diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/.keep" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/.keep" new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/Decomposition.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/Decomposition.py" new file mode 100644 index 0000000000000000000000000000000000000000..1705c2980b3c48934f34970d5631fe72009b8d48 --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/Decomposition.py" @@ -0,0 +1,59 @@ +import matplotlib.pyplot as plt +import numpy as np +import cv2 + +def highPassFilter(image,d): + f = np.fft.fft2(image) + fshift = np.fft.fftshift(f) + def make_transform_matrix(d): + transfor_matrix = np.zeros(image.shape) + center_point = tuple(map(lambda x:(x-1)/2,img.shape)) + for i in range(transfor_matrix.shape[0]): + for j in range(transfor_matrix.shape[1]): + def cal_distance(pa,pb): + from math import sqrt + dis = sqrt((pa[0]-pb[0])**2+(pa[1]-pb[1])**2) + return dis + dis = cal_distance(center_point,(i,j)) + if dis <= d: + transfor_matrix[i,j]=0 + else: + transfor_matrix[i,j]=1 + return transfor_matrix + d_matrix = make_transform_matrix(d) + new_img = np.abs(np.fft.ifft2(np.fft.ifftshift(fshift*d_matrix))) + return new_img + + +def lowPassFilter(image, d): + f = np.fft.fft2(image) + fshift = np.fft.fftshift(f) + + def make_transform_matrix(d): + transfor_matrix = np.zeros(image.shape) + center_point = tuple(map(lambda x: (x - 1) / 2, img.shape)) + for i in range(transfor_matrix.shape[0]): + for j in range(transfor_matrix.shape[1]): + def cal_distance(pa, pb): + from math import sqrt + dis = sqrt((pa[0] - pb[0]) ** 2 + (pa[1] - pb[1]) ** 2) + return dis + + dis = cal_distance(center_point, (i, j)) + if dis <= d: + transfor_matrix[i, j] = 1 + else: + transfor_matrix[i, j] = 0 + return transfor_matrix + + d_matrix = make_transform_matrix(d) + new_img = np.abs(np.fft.ifft2(np.fft.ifftshift(fshift * d_matrix))) + return new_img + +img = cv2.imread(r'D:\ziliao\bishe\dataset\train_Heavy\data\00022.png',0) +img = cv2.imread(r'D:\ziliao\bishe\dataset\train_Heavy\gt\00022.png',0) +img_d1 = highPassFilter(img,100) + +cv2.imshow("",img_d1); +cv2.waitKey(); + diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/README.md" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/README.md" new file mode 100644 index 0000000000000000000000000000000000000000..bf932eda06dae1780ee1469c4e32eb42dab6315c --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/README.md" @@ -0,0 +1,37 @@ +# DeRain + +## 简介 +代码复现了论文中的除雨算法。[[论文链接]](https://arxiv.org/abs/1711.10098) + +## 参数设置 +参数位于parameters.py文件中 + +下载数据集 https://drive.google.com/open?id=1e7R76s6vwUJxILOcAsthgDLPSnOrQ49K + +DISCRIMINATOR：使用判别器 + +ATTENTIVE_DISCRIMINATOR：使用注意力判别器 + +ATTENTIVE_AUTOENCODER：使用带有注意力机制的自编码器 + +TRAIN_PATH, EVAL_PATH, TEST_PATH：数据集目录 + +BATCH_SZ：每个batch中的图片数目 + +THETA, GAMMA, LAMBDA：论文中模型的超参数 + +LR：学习率 + +ITERATION：LSTM中单元个数 + +EPOCHES：训练时的epoch个数 + +MODEL_PATH：测试模型性能时模型的位置 + +device：训练时的单元，可以设置为'cpu','cuda:0'等 + +## 训练模型 +在parameters.py中设置好参数。运行命令 python train.py。在images文件夹中保存的图片显示了训练的效果。文件夹weights中保存了模型的参数。 + +## 测试模型性能 +在parameters.py中设置好参数。运行命令python evaluate.py可以测试模型的性能。 diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/bicubic.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/bicubic.py" new file mode 100644 index 0000000000000000000000000000000000000000..3ae4c1d17c506a6ca72c23c71c5a76cab37ff3f2 --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/bicubic.py" @@ -0,0 +1,139 @@ +import os +import math +import pickle +import random +import numpy as np +import torch +import cv2 +import logging + +# matlab 'imresize' function, now only support 'bicubic' +def cubic(x): + absx = torch.abs(x) + absx2 = absx**2 + absx3 = absx**3 + return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ + (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) + + +def imresize(img, scale, antialiasing=True): + # Now the scale should be the same for H and W + # input: img: CHW RGB [0,1] + # output: CHW RGB [0,1] w/o round + + in_C, in_H, in_W = img.size() + out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) + kernel_width = 4 + kernel = 'cubic' + + # Return