.name])) + + _np.savetxt(stream, [fields], '%.18g', newline='\r\n') + + def _read_bin(self, stream, byte_order): + ''' + Load a PLY element from a binary PLY file. The element may + contain list properties. + + ''' + self._data = _np.empty(self.count, dtype=self.dtype(byte_order)) + + for k in _range(self.count): + for prop in self.properties: + try: + self._data[prop.name][k] = \ + prop._read_bin(stream, byte_order) + except StopIteration: + raise PlyParseError("early end-of-file", + self, k, prop) + + def _write_bin(self, stream, byte_order): + ''' + Save a PLY element to a binary PLY file. The element may + contain list properties. + + ''' + for rec in self.data: + for prop in self.properties: + prop._write_bin(rec[prop.name], stream, byte_order) + + @property + def header(self): + ''' + Format this element's metadata as it would appear in a PLY + header. + + ''' + lines = ['element %s %d' % (self.name, self.count)] + + # Some information is lost here, since all comments are placed + # between the 'element' line and the first property definition. + for c in self.comments: + lines.append('comment ' + c) + + lines.extend(list(map(str, self.properties))) + + return '\r\n'.join(lines) + + def __getitem__(self, key): + return self.data[key] + + def __setitem__(self, key, value): + self.data[key] = value + + def __str__(self): + return self.header + + def __repr__(self): + return ('PlyElement(%r, %r, count=%d, comments=%r)' % + (self.name, self.properties, self.count, + self.comments)) + + +class PlyProperty(object): + + ''' + PLY property description. This class is pure metadata; the data + itself is contained in PlyElement instances. + + ''' + + def __init__(self, name, val_dtype): + self._name = str(name) + self._check_name() + self.val_dtype = val_dtype + + def _get_val_dtype(self): + return self._val_dtype + + def _set_val_dtype(self, val_dtype): + self._val_dtype = _data_types[_lookup_type(val_dtype)] + + val_dtype = property(_get_val_dtype, _set_val_dtype) + + @property + def name(self): + return self._name + + def _check_name(self): + if any(c.isspace() for c in self._name): + msg = "Error: property name %r contains spaces" % self._name + raise RuntimeError(msg) + + @staticmethod + def _parse_one(line): + assert line[0] == 'property' + + if line[1] == 'list': + if len(line) > 5: + raise PlyParseError("too many fields after " + "'property list'") + if len(line) < 5: + raise PlyParseError("too few fields after " + "'property list'") + + return PlyListProperty(line[4], line[2], line[3]) + + else: + if len(line) > 3: + raise PlyParseError("too many fields after " + "'property'") + if len(line) < 3: + raise PlyParseError("too few fields after " + "'property'") + + return PlyProperty(line[2], line[1]) + + def dtype(self, byte_order='='): + ''' + Return the numpy dtype description for this property (as a tuple + of strings). + + ''' + return byte_order + self.val_dtype + + def _from_fields(self, fields): + ''' + Parse from generator. Raise StopIteration if the property could + not be read. + + ''' + return _np.dtype(self.dtype()).type(next(fields)) + + def _to_fields(self, data): + ''' + Return generator over one item. + + ''' + yield _np.dtype(self.dtype()).type(data) + + def _read_bin(self, stream, byte_order): + ''' + Read data from a binary stream. Raise StopIteration if the + property could not be read. + + ''' + try: + return _np.fromfile(stream, self.dtype(byte_order), 1)[0] + except IndexError: + raise StopIteration + + def _write_bin(self, data, stream, byte_order): + ''' + Write data to a binary stream. + + ''' + _np.dtype(self.dtype(byte_order)).type(data).tofile(stream) + + def __str__(self): + val_str = _data_type_reverse[self.val_dtype] + return 'property %s %s' % (val_str, self.name) + + def __repr__(self): + return 'PlyProperty(%r, %r)' % (self.name, + _lookup_type(self.val_dtype)) + + +class PlyListProperty(PlyProperty): + + ''' + PLY list property description. + + ''' + + def __init__(self, name, len_dtype, val_dtype): + PlyProperty.__init__(self, name, val_dtype) + + self.len_dtype = len_dtype + + def _get_len_dtype(self): + return self._len_dtype + + def _set_len_dtype(self, len_dtype): + self._len_dtype = _data_types[_lookup_type(len_dtype)] + + len_dtype = property(_get_len_dtype, _set_len_dtype) + + def dtype(self, byte_order='='): + ''' + List properties always have a numpy dtype of "object". + + ''' + return '|O' + + def list_dtype(self, byte_order='='): + ''' + Return the pair (len_dtype, val_dtype) (both numpy-friendly + strings). + + ''' + return (byte_order + self.len_dtype, + byte_order + self.val_dtype) + + def _from_fields(self, fields): + (len_t, val_t) = self.list_dtype() + + n = int(_np.dtype(len_t).type(next(fields))) + + data = _np.loadtxt(list(_islice(fields, n)), val_t, ndmin=1) + if len(data) < n: + raise StopIteration + + return data + + def _to_fields(self, data): + ''' + Return generator over the (numerical) PLY representation of the + list data (length followed by actual data). + + ''' + (len_t, val_t) = self.list_dtype() + + data = _np.asarray(data, dtype=val_t).ravel() + + yield _np.dtype(len_t).type(data.size) + for x in data: + yield x + + def _read_bin(self, stream, byte_order): + (len_t, val_t) = self.list_dtype(byte_order) + + try: + n = _np.fromfile(stream, len_t, 1)[0] + except IndexError: + raise StopIteration + + data = _np.fromfile(stream, val_t, n) + if len(data) < n: + raise StopIteration + + return data + + def _write_bin(self, data, stream, byte_order): + ''' + Write data to a binary stream. + + ''' + (len_t, val_t) = self.list_dtype(byte_order) + + data = _np.asarray(data, dtype=val_t).ravel() + + _np.array(data.size, dtype=len_t).tofile(stream) + data.tofile(stream) + + def __str__(self): + len_str = _data_type_reverse[self.len_dtype] + val_str = _data_type_reverse[self.val_dtype] + return 'property list %s %s %s' % (len_str, val_str, self.name) + + def __repr__(self): + return ('PlyListProperty(%r, %r, %r)' % + (self.name, + _lookup_type(self.len_dtype), + _lookup_type(self.val_dtype))) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/pointnet_util.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/pointnet_util.py" new file mode 100644 index 0000000..88ff701 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/pointnet_util.py" @@ -0,0 +1,230 @@ +""" PointNet++ Layers +Author: Charles R. Qi +Date: November 2017 +""" +import os +import sys +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = os.path.dirname(BASE_DIR) +sys.path.append(os.path.join(ROOT_DIR, 'utils')) +sys.path.append(os.path.join(ROOT_DIR, 'tf_ops/sampling')) +sys.path.append(os.path.join(ROOT_DIR, 'tf_ops/grouping')) +sys.path.append(os.path.join(ROOT_DIR, 'tf_ops/3d_interpolation')) +from tf_sampling import farthest_point_sample, gather_point +from tf_grouping import query_ball_point, group_point, knn_point +from tf_interpolate import three_nn, three_interpolate +import tensorflow as tf + +import numpy as np +import tf_util + +def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True): + ''' + Input: + npoint: int32 + radius: float32 + nsample: int32 + xyz: (batch_size, ndataset, 3) TF tensor + points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points + knn: bool, if True use kNN instead of radius search + use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features + Output: + new_xyz: (batch_size, npoint, 3) TF tensor + new_points: (batch_size, npoint, nsample, 3+channel) TF tensor + idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points + grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs + (subtracted by seed point XYZ) in local regions + ''' + + new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) # (batch_size, npoint, 3) + if knn: + _,idx = knn_point(nsample, xyz, new_xyz) + else: + idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz) + grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3) + grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization + if points is not None: + grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel) + if use_xyz: + new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel) + else: + new_points = grouped_points + else: + new_points = grouped_xyz + + return new_xyz, new_points, idx, grouped_xyz + + +def sample_and_group_all(xyz, points, use_xyz=True): + ''' + Inputs: + xyz: (batch_size, ndataset, 3) TF tensor + points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points + use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features + Outputs: + new_xyz: (batch_size, 1, 3) as (0,0,0) + new_points: (batch_size, 1, ndataset, 3+channel) TF tensor + Note: + Equivalent to sample_and_group with npoint=1, radius=inf, use (0,0,0) as the centroid + ''' + batch_size = xyz.get_shape()[0].value + nsample = xyz.get_shape()[1].value + new_xyz = tf.constant(np.tile(np.array([0,0,0]).reshape((1,1,3)), (batch_size,1,1)),dtype=tf.float32) # (batch_size, 1, 3) + idx = tf.constant(np.tile(np.array(range(nsample)).reshape((1,1,nsample)), (batch_size,1,1))) + grouped_xyz = tf.reshape(xyz, (batch_size, 1, nsample, 3)) # (batch_size, npoint=1, nsample, 3) + if points is not None: + if use_xyz: + new_points = tf.concat([xyz, points], axis=2) # (batch_size, 16, 259) + else: + new_points = points + new_points = tf.expand_dims(new_points, 1) # (batch_size, 1, 16, 259) + else: + new_points = grouped_xyz + return new_xyz, new_points, idx, grouped_xyz + + +def pointnet_sa_module(xyz, points, npoint, radius, nsample, mlp, mlp2, group_all, is_training, bn_decay, scope, bn=True, pooling='max', knn=False, use_xyz=True, use_nchw=False): + ''' PointNet Set Abstraction (SA) Module + Input: + xyz: (batch_size, ndataset, 3) TF tensor + points: (batch_size, ndataset, channel) TF tensor + npoint: int32 -- #points sampled in farthest point sampling + radius: float32 -- search radius in local region + nsample: int32 -- how many points in each local region + mlp: list of int32 -- output size for MLP on each point + mlp2: list of int32 -- output size for MLP on each region + group_all: bool -- group all points into one PC if set true, OVERRIDE + npoint, radius and nsample settings + use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features + use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format + Return: + new_xyz: (batch_size, npoint, 3) TF tensor + new_points: (batch_size, npoint, mlp[-1] or mlp2[-1]) TF tensor + idx: (batch_size, npoint, nsample) int32 -- indices for local regions + ''' + data_format = 'NCHW' if use_nchw else 'NHWC' + with tf.variable_scope(scope) as sc: + # Sample and Grouping + if group_all: + nsample = xyz.get_shape()[1].value + new_xyz, new_points, idx, grouped_xyz = sample_and_group_all(xyz, points, use_xyz) + else: + new_xyz, new_points, idx, grouped_xyz = sample_and_group(npoint, radius, nsample, xyz, points, knn, use_xyz) + + # Point Feature Embedding + if use_nchw: new_points = tf.transpose(new_points, [0,3,1,2]) + for i, num_out_channel in enumerate(mlp): + new_points = tf_util.conv2d(new_points, num_out_channel, [1,1], + padding='VALID', stride=[1,1], + bn=bn, is_training=is_training, + scope='conv%d'%(i), bn_decay=bn_decay, + data_format=data_format) + if use_nchw: new_points = tf.transpose(new_points, [0,2,3,1]) + + # Pooling in Local Regions + if pooling=='max': + new_points = tf.reduce_max(new_points, axis=[2], keepdims=True, name='maxpool') + elif pooling=='avg': + new_points = tf.reduce_mean(new_points, axis=[2], keepdims=True, name='avgpool') + elif pooling=='weighted_avg': + with tf.variable_scope('weighted_avg'): + dists = tf.norm(grouped_xyz,axis=-1,ord=2,keepdims=True) + exp_dists = tf.exp(-dists * 5) + weights = exp_dists/tf.reduce_sum(exp_dists,axis=2,keepdims=True) # (batch_size, npoint, nsample, 1) + new_points *= weights # (batch_size, npoint, nsample, mlp[-1]) + new_points = tf.reduce_sum(new_points, axis=2, keepdims=True) + elif pooling=='max_and_avg': + max_points = tf.reduce_max(new_points, axis=[2], keepdims=True, name='maxpool') + avg_points = tf.reduce_mean(new_points, axis=[2], keepdims=True, name='avgpool') + new_points = tf.concat([avg_points, max_points], axis=-1) + + # [Optional] Further Processing + if mlp2 is not None: + if use_nchw: new_points = tf.transpose(new_points, [0,3,1,2]) + for i, num_out_channel in enumerate(mlp2): + new_points = tf_util.conv2d(new_points, num_out_channel, [1,1], + padding='VALID', stride=[1,1], + bn=bn, is_training=is_training, + scope='conv_post_%d'%(i), bn_decay=bn_decay, + data_format=data_format) + if use_nchw: new_points = tf.transpose(new_points, [0,2,3,1]) + + new_points = tf.squeeze(new_points, [2]) # (batch_size, npoints, mlp2[-1]) + return new_xyz, new_points, idx + +def pointnet_sa_module_msg(xyz, points, npoint, radius_list, nsample_list, mlp_list, is_training, bn_decay, scope, bn=True, use_xyz=True, use_nchw=False): + ''' PointNet