the desired dimension order for performing the resize. The + # strategy is to perform the resize first along the dimension with the + # smallest scale factor. + # Now we do not support this. + + # get weights and indices + weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( + in_H, out_H, scale, kernel, kernel_width, antialiasing) + weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( + in_W, out_W, scale, kernel, kernel_width, antialiasing) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) + img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) + + sym_patch = img[:, :sym_len_Hs, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) + + sym_patch = img[:, -sym_len_He:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(in_C, out_H, in_W) + kernel_width = weights_H.size(1) + for i in range(out_H): + idx = int(indices_H[i][0]) + out_1[0, i, :] = img_aug[0, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) + out_1[1, i, :] = img_aug[1, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) + out_1[2, i, :] = img_aug[2, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) + out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) + + sym_patch = out_1[:, :, :sym_len_Ws] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, :, -sym_len_We:] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(in_C, out_H, out_W) + kernel_width = weights_W.size(1) + for i in range(out_W): + idx = int(indices_W[i][0]) + out_2[0, :, i] = out_1_aug[0, :, idx:idx + kernel_width].mv(weights_W[i]) + out_2[1, :, i] = out_1_aug[1, :, idx:idx + kernel_width].mv(weights_W[i]) + out_2[2, :, i] = out_1_aug[2, :, idx:idx + kernel_width].mv(weights_W[i]) + + return out_2 + +def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): + if (scale < 1) and (antialiasing): + # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width + kernel_width = kernel_width / scale + + # Output-space coordinates + x = torch.linspace(1, out_length, out_length) + + # Input-space coordinates. Calculate the inverse mapping such that 0.5 + # in output space maps to 0.5 in input space, and 0.5+scale in output + # space maps to 1.5 in input space. + u = x / scale + 0.5 * (1 - 1 / scale) + + # What is the left-most pixel that can be involved in the computation? + left = torch.floor(u - kernel_width / 2) + + # What is the maximum number of pixels that can be involved in the + # computation? Note: it's OK to use an extra pixel here; if the + # corresponding weights are all zero, it will be eliminated at the end + # of this function. + P = math.ceil(kernel_width) + 2 + + # The indices of the input pixels involved in computing the k-th output + # pixel are in row k of the indices matrix. + indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( + 1, P).expand(out_length, P) + + # The weights used to compute the k-th output pixel are in row k of the + # weights matrix. + distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices + # apply cubic kernel + if (scale < 1) and (antialiasing): + weights = scale * cubic(distance_to_center * scale) + else: + weights = cubic(distance_to_center) + # Normalize the weights matrix so that each row sums to 1. + weights_sum = torch.sum(weights, 1).view(out_length, 1) + weights = weights / weights_sum.expand(out_length, P) + + # If a column in weights is all zero, get rid of it. only consider the first and last column. + weights_zero_tmp = torch.sum((weights == 0), 0) + if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): + indices = indices.narrow(1, 1, P - 2) + weights = weights.narrow(1, 1, P - 2) + if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): + indices = indices.narrow(1, 0, P - 2) + weights = weights.narrow(1, 0, P - 2) + weights = weights.contiguous() + indices = indices.contiguous() + sym_len_s = -indices.min() + 1 + sym_len_e = indices.max() - in_length + indices = indices + sym_len_s - 1 + return weights, indices, int(sym_len_s), int(sym_len_e) diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/catImg.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/catImg.py" new file mode 100644 index 0000000000000000000000000000000000000000..0a82409889007e2d08cc7ca78575b3b99af8d201 --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/catImg.py" @@ -0,0 +1,17 @@ +import os +import cv2 +import numpy +output_path = 'D:/ziliao/bishe/dataset//output_ID_8'; +data_path = 'D:/ziliao/bishe/dataset/test_b/transformsize_data' + +catImg_path = 'D:/ziliao/bishe/dataset/catImg' + +filename_list=os.listdir('D:/ziliao/bishe/dataset//output_ID_8') +for i in filename_list: + output = cv2.imread(output_path+"/"+i); + data = cv2.imread(data_path+"/"+i); + result = numpy.zeros((448,224,3)); + result=result.astype(numpy.uint8); + result[0:224,:,:] = data + result[224:224*2,:,:]=output + cv2.imwrite(catImg_path+"/"+str(i),result); diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/derain_code.rar" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/derain_code.rar" new file mode 100644 index 0000000000000000000000000000000000000000..ae3755256b8529b413ef8685df2a0a11878d3ffd Binary files /dev/null and "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/derain_code.rar" differ diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/evaluate.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/evaluate.py" new file mode 100644 index 0000000000000000000000000000000000000000..68312af5eb1aa05fbc1566a6a855e54e0e93d66f --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/evaluate.py" @@ -0,0 +1,40 @@ +from parameters import * +from preprocessing import * +import numpy as np +from matric import * + +model = torch.load(MODEL_PATH).to(device) +model.eval() +test_dataset = DeRainDataset(is_test=True) +test_loader = DataLoader(test_dataset) +output_path = './result/output_nature' +psnr = 0 +psnr_ = 0 +ssim = 0 +ssim_ = 0 +with torch.no_grad(): + for i,(images,gts) in enumerate(test_loader): + images,gts = torch_variable(images,True), torch_variable(gts,False) + outputs = model(images.to(device))[-1] + (c,ih, iw) = gts[0].shape + h_pad = 4 - ih % 4 + w_pad = 4 - iw % 4 + (c,ih,iw) = outputs[0].shape + a=outputs[0][:,h_pad//2:ih-(h_pad-h_pad//2),w_pad//2:iw-(w_pad-w_pad//2)]; + b=images[0][:,h_pad//2:ih-(h_pad-h_pad//2),w_pad//2:iw-(w_pad-w_pad//2)]; + + img = outputs[0].permute(1, 2, 0).detach().cpu().numpy() * 255 + temp_path = output_path + '/' + str(i + 1) + '.png'; + cv2.imwrite(temp_path, img); + + + + psnr += PSNR(a.cpu(),gts[0].cpu()) + ssim += SSIM(a.cpu(),gts[0].cpu()) + # psnr_ += PSNR(b.cpu(),gts[0].cpu()) + # ssim_ += SSIM(b.cpu(),gts[0].cpu()) + psnr /= i+1 + ssim /= i+1 + +print("PSNR: ",psnr) +print("SSIM: ",ssim) \ No newline at end of file diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/gaussian_downsample.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/gaussian_downsample.py" new file mode 100644 index 0000000000000000000000000000000000000000..3804ceae7f4d984472f2a7d23b2805349952792c --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/gaussian_downsample.py" @@ -0,0 +1,54 @@ +# Gaussian kernel for downsampling +import scipy.ndimage.filters as fi +import numpy as np +import torch.nn.functional as F +import torch + +def gaussian_downsample(x, scale=4): + """Downsamping with Gaussian kernel used in the DUF official code + Args: + x (Tensor, [C, T, H, W]): frames to be downsampled. + scale (int): downsampling factor: 2 | 3 | 4. + """ + + assert scale in [2, 3, 4], 'Scale [{}] is not supported'.format(scale) + + def gkern(kernlen=13, nsig=1.6): + import scipy.ndimage.filters as fi + inp = np.zeros((kernlen, kernlen)) + # set element at the middle to one, a dirac delta + inp[kernlen // 2, kernlen // 2] = 1 + # gaussian-smooth the dirac, resulting in a gaussian filter mask + return fi.gaussian_filter(inp, nsig) + + if scale == 2: + h = gkern(13, 0.8) # 13 and 0.8 for x2 + elif scale == 3: + h = gkern(13, 1.2) # 13 and 1.2 for x3 + elif scale == 4: + h = gkern(13, 1.6) # 13 and 1.6 for x4 + else: + print('Invalid upscaling factor: {} (Must be one of 2, 3, 4)'.format(R)) + exit(1) + + C, T, H, W = x.size() + x = x.contiguous().view(-1, 1, H, W) # depth convolution (channel-wise convolution) + pad_w, pad_h = 6 + scale * 2, 6 + scale * 2 # 6 is the pad of the gaussian filter + r_h, r_w = 0, 0 + + if scale == 3: + r_h = 3 - (H % 3) + r_w = 3 - (W % 3) + + x = F.pad(x, [pad_w, pad_w + r_w, pad_h, pad_h + r_h], 'reflect') + gaussian_filter = torch.from_numpy(gkern(13, 0.4 * scale)).type_as(x).unsqueeze(0).unsqueeze(0) + x = F.conv2d(x, gaussian_filter, stride=scale) + # please keep the operation same as training. + # if downsample to 32 on training time, use the below code. + x = x[:, :, 2:-2, 2:-2] + # if downsample to 28 on training time, use the below code. + #x = x[:,:,scale:-scale,scale:-scale] + x = x.view(C, T, x.size(2), x.size(3)) + return x + + diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/loss.