Set Abstraction (SA) module with Multi-Scale Grouping (MSG) + Input: + xyz: (batch_size, ndataset, 3) TF tensor + points: (batch_size, ndataset, channel) TF tensor + npoint: int32 -- #points sampled in farthest point sampling + radius: list of float32 -- search radius in local region + nsample: list of int32 -- how many points in each local region + mlp: list of list of int32 -- output size for MLP on each point + use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features + use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format + Return: + new_xyz: (batch_size, npoint, 3) TF tensor + new_points: (batch_size, npoint, \sum_k{mlp[k][-1]}) TF tensor + ''' + data_format = 'NCHW' if use_nchw else 'NHWC' + with tf.variable_scope(scope) as sc: + new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) + new_points_list = [] + for i in range(len(radius_list)): + radius = radius_list[i] + nsample = nsample_list[i] + idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz) + grouped_xyz = group_point(xyz, idx) + grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) + if points is not None: + grouped_points = group_point(points, idx) + if use_xyz: + grouped_points = tf.concat([grouped_points, grouped_xyz], axis=-1) + else: + grouped_points = grouped_xyz + if use_nchw: grouped_points = tf.transpose(grouped_points, [0,3,1,2]) + for j,num_out_channel in enumerate(mlp_list[i]): + grouped_points = tf_util.conv2d(grouped_points, num_out_channel, [1,1], + padding='VALID', stride=[1,1], bn=bn, is_training=is_training, + scope='conv%d_%d'%(i,j), bn_decay=bn_decay) + if use_nchw: grouped_points = tf.transpose(grouped_points, [0,2,3,1]) + new_points = tf.reduce_max(grouped_points, axis=[2]) + new_points_list.append(new_points) + new_points_concat = tf.concat(new_points_list, axis=-1) + return new_xyz, new_points_concat + + +def pointnet_fp_module(xyz1, xyz2, points1, points2, mlp, is_training, bn_decay, scope, bn=True): + ''' PointNet Feature Propogation (FP) Module + Input: + xyz1: (batch_size, ndataset1, 3) TF tensor + xyz2: (batch_size, ndataset2, 3) TF tensor, sparser than xyz1 + points1: (batch_size, ndataset1, nchannel1) TF tensor + points2: (batch_size, ndataset2, nchannel2) TF tensor + mlp: list of int32 -- output size for MLP on each point + Return: + new_points: (batch_size, ndataset1, mlp[-1]) TF tensor + ''' + with tf.variable_scope(scope) as sc: + dist, idx = three_nn(xyz1, xyz2) + dist = tf.maximum(dist, 1e-10) + #修改， + norm = tf.reduce_sum((1.0/dist),axis=2,keepdims=True) + + norm = tf.tile(norm,[1,1,3]) + weight = (1.0/dist) / norm + interpolated_points = three_interpolate(points2, idx, weight) + + if points1 is not None: + new_points1 = tf.concat(axis=2, values=[interpolated_points, points1]) # B,ndataset1,nchannel1+nchannel2 + else: + new_points1 = interpolated_points + new_points1 = tf.expand_dims(new_points1, 2) + for i, num_out_channel in enumerate(mlp): + new_points1 = tf_util.conv2d(new_points1, num_out_channel, [1,1], + padding='VALID', stride=[1,1], + bn=bn, is_training=is_training, + scope='conv_%d'%(i), bn_decay=bn_decay) + new_points1 = tf.squeeze(new_points1, [2]) # B,ndataset1,mlp[-1] + return new_points1 diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/provider.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/provider.py" new file mode 100644 index 0000000..95118f5 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/provider.py" @@ -0,0 +1,247 @@ +import os +import sys +import numpy as np +import h5py +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) + +def shuffle_data(data, labels): + """ Shuffle data and labels. + Input: + data: B,N,... numpy array + label: B,... numpy array + Return: + shuffled data, label and shuffle indices + """ + idx = np.arange(len(labels)) + np.random.shuffle(idx) + return data[idx, ...], labels[idx], idx + +def shuffle_points(batch_data): + """ Shuffle orders of points in each point cloud -- changes FPS behavior. + Use the same shuffling idx for the entire batch. + Input: + BxNxC array + Output: + BxNxC array + """ + idx = np.arange(batch_data.shape[1]) + np.random.shuffle(idx) + return batch_data[:,idx,:] + +def rotate_point_cloud(batch_data): + """ Randomly rotate the point clouds to augument the dataset + rotation is per shape based along up direction + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, 0, sinval], + [0, 1, 0], + [-sinval, 0, cosval]]) + shape_pc = batch_data[k, ...] + rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + return rotated_data + +def rotate_point_cloud_z(batch_data): + """ Randomly rotate the point clouds to augument the dataset + rotation is per shape based along up direction + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, sinval, 0], + [-sinval, cosval, 0], + [0, 0, 1]]) + shape_pc = batch_data[k, ...] + rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + return rotated_data + +def rotate_point_cloud_with_normal(batch_xyz_normal): + ''' Randomly rotate XYZ, normal point cloud. + Input: + batch_xyz_normal: B,N,6, first three channels are XYZ, last 3 all normal + Output: + B,N,6, rotated XYZ, normal point cloud + ''' + for k in range(batch_xyz_normal.shape[0]): + rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, 0, sinval], + [0, 1, 0], + [-sinval, 0, cosval]]) + shape_pc = batch_xyz_normal[k,:,0:3] + shape_normal = batch_xyz_normal[k,:,3:6] + batch_xyz_normal[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + batch_xyz_normal[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), rotation_matrix) + return batch_xyz_normal + +def rotate_perturbation_point_cloud_with_normal(batch_data, angle_sigma=0.06, angle_clip=0.18): + """ Randomly perturb the point clouds by small rotations + Input: + BxNx6 array, original batch of point clouds and point normals + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip) + Rx = np.array([[1,0,0], + [0,np.cos(angles[0]),-np.sin(angles[0])], + [0,np.sin(angles[0]),np.cos(angles[0])]]) + Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])], + [0,1,0], + [-np.sin(angles[1]),0,np.cos(angles[1])]]) + Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0], + [np.sin(angles[2]),np.cos(angles[2]),0], + [0,0,1]]) + R = np.dot(Rz, np.dot(Ry,Rx)) + shape_pc = batch_data[k,:,0:3] + shape_normal = batch_data[k,:,3:6] + rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), R) + rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), R) + return rotated_data + + +def rotate_point_cloud_by_angle(batch_data, rotation_angle): + """ Rotate the point cloud along up direction with certain angle. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + #rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, 0, sinval], + [0, 1, 0], + [-sinval, 0, cosval]]) + shape_pc = batch_data[k,:,0:3] + rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + return rotated_data + +def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle): + """ Rotate the point cloud along up direction with certain angle. + Input: + BxNx6 array, original batch of point clouds with normal + scalar, angle of rotation + Return: + BxNx6 array, rotated batch of point clouds iwth normal + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + #rotation_angle = np.random.uniform() * 2 * np.pi + cosval = np.cos(rotation_angle) + sinval = np.sin(rotation_angle) + rotation_matrix = np.array([[cosval, 0, sinval], + [0, 1, 0], + [-sinval, 0, cosval]]) + shape_pc = batch_data[k,:,0:3] + shape_normal = batch_data[k,:,3:6] + rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) + rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1,3)), rotation_matrix) + return rotated_data + + + +def rotate_perturbation_point_cloud(batch_data, angle_sigma=0.06, angle_clip=0.18): + """ Randomly perturb the point clouds by small rotations + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, rotated batch of point clouds + """ + rotated_data = np.zeros(batch_data.shape, dtype=np.float32) + for k in range(batch_data.shape[0]): + angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip) + Rx = np.array([[1,0,0], + [0,np.cos(angles[0]),-np.sin(angles[0])], + [0,np.sin(angles[0]),np.cos(angles[0])]]) + Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])], + [0,1,0], + [-np.sin(angles[1]),0,np.cos(angles[1])]]) + Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0], + [np.sin(angles[2]),np.cos(angles[2]),0], + [0,0,1]]) + R = np.dot(Rz, np.dot(Ry,Rx)) + shape_pc = batch_data[k, ...] + rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), R) + return rotated_data + + +def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05): + """ Randomly jitter points. jittering is per point. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, jittered batch of point clouds + """ + B, N, C = batch_data.shape + assert(clip > 0) + jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip) + jittered_data += batch_data + return jittered_data + +def shift_point_cloud(batch_data, shift_range=0.1): + """ Randomly shift point cloud. Shift is per point cloud. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, shifted batch of point clouds + """ + B, N, C = batch_data.shape + shifts = np.random.uniform(-shift_range, shift_range, (B,3)) + for batch_index in range(B): + batch_data[batch_index,:,:] += shifts[batch_index,:] + return batch_data + + +def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25): + """ Randomly scale the point cloud. Scale is per point cloud. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, scaled batch of point clouds + """ + B, N, C = batch_data.shape + scales = np.random.uniform(scale_low, scale_high, B) + for batch_index in range(B): + batch_data[batch_index,:,:] *= scales[batch_index] + return batch_data + +def random_point_dropout(batch_pc, max_dropout_ratio=0.875): + ''' batch_pc: BxNx3 ''' + for b in range(batch_pc.shape[0]): + dropout_ratio = np.random.random()*max_dropout_ratio # 0~0.875 + drop_idx = np.where(np.random.random((batch_pc.shape[1]))<=dropout_ratio)[0] + if len(drop_idx)>0: + batch_pc[b,drop_idx,:] = batch_pc[b,0,:] # set to the first point + return batch_pc + + +def getDataFiles(list_filename): + return [line.rstrip() for line in open(list_filename)] + +def load_h5(h5_filename): + f = h5py.File(h5_filename) + data = f['data'][:] + label = f['label'][:] + return (data, label) + +def loadDataFile(filename): + return load_h5(filename) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/render_balls_so.cpp" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/render_balls_so.cpp" new file mode 100644 index 0000000..e95aeba --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/render_balls_so.cpp" @@ -0,0 +1,58 @@ +#include +#include +#include +#include +using namespace std; + +struct PointInfo{ + int x,y,z; + float r,g,b; +}; + +extern "C"{ + +void render_ball(int h,int w,unsigned char * show,int n,int * xyzs,float * c0,float * c1,float * c2,int r){ + r=max(r,1); + vector depth(h*w,-2100000000); + vector pattern; + for (int dx=-r;dx<=r;dx++) + for (int dy=-r;dy<=r;dy++) + if (dx*dx+dy*dy=h || y2<0 || y2>=w) && depth[x2*w+y2]0: + show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],1,axis=0)) + if magnifyBlue>=2: + show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],-1,axis=0)) + show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],1,axis=1)) + if magnifyBlue>=2: + show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],-1,axis=1)) + if showrot: + cv2.putText(show,'xangle %d'%(int(xangle/np.pi*180)),(30,showsz-30),0,0.5,cv2.cv.CV_RGB(255,0,0)) + cv2.putText(show,'yangle %d'%(int(yangle/np.pi*180)),(30,showsz-50),0,0.5,cv2.cv.CV_RGB(255,0,0)) + cv2.putText(show,'zoom %d%%'%(int(zoom*100)),(30,showsz-70),0,0.5,cv2.cv.CV_RGB(255,0,0)) + changed=True + while True: + if changed: + render() + changed=False + cv2.imshow('show3d',show) + if waittime==0: + cmd=cv2.waitKey(10)%256 + else: + cmd=cv2.waitKey(waittime)%256 + if cmd==ord('q'): + break + elif cmd==ord('Q'): + sys.exit(0) + + if cmd==ord('t') or cmd == ord('p'): + if cmd == ord('t'): + if c_gt is None: + c0=np.zeros((len(xyz),),dtype='float32')+255 + c1=np.zeros((len(xyz),),dtype='float32')+255 + c2=np.zeros((len(xyz),),dtype='float32')+255 + else: + c0=c_gt[:,0] + c1=c_gt[:,1] + c2=c_gt[:,2] + else: + if c_pred is None: + c0=np.zeros((len(xyz),),dtype='float32')+255 + c1=np.zeros((len(xyz),),dtype='float32')+255 + c2=np.zeros((len(xyz),),dtype='float32')+255 + else: + c0=c_pred[:,0] + c1=c_pred[:,1] + c2=c_pred[:,2] + if normalizecolor: + c0/=(c0.max()+1e-14)/255.0 + c1/=(c1.max()+1e-14)/255.0 + c2/=(c2.max()+1e-14)/255.0 + c0=np.require(c0,'float32','C') + c1=np.require(c1,'float32','C') + c2=np.require(c2,'float32','C') + changed = True + + + + if cmd==ord('n'): + zoom*=1.1 + changed=True + elif cmd==ord('m'): + zoom/=1.1 + changed=True + elif cmd==ord('r'): + zoom=1.0 + changed=True + elif cmd==ord('s'): + cv2.imwrite('show3d.png',show) + if waittime!