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/loss.py" new file mode 100644 index 0000000000000000000000000000000000000000..5d109029a7682e814c2bef5d20b6f6607d8b129c --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/loss.py" @@ -0,0 +1,65 @@ +import torch +from torch import nn +from torchvision.models.vgg import vgg16 +from models import * +from parameters import * + +BCELoss = nn.BCELoss().to(device) +MSELoss = nn.MSELoss().to(device) + +def GANLoss(input_, is_real): + return BCELoss(input_,torch.FloatTensor(input_.size()).fill_(is_real*1.0).to(device)) + +def AttentionLoss(A_,M_): + loss_ATT = 0 + for i in range(1, ITERATION+1): + loss_ATT += pow(THETA, ITERATION-i) * BCELoss(A_[i-1],M_) + return loss_ATT + +vgg_model = vgg(vgg_init()) + +def PerceptualLoss(O_,T_): + O = vgg_model(O_) + T = vgg_model(T_) + loss = 0 + for i in range(len(T)): + loss += MSELoss(O[i],T[i])/float(len(T)) + return loss + +def MultiScaleLoss(S_,gt,batch=1): + gt = gt.permute((0,2,3,1)) + lambdas = [0.6,0.8,1.0] + #ground truths + T_ = [] + #resize gt by 1/4,1/2,1 + for i in range(S_[0].shape[0]): + temp = [] + x = (np.array(gt[i])*255.).astype(np.uint8) + t = cv2.resize(x, None, fx=1.0/4.0,fy=1.0/4.0, interpolation=cv2.INTER_AREA) + t = np.expand_dims((t/255.).astype(np.float32).transpose(2,0,1),axis=0) + temp.append(t) + t = cv2.resize(x, None, fx=1.0/2.0,fy=1.0/2.0, interpolation=cv2.INTER_AREA) + t = np.expand_dims((t/255.).astype(np.float32).transpose(2,0,1),axis=0) + temp.append(t) + x = np.expand_dims((x/255.).astype(np.float32).transpose(2,0,1),axis=0) + temp.append(x) + T_.append(temp) + temp_T = [] + for i in range(len(lambdas)): + for j in range((S_[0].shape[0])): + if j == 0: + x = T_[j][i] + else: + #print(x.shape,T_[j][i].shape) + x = np.concatenate((x, T_[j][i]), axis=0) + #print(x.shape) + temp_T.append(torch.from_numpy(x).to(device)) + T_ = temp_T + loss_ML = 0 + for i in range(len(lambdas)): + #print(S_[i].shape,T_[i].shape) + loss_ML += lambdas[i] * MSELoss(S_[i], T_[i]) + return loss_ML/float(S_[0].shape[0]) + +def MAPLoss(D_O,D_R,A_N): + return MSELoss(D_O,A_N) + MSELoss(D_R,torch.zeros(D_R.shape).to(device)) \ No newline at end of file diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/loss.txt" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/loss.txt" new file mode 100644 index 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"b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/matric.py" new file mode 100644 index 0000000000000000000000000000000000000000..60e6543c4752e43e97a562d5e8ff98fb931094e3 --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/matric.py" @@ -0,0 +1,19 @@ +import numpy as np +from skimage.metrics import peak_signal_noise_ratio as compare_psnr +from skimage.metrics import structural_similarity as compare_ssim +import cv2 + +def PSNR(output,gt): + output = cv2.cvtColor(output.permute(1,2,0).numpy(),cv2.COLOR_BGR2YCR_CB)[:, :, 0] + gt = cv2.cvtColor(gt.permute(1,2,0).numpy(), cv2.COLOR_BGR2YCR_CB)[:, :, 0] + output = np.array([output]) + gt = np.array([gt]) + dif = np.array(output-gt) + return 10*np.log10(dif.size/np.sum(dif*dif)) + +def SSIM(im1, im2): + im1,im2 = im1.detach().permute(1,2,0).numpy()*255, im2.detach().permute(1,2,0).numpy()*255 + im1,im2 = np.array(im1,dtype=np.uint8),np.array(im2,dtype=np.uint8) + im1_y = cv2.cvtColor(im1, cv2.COLOR_BGR2YCR_CB)[:, :, 0] + im2_y = cv2.cvtColor(im2, cv2.COLOR_BGR2YCR_CB)[:, :, 0] + return compare_ssim(im1_y, im2_y) diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/models.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/models.py" new file mode 100644 index 0000000000000000000000000000000000000000..9c4c3f0471eee0965195fdafb31c383815547c1d --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/models.py" @@ -0,0 +1,330 @@ +#PyTorch lib +import torch +import torch.nn as nn +from torch.autograd import Variable +import torch.utils.data as Data +import torch.nn.functional as F +import torchvision +#Tools lib +import numpy as np +import cv2 +import random +import time +import os +from parameters import * + +def trainable(net, trainable): + for para in net.parameters(): + para.requires_grad = trainable + +#Initialize VGG16 with pretrained weight on ImageNet +def vgg_init(): + vgg_model = torchvision.models.vgg16(pretrained = True).to(device) + trainable(vgg_model, False) + return vgg_model + +#Extract features from internal layers for perceptual loss +class vgg(nn.Module): + def __init__(self, vgg_model): + super(vgg, self).__init__() + self.vgg_layers = vgg_model.features + self.layer_name_mapping = { + '1': "relu1_1", + '3': "relu1_2", + '6': "relu2_1", + '8': "relu2_2" + } + + def forward(self, x): + output = [] + for name, module in self.vgg_layers._modules.items(): + x = module(x) + if name in self.layer_name_mapping: + output.append(x) + return output + +#Model + +class Generator(nn.Module): + def __init__(self): + super(Generator, self).__init__() + self.det_conv0 = nn.Sequential( + nn.Conv2d(4, 32, 3, 1, 1), + nn.ReLU() + ) + self.det_conv1 = nn.Sequential( + nn.Conv2d(32, 64, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(64, 32, 3, 1, 1), + nn.ReLU() + ) + + self.det_conv2 = nn.Sequential( + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU() + ) + self.det_conv3 = nn.Sequential( + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU() + ) + self.det_conv4 = nn.Sequential( + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU() + ) + self.det_conv5 = nn.Sequential( + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(32, 32, 3, 1, 1), + nn.ReLU() + ) + self.conv_i = nn.Sequential( + nn.Conv2d(32 + 32, 32, 3, 1, 1), + nn.Sigmoid() + ) + self.conv_f = nn.Sequential( + nn.Conv2d(32 + 32, 32, 3, 1, 1), + nn.Sigmoid() + ) + self.conv_g = nn.Sequential( + nn.Conv2d(32 + 32, 32, 3, 1, 1), + nn.Tanh() + ) + self.conv_o = nn.Sequential( + nn.Conv2d(32 + 32, 32, 3, 1, 1), + nn.Sigmoid() + ) + self.det_conv_mask = nn.Sequential( + nn.Conv2d(32, 1, 3, 1, 1), + ) + self.sigmoid = nn.Sigmoid() + self.conv1 = nn.Sequential( + nn.Conv2d(4, 64, 5, 1, 2), + nn.ReLU() + ) + self.conv2 = nn.Sequential( + nn.Conv2d(64, 128, 3, 2, 1), + nn.ReLU() + ) + self.conv3 = nn.Sequential( + nn.Conv2d(128, 128, 3, 1, 1), + nn.ReLU() + ) + self.conv4 = nn.Sequential( + nn.Conv2d(128, 256, 3, 2, 1), + nn.ReLU() + ) + self.conv5 = nn.Sequential( + nn.Conv2d(256, 256, 3, 1, 1), + nn.ReLU() + ) + self.conv6 = nn.Sequential( + nn.Conv2d(256, 256, 3, 1, 1), + nn.ReLU() + ) + self.diconv1 = nn.Sequential( + nn.Conv2d(256, 256, 3, 1, 2, dilation = 2), + nn.ReLU() + ) + self.diconv2 = nn.Sequential( + nn.Conv2d(256, 256, 3, 1, 4, dilation = 4), + nn.ReLU() + ) + self.diconv3 = nn.Sequential( + nn.Conv2d(256, 256, 3, 1, 8, dilation = 8), + nn.ReLU() + ) + self.diconv4 = nn.Sequential( + nn.Conv2d(256, 256, 3, 1, 16, dilation = 16), + nn.ReLU() + ) + self.conv7 = nn.Sequential( + nn.Conv2d(256, 256, 3, 1, 1), + nn.ReLU() + ) + self.conv8 = nn.Sequential( + nn.Conv2d(256, 256, 3, 1, 1), + nn.ReLU() + ) + self.deconv1 = nn.Sequential( + nn.ConvTranspose2d(256, 128, 4, 2, 1), + nn.ReflectionPad2d((1, 0, 1, 0)), + # nn.AvgPool2d(2, stride = 1), + nn.Conv2d(128,128,4,1,1), + nn.ReLU() + ) + self.conv9 = nn.Sequential( + nn.Conv2d(128, 128, 3, 1, 1), + nn.ReLU() + ) + self.deconv2 = nn.Sequential( + nn.ConvTranspose2d(128, 64, 4, 2, 1), + nn.ReflectionPad2d((1, 0, 1, 0)), + # nn.AvgPool2d(2, stride = 1), + nn.Conv2d(64, 64, 4, 1, 1), + nn.ReLU() + ) + self.conv10 = nn.Sequential( + nn.Conv2d(64, 32, 3, 1, 1), + nn.ReLU() + ) + self.outframe1 = nn.Sequential( + nn.Conv2d(256, 3, 3, 1, 1), + nn.ReLU() + ) + self.outframe2 = nn.Sequential( + nn.Conv2d(128, 3, 3, 1, 1), + nn.ReLU() + ) + self.output = nn.Sequential( + nn.Conv2d(32, 3, 3, 1, 1) + ) + #StructureDetail + self.d1DCCL = nn.Sequential( + nn.Conv2d(32,3,3,1,1,dilation=1), + nn.ReLU() + ) + self.d3DCCL = nn.Sequential( + nn.Conv2d(32,3,3,1,1,dilation=3), + nn.ReLU() + ) + self.d5DCCL = nn.Sequential( + nn.Conv2d(32,3,3,1,1,dilation=5), + nn.ReLU() + ) + + + self.deconv3 = nn.Sequential( + nn.Conv2d(160,32,3,1,1) + ) + #masks, last 5th layer, last 3rd layer, last layer + def forward(self, input): + batch_size, row, col = input.size(0), input.size(2), input.size(3) + mask = Variable(torch.ones(batch_size, 1, row, col)).to(device) / 2. + h = Variable(torch.zeros(batch_size, 32, row, col)).to(device) + c = Variable(torch.zeros(batch_size, 32, row, col)).to(device) + mask_list = [] + #CONV-LSTM + for i in range(ITERATION): + #concatenate the input image with the mask + x = torch.cat((input, mask), 1) + #extract feature with ResNet + x = self.det_conv0(x) + resx1 = x + x = F.relu(self.det_conv1(x) + resx1) + resx2 = x + + x = F.relu(self.det_conv2(x) + resx2 ) + resx3 = x + x = F.relu(self.det_conv3(x) + resx3) + resx4 = x + x = F.relu(self.det_conv4(x) + resx4) + resx5 = x + resx6 = torch.cat((resx1,resx2,resx3,resx4,resx5),1) + resx6 = self.deconv3(resx6); + x = F.relu(self.det_conv5(x) + resx6) + #forward through an LSTM + x = torch.cat((x, h), 1) + i = self.conv_i(x) + f = self.conv_f(x) + g = self.conv_g(x) + o = self.conv_o(x) + c = f * c + i * g + h = o * torch.tanh(c) + mask = self.sigmoid(self.det_conv_mask(h)) + mask_list.append(mask) + #contextual autoencoder + x = torch.cat((input, mask), 1) + x = self.conv1(x) + res1 = x + x = self.conv2(x) + x = self.conv3(x) + res2 = x + x = self.conv4(x) + x = self.conv5(x) + x = self.conv6(x) + x = self.diconv1(x) + x = self.diconv2(x) + x = self.diconv3(x) + x = self.diconv4(x) + x = self.conv7(x) + x = self.conv8(x) + #last 5th layer + frame1 = self.outframe1(x) + x = self.deconv1(x) + x = x + res2 + x = self.conv9(x) + #last 3rd layer + frame2 = self.outframe2(x) + x = self.deconv2(x) + x = x + res1 + x = self.conv10(x) + #last layer + x = self.output(x) + #masks, last 5th layer, last 3rd layer, last layer + return mask_list, frame1, frame2, x + +class Discriminator(nn.Module): + def __init__(self): + super(Discriminator, self).__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(3, 8, 5, 1, 2), + nn.ReLU() + ) + self.conv2 = nn.Sequential( + nn.Conv2d(8, 16, 5, 1, 2), + nn.ReLU() + ) + self.conv3 = nn.Sequential( + nn.Conv2d(16, 64, 5, 1, 2), + nn.ReLU() + ) + self.conv4 = nn.Sequential( + nn.Conv2d(64, 128, 5, 1, 2), + nn.ReLU() + ) + self.conv5 = nn.Sequential( + nn.Conv2d(128, 128, 5, 1, 2), + nn.ReLU() + ) + self.conv6 = nn.Sequential( + nn.Conv2d(128, 128, 5, 1, 2), + nn.ReLU() + ) + self.conv_mask = nn.Sequential( + nn.Conv2d(128, 1, 5, 1, 2) + ) + self.conv7 = nn.Sequential( + nn.Conv2d(128, 64, 5, 4, 1), + nn.ReLU() + ) + self.conv8 = nn.Sequential( + nn.Conv2d(64, 32, 5, 4, 1), + nn.ReLU() + ) + self.fc = nn.Sequential( + nn.Linear(32 * 14 * 14, 1024), + nn.Linear(1024, 1), + nn.Sigmoid() + ) + + #mask, probability + + def forward(self, x): + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + x = self.conv4(x) + x = self.conv5(x) + x = self.conv6(x) + mask = self.conv_mask(x) + x = self.conv7(x * mask) + #x = self.conv7(x) + x = self.conv8(x) + x = x.view(x.size(0), -1) + return mask, self.fc(x) \ No newline at end of file diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/parameters.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/parameters.py" new file mode 100644 index 0000000000000000000000000000000000000000..6544ed454abf0a0d2e96a233a2b5933ab7190712 --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/parameters.py" @@ -0,0 +1,34 @@ +import torch + +DISCRIMINATOR = True +ATTENTIVE_DISCRIMINATOR = True +ATTENTIVE_AUTOENCODER = True + +#parameters +TRAIN_PATH = '../dataset/train ' +# EVAL_PATH = '../dataset/test_a/' +# TEST_PATH = '../dataset/test_b/' + +# EVAL_PATH = 'D:/ziliao/bishe/rain_data_test_Heavy/rain_heavy_test' +# TEST_PATH = r'D:\ziliao\bishe\rain_data_test_Heavy\rain_heavy_test' + +EVAL_PATH = '../dataset/real_test_1000/' +TEST_PATH = '../dataset/real_test_1000/' + + +# TEST_PATH = '../dataset/' +BATCH_SZ = 2 +THETA = 0.6 +ITERATION = 4 +GAMMA = 0.05*ATTENTIVE_DISCRIMINATOR +EPOCHES = 100 +LR = 0.0005 +LAMBDA = 0.01*DISCRIMINATOR +ID = 'Detail_10' +BETA = 0.6 +step_size = 10 +# MODEL_PATH='./weights/netG_epoch_90_ID_cat_18_LOSS_0.46' +MODEL_PATH='./result/netG_epoch_80_ID_cat_18_LOSS_0.11' + +#device +device = torch.device('cuda:0') \ No newline at end of file diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/pix2pix.