=0: + break + return cmd +if __name__=='__main__': + np.random.seed(100) + showpoints(np.random.randn(2500,3)) + diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/tf_util.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/tf_util.py" new file mode 100644 index 0000000..2f58cb0 --- /dev/null +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/utils/tf_util.py" @@ -0,0 +1,616 @@ +""" Wrapper functions for TensorFlow layers. + +Author: Charles R. Qi +Date: November 2017 +""" + +import numpy as np +import tensorflow as tf + +def _variable_on_cpu(name, shape, initializer, use_fp16=False): + """Helper to create a Variable stored on CPU memory. + Args: + name: name of the variable + shape: list of ints + initializer: initializer for Variable + Returns: + Variable Tensor + """ + with tf.device("/cpu:0"): + dtype = tf.float16 if use_fp16 else tf.float32 + var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype) + return var + +def _variable_with_weight_decay(name, shape, stddev, wd, use_xavier=True): + """Helper to create an initialized Variable with weight decay. + + Note that the Variable is initialized with a truncated normal distribution. + A weight decay is added only if one is specified. + + Args: + name: name of the variable + shape: list of ints + stddev: standard deviation of a truncated Gaussian + wd: add L2Loss weight decay multiplied by this float. If None, weight + decay is not added for this Variable. + use_xavier: bool, whether to use xavier initializer + + Returns: + Variable Tensor + """ + if use_xavier: + initializer = tf.contrib.layers.xavier_initializer() + else: + initializer = tf.truncated_normal_initializer(stddev=stddev) + var = _variable_on_cpu(name, shape, initializer) + if wd is not None: + weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + return var + + +def conv1d(inputs, + num_output_channels, + kernel_size, + scope, + stride=1, + padding='SAME', + data_format='NHWC', + use_xavier=True, + stddev=1e-3, + weight_decay=None, + activation_fn=tf.nn.relu, + bn=False, + bn_decay=None, + is_training=None): + """ 1D convolution with non-linear operation. + + Args: + inputs: 3-D tensor variable BxLxC + num_output_channels: int + kernel_size: int + scope: string + stride: int + padding: 'SAME' or 'VALID' + data_format: 'NHWC' or 'NCHW' + use_xavier: bool, use xavier_initializer if true + stddev: float, stddev for truncated_normal init + weight_decay: float + activation_fn: function + bn: bool, whether to use batch norm + bn_decay: float or float tensor variable in [0,1] + is_training: bool Tensor variable + + Returns: + Variable tensor + """ + with tf.variable_scope(scope) as sc: + assert(data_format=='NHWC' or data_format=='NCHW') + if data_format == 'NHWC': + num_in_channels = inputs.get_shape()[-1].value + elif data_format=='NCHW': + num_in_channels = inputs.get_shape()[1].value + kernel_shape = [kernel_size, + num_in_channels, num_output_channels] + kernel = _variable_with_weight_decay('weights', + shape=kernel_shape, + use_xavier=use_xavier, + stddev=stddev, + wd=weight_decay) + outputs = tf.nn.conv1d(inputs, kernel, + stride=stride, + padding=padding, + data_format=data_format) + biases = _variable_on_cpu('biases', [num_output_channels], + tf.constant_initializer(0.0)) + outputs = tf.nn.bias_add(outputs, biases, data_format=data_format) + + if bn: + outputs = batch_norm_for_conv1d(outputs, is_training, + bn_decay=bn_decay, scope='bn', + data_format=data_format) + + if activation_fn is not None: + outputs = activation_fn(outputs) + return outputs + + + + +def conv2d(inputs, + num_output_channels, + kernel_size, + scope, + stride=[1, 1], + padding='SAME', + data_format='NHWC', + use_xavier=True, + stddev=1e-3, + weight_decay=None, + activation_fn=tf.nn.relu, + bn=False, + bn_decay=None, + is_training=None): + """ 2D convolution with non-linear operation. + + Args: + inputs: 4-D tensor variable BxHxWxC + num_output_channels: int + kernel_size: a list of 2 ints + scope: string + stride: a list of 2 ints + padding: 'SAME' or 'VALID' + data_format: 'NHWC' or 'NCHW' + use_xavier: bool, use xavier_initializer if true + stddev: float, stddev for truncated_normal init + weight_decay: float + activation_fn: function + bn: bool, whether to use batch norm + bn_decay: float or float tensor variable in [0,1] + is_training: bool Tensor variable + + Returns: + Variable tensor + """ + with tf.variable_scope(scope) as sc: + kernel_h, kernel_w = kernel_size + assert(data_format=='NHWC' or data_format=='NCHW') + if data_format == 'NHWC': + num_in_channels = inputs.get_shape()[-1].value + elif data_format=='NCHW': + num_in_channels = inputs.get_shape()[1].value + kernel_shape = [kernel_h, kernel_w, + num_in_channels, num_output_channels] + kernel = _variable_with_weight_decay('weights', + shape=kernel_shape, + use_xavier=use_xavier, + stddev=stddev, + wd=weight_decay) + stride_h, stride_w = stride + outputs = tf.nn.conv2d(inputs, kernel, + [1, stride_h, stride_w, 1], + padding=padding, + data_format=data_format) + biases = _variable_on_cpu('biases', [num_output_channels], + tf.constant_initializer(0.0)) + outputs = tf.nn.bias_add(outputs, biases, data_format=data_format) + + if bn: + outputs = batch_norm_for_conv2d(outputs, is_training, + bn_decay=bn_decay, scope='bn', + data_format=data_format) + + if activation_fn is not None: + outputs = activation_fn(outputs) + return outputs + + +def conv2d_transpose(inputs, + num_output_channels, + kernel_size, + scope, + stride=[1, 1], + padding='SAME', + use_xavier=True, + stddev=1e-3, + weight_decay=None, + activation_fn=tf.nn.relu, + bn=False, + bn_decay=None, + is_training=None): + """ 2D convolution transpose with non-linear operation. + + Args: + inputs: 4-D tensor variable BxHxWxC + num_output_channels: int + kernel_size: a list of 2 ints + scope: string + stride: a list of 2 ints + padding: 'SAME' or 'VALID' + use_xavier: bool, use xavier_initializer if true + stddev: float, stddev for truncated_normal init + weight_decay: float + activation_fn: function + bn: bool, whether to use batch norm + bn_decay: float or float tensor variable in [0,1] + is_training: bool Tensor variable + + Returns: + Variable tensor + + Note: conv2d(conv2d_transpose(a, num_out, ksize, stride), a.shape[-1], ksize, stride) == a + """ + with tf.variable_scope(scope) as sc: + kernel_h, kernel_w = kernel_size + num_in_channels = inputs.get_shape()[-1].value + kernel_shape = [kernel_h, kernel_w, + num_output_channels, num_in_channels] # reversed to conv2d + kernel = _variable_with_weight_decay('weights', + shape=kernel_shape, + use_xavier=use_xavier, + stddev=stddev, + wd=weight_decay) + stride_h, stride_w = stride + + # from slim.convolution2d_transpose + def get_deconv_dim(dim_size, stride_size, kernel_size, padding): + dim_size *= stride_size + + if padding == 'VALID' and dim_size is not None: + dim_size += max(kernel_size - stride_size, 0) + return dim_size + + # caculate output shape + batch_size = inputs.get_shape()[0].value + height = inputs.get_shape()[1].value + width = inputs.get_shape()[2].value + out_height = get_deconv_dim(height, stride_h, kernel_h, padding) + out_width = get_deconv_dim(width, stride_w, kernel_w, padding) + output_shape = [batch_size, out_height, out_width, num_output_channels] + + outputs = tf.nn.conv2d_transpose(inputs, kernel, output_shape, + [1, stride_h, stride_w, 1], + padding=padding) + biases = _variable_on_cpu('biases', [num_output_channels], + tf.constant_initializer(0.0)) + outputs = tf.nn.bias_add(outputs, biases) + + if bn: + outputs = batch_norm_for_conv2d(outputs, is_training, + bn_decay=bn_decay, scope='bn') + + if activation_fn is not None: + outputs = activation_fn(outputs) + return outputs + + + +def conv3d(inputs, + num_output_channels, + kernel_size, + scope, + stride=[1, 1, 1], + padding='SAME', + use_xavier=True, + stddev=1e-3, + weight_decay=None, + activation_fn=tf.nn.relu, + bn=False, + bn_decay=None, + is_training=None): + """ 3D convolution with non-linear operation. + + Args: + inputs: 5-D tensor variable BxDxHxWxC + num_output_channels: int + kernel_size: a list of 3 ints + scope: string + stride: a list of 3 ints + padding: 'SAME' or 'VALID' + use_xavier: bool, use xavier_initializer if true + stddev: float, stddev for truncated_normal init + weight_decay: float + activation_fn: function + bn: bool, whether to use batch norm + bn_decay: float or float tensor variable in [0,1] + is_training: bool Tensor variable + + Returns: + Variable tensor + """ + with tf.variable_scope(scope) as sc: + kernel_d, kernel_h, kernel_w = kernel_size + num_in_channels = inputs.get_shape()[-1].value + kernel_shape = [kernel_d, kernel_h, kernel_w, + num_in_channels, num_output_channels] + kernel = _variable_with_weight_decay('weights', + shape=kernel_shape, + use_xavier=use_xavier, + stddev=stddev, + wd=weight_decay) + stride_d, stride_h, stride_w = stride + outputs = tf.nn.conv3d(inputs, kernel, + [1, stride_d, stride_h, stride_w, 1], + padding=padding) + biases = _variable_on_cpu('biases', [num_output_channels], + tf.constant_initializer(0.0)) + outputs = tf.nn.bias_add(outputs, biases) + + if bn: + outputs = batch_norm_for_conv3d(outputs, is_training, + bn_decay=bn_decay, scope='bn') + + if activation_fn is not None: + outputs = activation_fn(outputs) + return outputs + +def fully_connected(inputs, + num_outputs, + scope, + use_xavier=True, + stddev=1e-3, + weight_decay=None, + activation_fn=tf.nn.relu, + bn=False, + bn_decay=None, + is_training=None): + """ Fully connected layer with non-linear operation. + + Args: + inputs: 2-D tensor BxN + num_outputs: int + + Returns: + Variable tensor of size B x num_outputs. + """ + with tf.variable_scope(scope) as sc: + num_input_units = inputs.get_shape()[-1].value + weights = _variable_with_weight_decay('weights', + shape=[num_input_units, num_outputs], + use_xavier=use_xavier, + stddev=stddev, + wd=weight_decay) + outputs = tf.matmul(inputs, weights) + biases = _variable_on_cpu('biases', [num_outputs], + tf.constant_initializer(0.0)) + outputs = tf.nn.bias_add(outputs, biases) + + if bn: + outputs = batch_norm_for_fc(outputs, is_training, bn_decay, 'bn') + + if activation_fn is not None: + outputs = activation_fn(outputs) + return outputs + + +def max_pool2d(inputs, + kernel_size, + scope, + stride=[2, 2], + padding='VALID'): + """ 2D max pooling. + + Args: + inputs: 4-D tensor BxHxWxC + kernel_size: a list of 2 ints + stride: a list of 2 ints + + Returns: + Variable tensor + """ + with tf.variable_scope(scope) as sc: + kernel_h, kernel_w = kernel_size + stride_h, stride_w = stride + outputs = tf.nn.max_pool(inputs, + ksize=[1, kernel_h, kernel_w, 1], + strides=[1, stride_h, stride_w, 1], + padding=padding, + name=sc.name) + return outputs + +def avg_pool2d(inputs, + kernel_size, + scope, + stride=[2, 2], + padding='VALID'): + """ 2D avg pooling. + + Args: + inputs: 4-D tensor BxHxWxC + kernel_size: a list of 2 ints + stride: a list of 2 ints + + Returns: + Variable tensor + """ + with tf.variable_scope(scope) as sc: + kernel_h, kernel_w = kernel_size + stride_h, stride_w = stride + outputs = tf.nn.avg_pool(inputs, + ksize=[1, kernel_h, kernel_w, 1], + strides=[1, stride_h, stride_w, 1], + padding=padding, + name=sc.name) + return outputs + + +def max_pool3d(inputs, + kernel_size, + scope, + stride=[2, 2, 2], + padding='VALID'): + """ 3D max pooling. + + Args: + inputs: 5-D tensor BxDxHxWxC + kernel_size: a list of 3 ints + stride: a list of 3 ints + + Returns: + Variable tensor + """ + with tf.variable_scope(scope) as sc: + kernel_d, kernel_h, kernel_w = kernel_size + stride_d, stride_h, stride_w = stride + outputs = tf.nn.max_pool3d(inputs, + ksize=[1, kernel_d, kernel_h, kernel_w, 1], + strides=[1, stride_d, stride_h, stride_w, 1], + padding=padding, + name=sc.name) + return outputs + +def avg_pool3d(inputs, + kernel_size, + scope, + stride=[2, 2, 2], + padding='VALID'): + """ 3D avg pooling. + + Args: + inputs: 5-D tensor BxDxHxWxC + kernel_size: a list of 3 ints + stride: a list of 3 ints + + Returns: + Variable tensor + """ + with tf.variable_scope(scope) as sc: + kernel_d, kernel_h, kernel_w = kernel_size + stride_d, stride_h, stride_w = stride + outputs = tf.nn.avg_pool3d(inputs, + ksize=[1, kernel_d, kernel_h, kernel_w, 1], + strides=[1, stride_d, stride_h, stride_w, 1], + padding=padding, + name=sc.name) + return outputs + + +def batch_norm_template_unused(inputs, is_training, scope, moments_dims, bn_decay): + """ NOTE: this is older version of the util func. it is deprecated. + Batch normalization on convolutional maps and beyond... + Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow + + Args: + inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC + is_training: boolean tf.Varialbe, true indicates training phase + scope: string, variable scope + moments_dims: a list of ints, indicating dimensions for moments calculation + bn_decay: float or float tensor variable, controling moving average weight + Return: + normed: batch-normalized maps + """ + with tf.variable_scope(scope) as sc: + num_channels = inputs.get_shape()[-1].value + beta = _variable_on_cpu(name='beta',shape=[num_channels], + initializer=tf.constant_initializer(0)) + gamma = _variable_on_cpu(name='gamma',shape=[num_channels], + initializer=tf.constant_initializer(1.0)) + batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name='moments') + decay = bn_decay if bn_decay is not None else 0.9 + ema = tf.train.ExponentialMovingAverage(decay=decay) + # Operator that maintains moving averages of variables. + # Need to set reuse=False, otherwise if reuse, will see moments_1/mean/ExponentialMovingAverage/ does not exist + # https://github.com/shekkizh/WassersteinGAN.tensorflow/issues/3 + with tf.variable_scope(tf.get_variable_scope(), reuse=False): + ema_apply_op = tf.cond(is_training, + lambda: ema.apply([batch_mean, batch_var]), + lambda: tf.no_op()) + + # Update moving average and return current batch's avg and var. + def mean_var_with_update(): + with tf.control_dependencies([ema_apply_op]): + return tf.identity(batch_mean), tf.identity(batch_var) + + # ema.average returns the Variable holding the average of var. + mean, var = tf.cond(is_training, + mean_var_with_update, + lambda: (ema.average(batch_mean), ema.average(batch_var))) + normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3) + return normed + + +def batch_norm_template(inputs, is_training, scope, moments_dims_unused, bn_decay, data_format='NHWC'): + """ Batch normalization on convolutional maps and beyond... + Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow + + Args: + inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC + is_training: boolean tf.Varialbe, true indicates training phase + scope: string, variable scope + moments_dims: a list of ints, indicating dimensions for moments calculation + bn_decay: float or float tensor variable, controling moving average weight + data_format: 'NHWC' or 'NCHW' + Return: + normed: batch-normalized maps + """ + bn_decay = bn_decay if bn_decay is not None else 0.9 + return tf.contrib.layers.batch_norm(inputs, + center=True, scale=True, + is_training=is_training, decay=bn_decay,updates_collections=None, + scope=scope, + data_format=data_format) + + +def batch_norm_for_fc(inputs, is_training, bn_decay, scope): + """ Batch normalization on FC data. + + Args: + inputs: Tensor, 2D BxC input + is_training: boolean tf.Varialbe, true indicates training phase + bn_decay: float or float tensor variable, controling moving average weight + scope: string, variable scope + Return: + normed: batch-normalized maps + """ + return batch_norm_template(inputs, is_training, scope, [0,], bn_decay) + + +def batch_norm_for_conv1d(inputs, is_training, bn_decay, scope, data_format): + """ Batch normalization on 1D convolutional maps. + + Args: + inputs: Tensor, 3D BLC input maps + is_training: boolean tf.Varialbe, true indicates training phase + bn_decay: float or float tensor variable, controling moving average weight + scope: string, variable scope + data_format: 'NHWC' or 'NCHW' + Return: + normed: batch-normalized maps + """ + return batch_norm_template(inputs, is_training, scope, [0,1], bn_decay, data_format) + + + + +def batch_norm_for_conv2d(inputs, is_training, bn_decay, scope, data_format): + """ Batch normalization on 2D convolutional maps. + + Args: + inputs: Tensor, 4D BHWC input maps + is_training: boolean tf.Varialbe, true indicates training phase + bn_decay: float or float tensor variable, controling moving average weight + scope: string, variable scope + data_format: 'NHWC' or 'NCHW' + Return: + normed: batch-normalized maps + """ + return batch_norm_template(inputs, is_training, scope, [0,1,2], bn_decay, data_format) + + +def batch_norm_for_conv3d(inputs, is_training, bn_decay, scope): + """ Batch normalization on 3D convolutional maps. + + Args: + inputs: Tensor, 5D BDHWC input maps + is_training: boolean tf.Varialbe, true indicates training phase + bn_decay: float or float tensor variable, controling moving average weight + scope: string, variable scope + Return: + normed: batch-normalized maps + """ + return batch_norm_template(inputs, is_training, scope, [0,1,2,3], bn_decay) + + +def dropout(inputs, + is_training, + scope, + keep_prob=0.5, + noise_shape=None): + """ Dropout layer. + + Args: + inputs: tensor + is_training: boolean tf.Variable + scope: string + keep_prob: float in [0,1] + noise_shape: list of ints + + Returns: + tensor variable + """ + with tf.variable_scope(scope) as sc: + outputs = tf.cond(is_training, + # lambda: tf.nn.dropout(inputs, keep_prob, noise_shape), + lambda: tf.nn.dropout(inputs, keep_prob, noise_shape), + lambda: inputs) + return outputs -- Gitee From 5455c4f01f3837f86163802ba3909c9ec684be74 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:36:16 +0000 Subject: [PATCH 62/69] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20co?= =?UTF-8?q?de/2022=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E?= =?UTF-8?q?3D=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86?= =?UTF-8?q?=E5=88=AB/models/pointnet=5Fcls.py=20for=20torch?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../models/pointnet_cls.py for torch" | 40 ------------------- 1 file changed, 40 deletions(-) delete mode 100644 "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py for torch" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py for torch" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py for torch" deleted file mode 100644 index 520e8f2..0000000 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/models/pointnet_cls.py for torch" +++ /dev/null @@ -1,40 +0,0 @@ -import torch.nn as nn -import torch.utils.data -import torch.nn.functional as F -from pointnet import PointNetEncoder, feature_transform_reguliarzer - -class get_model(nn.Module): - def __init__(self, k=40, normal_channel=True): - super(get_model, self).__init__() - if normal_channel: - channel = 6 - else: - channel = 3 - self.feat = PointNetEncoder(global_feat=True, feature_transform=True, channel=channel) - self.fc1 = nn.Linear(1024, 512) - self.fc2 = nn.Linear(512, 256) - self.fc3 = nn.Linear(256, k) - self.dropout = nn.Dropout(p=0.4) - self.bn1 = nn.BatchNorm1d(512) - self.bn2 = nn.BatchNorm1d(256) - self.relu = nn.ReLU() - - def forward(self, x): - x, trans, trans_feat = self.feat(x) - x = F.relu(self.bn1(self.fc1(x))) - x = F.relu(self.bn2(self.dropout(self.fc2(x)))) - x = self.fc3(x) - x = F.log_softmax(x, dim=1) - return x, trans_feat - -class get_loss(torch.nn.Module): - def __init__(self, mat_diff_loss_scale=0.001): - super(get_loss, self).__init__() - self.mat_diff_loss_scale = mat_diff_loss_scale - - def forward(self, pred, target, trans_feat): - loss = F.nll_loss(pred, target) - mat_diff_loss = feature_transform_reguliarzer(trans_feat) - - total_loss = loss + mat_diff_loss * self.mat_diff_loss_scale - return total_loss -- Gitee From 888993f215872ab48b89e3d460161e7912c76bb6 Mon Sep 17 00:00:00 2001 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<11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:37:28 +0000 Subject: [PATCH 64/69] =?UTF-8?q?=E9=87=8D=E5=91=BD=E5=90=8D=20code/2022?= =?UTF-8?q?=5Fautumn/=E5=B4=94=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D?= =?UTF-8?q?=E7=82=B9=E4=BA=91=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84?= =?UTF-8?q?=E9=9C=87=E5=AE=B3=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB?= =?UTF-8?q?/data/modelnet40=20=E4=B8=BA=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data/damaged-buil?= =?UTF-8?q?ding=20data?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../data/damaged-building data/.keep" | 0 .../data/damaged-building data/download" | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename 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"code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" (100%) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/.keep" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/.keep" similarity index 100% rename from "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/.keep" rename to "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/.keep" diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/download" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" similarity index 100% rename from "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/modelnet40/download" rename to "code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" -- Gitee From c61f4331e05246963ee90c86ec10155a21b748bc Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Fri, 28 Oct 2022 11:42:57 +0000 Subject: [PATCH 65/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data/damaged-buil?= =?UTF-8?q?ding=20data/download.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data/damaged-building data/download" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" index d850c61..5830004 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" @@ -1,2 +1,2 @@ -链接：https://pan.baidu.com/s/1J1svTCLW_Iy7blMan0YiPQ -提取码：20yl \ No newline at end of file +链接：https://pan.baidu.com/s/16XXSZuG9wIMVRpyxWsV2vA +提取码：hqs6 \ No newline at end of file -- Gitee From 691fc50cae8effc195c89ac44714398cecedebc5 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Sat, 29 Oct 2022 03:41:17 +0000 Subject: [PATCH 66/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/train.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../train.py" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train.py" index 0c03076..574f25b 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train.py" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/train.py" @@ -86,7 +86,7 @@ BN_DECAY_CLIP = 0.99 HOSTNAME = socket.gethostname() -NUM_CLASSES = 40 +NUM_CLASSES = 3 # Shapenet official train/test split -- Gitee From 4ad04bd7eed9459c96c88f5d726af5cc5a5d2a74 Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Sat, 29 Oct 2022 03:41:52 +0000 Subject: [PATCH 67/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/evaluate.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../evaluate.py" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" index 224d65b..c4319f8 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" @@ -44,9 +44,9 @@ if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR) LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') -NUM_CLASSES = 40 +NUM_CLASSES = 3 SHAPE_NAMES = [line.rstrip() for line in \ - open(os.path.join(ROOT_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))] + open(os.path.join(ROOT_DIR, 'data/modelnet40_ply_hdf5_2048/file_names.txt'))] HOSTNAME = socket.gethostname() -- Gitee From 040d4d029f917e8a37246895def900ac6a9a594e Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Sat, 29 Oct 2022 03:48:40 +0000 Subject: [PATCH 68/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/data/damaged-buil?= =?UTF-8?q?ding=20data/download.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../data/damaged-building data/download" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" index 5830004..0e817f9 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/data/damaged-building data/download" @@ -1,2 +1,2 @@ -链接：https://pan.baidu.com/s/16XXSZuG9wIMVRpyxWsV2vA -提取码：hqs6 \ No newline at end of file +链接：https://pan.baidu.com/s/15n8BRLCaa-N3joRPzXZsTg +提取码：79d7 \ No newline at end of file -- Gitee From ad3b598534f3bc9d3dcea31cb164889e6b7a54ca Mon Sep 17 00:00:00 2001 From: yining7964 <11652807+yining7964@user.noreply.gitee.com> Date: Sat, 29 Oct 2022 03:49:07 +0000 Subject: [PATCH 69/69] =?UTF-8?q?update=20code/2022=5Fautumn/=E5=B4=94?= =?UTF-8?q?=E9=A9=BF=E5=AE=81-=E5=9F=BA=E4=BA=8E3D=E7=82=B9=E4=BA=91?= =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84=E9=9C=87=E5=AE=B3?= =?UTF-8?q?=E5=BB=BA=E7=AD=91=E7=89=A9=E8=AF=86=E5=88=AB/evaluate.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: yining7964 <11652807+yining7964@user.noreply.gitee.com> --- .../evaluate.py" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" index c4319f8..abc5a52 100644 --- "a/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" +++ "b/code/2022_autumn/\345\264\224\351\251\277\345\256\201-\345\237\272\344\272\2163D\347\202\271\344\272\221\346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\351\234\207\345\256\263\345\273\272\347\255\221\347\211\251\350\257\206\345\210\253/evaluate.py" @@ -46,7 +46,7 @@ LOG_FOUT.write(str(FLAGS)+'\n') NUM_CLASSES = 3 SHAPE_NAMES = [line.rstrip() for line in \ - open(os.path.join(ROOT_DIR, 'data/modelnet40_ply_hdf5_2048/file_names.txt'))] + open(os.path.join(ROOT_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))] HOSTNAME = socket.gethostname() -- Gitee