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/pix2pix.py" new file mode 100644 index 0000000000000000000000000000000000000000..7c675d763edd00b33fa3c497c7527df1d6da0981 --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/pix2pix.py" @@ -0,0 +1,14 @@ +import cv2 +import os + +data_path = 'D:/ziliao/bishe/dataset/test_b/data' +output_path = 'D:/ziliao/bishe/dataset/test_b/transformsize_data' + +img_list=os.listdir(data_path) + +for i in img_list: + temp = cv2.imread(data_path+"/"+i); + size = (224,224); + temp = cv2.resize(temp,size); + cv2.imwrite(output_path+"/"+i,temp); + diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/preprocessing.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/preprocessing.py" new file mode 100644 index 0000000000000000000000000000000000000000..08763979f2bb21c0e8a56133d572d785914cf258 --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/preprocessing.py" @@ -0,0 +1,140 @@ +from torch.utils.data import Dataset,DataLoader +import glob +import cv2 +import numpy as np +import torch +from parameters import * +import matplotlib.pyplot as plt +from torchvision.transforms import Compose, ToTensor +from torch.nn import functional as F +import random +def transform(): + return Compose([ + ToTensor(), + ]) + + +class DeRainDataset(Dataset): + def __init__(self, is_eval=False, is_test=False): + super(DeRainDataset, self).__init__() + + if is_test: + self.dataset = TEST_PATH + elif is_eval: + self.dataset = EVAL_PATH + else: + self.dataset = TRAIN_PATH + self.is_eval = is_eval; + self.is_test = is_test; + self.transform = transform(); + self.img_list = sorted(glob.glob(self.dataset+'/data/*')) + self.gt_list = sorted(glob.glob(self.dataset+'/gt/*')) + def __len__(self): + return len(self.img_list) + + def modcrop(self,img,rad_ih,rad_iw): + + img = img[rad_ih:rad_ih + 224, rad_iw:rad_iw + 224, :] + return img + + def get_SR(self,data , gt): + data = self.transform(data); + gt = self.transform(gt); + + # cv2.imshow("data",data.permute(1,2,0).cpu().numpy()); + # cv2.waitKey(); + c = gt.shape[0] + h = gt.shape[1] + w = gt.shape[2] + data=data.view(1,c,h,w) + gt= gt.view(1,c,h,w) + + LR_S = F.interpolate(data, scale_factor=0.5, mode='bilinear', align_corners=False) + + LR_S = F.interpolate(LR_S, scale_factor=2, mode='bilinear', align_corners=False) + + LR_D =data - LR_S + + GT_S = F.interpolate(gt, scale_factor=0.5, mode='bilinear', align_corners=False) + GT_S = F.interpolate(GT_S, scale_factor=2, mode='bilinear', align_corners=False) + GT_D = gt - GT_S + + return LR_D,GT_D,GT_S,LR_S,LR,GT; + + def __getitem__(self, idx): + img_name = self.img_list[idx] + gt_name = self.gt_list[idx] + + img = cv2.imread(img_name,1) + + gt = cv2.imread(gt_name,1) + (ih, iw, c) = img.shape + if self.is_eval==True or self.is_test==True: + h_pad = 4 -ih%4 + w_pad = 4 - iw%4 + img = np.lib.pad(img, pad_width=((h_pad//2, h_pad-h_pad//2), (w_pad//2, w_pad-w_pad//2), (0, 0)), mode='reflect') + else: + rad_ih = random.randint(0, ih - 224); + rad_iw = random.randint(0, iw - 224); + #each image is resized into a 3*224*224 numpy array and each pixel is squeezed into an interval of [0,1) + img = self.modcrop(img,rad_ih,rad_iw); + gt = self.modcrop(gt, rad_ih, rad_iw); + # rad_ih = random.randint(0, ih - 224); + # rad_iw = random.randint(0, iw - 224); + # # each image is resized into a 3*224*224 numpy array and each pixel is squeezed into an interval of [0,1) + # img = self.modcrop(img, rad_ih, rad_iw); + # gt = self.modcrop(gt, rad_ih, rad_iw); + #Detail + + # LR_D,GT_D,GT_S,LR_S,LR,GT = self.get_SR(img,gt); + # + # img= LR_D.squeeze(0); + # gt=GT_D.squeeze(0); + # + # img = img.permute(1,2,0); + # gt = gt.permute(1,2,0); + + # img_cv = img.permute(0,1,2).cpu().numpy(); + # cv2.imshow("",GT_S.squeeze(0).permute(1,2,0).numpy()); + # cv2.imshow("LR_Detail",img_cv); + # cv2.waitKey(); + #resize + if img.dtype == np.uint8: + img = (img / 255.0).astype('float32') + if gt.dtype == np.uint8: + gt = (gt / 255.0).astype('float32') + + return [img,gt] + +def get_mask(dg_img,img): + #convert to numpy arrays + dg_img, img = np.array(dg_img), np.array(img) + + # downgraded image - image + mask = np.fabs(dg_img-img) + + # threshold under 30 + mask[np.where(mask<(30.0/255.0))] = 0.0 + mask[np.where(mask>0.0)] = 1.0 + #avg? max? + mask = np.max(mask, axis=2) + mask = np.expand_dims(mask, axis=2) + return mask + + +def torch_variable(x, is_train): + return torch.tensor(np.array(x).transpose((0,3,1,2)),requires_grad=is_train).to(device) + +#input is a 3*224*224 tensor +def plot(x): + img = x.detach().cpu().numpy().transpose(1,2,0) + plt.imshow(img) + plt.show() + +#input is a 1*224*224 tensor +def mask_to_image(x): + return torch.cat((x,x,x),dim=0) + +def truncate(x): + return int(100*x)/100 + diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/preresult.rar" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/preresult.rar" new file mode 100644 index 0000000000000000000000000000000000000000..a5c97348a35ab46270f7b3c4bae3f8a0f7a986c1 Binary files /dev/null and "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/preresult.rar" differ diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/rename.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/rename.py" new file mode 100644 index 0000000000000000000000000000000000000000..0e9aa4201e22fe42ad2ff699f4469882f57ed04e --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/rename.py" @@ -0,0 +1,8 @@ +import os + +filenames = os.listdir(r'D:\ziliao\bishe\dataset\real_test_1000\gt') +path = r'D:\ziliao\bishe\dataset\real_test_1000\gt' +for name in filenames: + i = path+ '/'+ name.split('.')[0].zfill(5)+".png" + + os.rename(path+'/'+name,i) diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/test.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/test.py" new file mode 100644 index 0000000000000000000000000000000000000000..5b15558c085091747d86aaa8717a8b955c37208b --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/test.py" @@ -0,0 +1,25 @@ +import torch; +from parameters import * +from preprocessing import * +import numpy as np +from matric import * +import cv2; + +# test_path = '../dataset/test_b/data ' +# test_path = r'D:\ziliao\bishe\rain_data_test_Heavy\rain_heavy_test/data' +test_path = '../dataset/test_nature ' +output_path = '../dataset/ouput_natrue' +models_path = './weights/netG_epoch_100_ID_8_LOSS_0.1' + +model = torch.load(models_path).to(device) + +test_dataset = DeRainDataset(is_test=True) +test_loader = DataLoader(test_dataset) + +for i,(images,gts) in enumerate(test_loader): + images,gts = torch_variable(images,True), torch_variable(gts,False) + outputs = model(images.to(device))[-1] + img = outputs[0].permute(1,2,0).detach().cpu().numpy()*255 + temp_path = output_path + '/'+str(i+1)+'.png'; + cv2.imwrite(temp_path,img); + diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/train.py" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/train.py" new file mode 100644 index 0000000000000000000000000000000000000000..01414cf3c12e62954c9f5b89fb42c6aebd0434bd --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/train.py" @@ -0,0 +1,109 @@ +#data preprocessing +from torch.utils.data import Dataset,DataLoader +import numpy as np +from models import * +from preprocessing import * +from parameters import * +from loss import * +import PIL + +#processing data +train_dataset = DeRainDataset() +eval_dataset = DeRainDataset(is_eval=True) +test_dataset = DeRainDataset(is_test=True) +train_loader = DataLoader(train_dataset, batch_size=BATCH_SZ, shuffle=True) +eval_loader = DataLoader(eval_dataset) + +netG = Generator().to(device) +netD = Discriminator().to(device) + + +#MODELG_PATH = './weights/netG_epoch_200_ID_1_LOSS_0.03' +#MODELD_PATH = './weights/netD_epoch_200_ID_1_LOSS_1.38' +#netG = torch.load(MODELG_PATH).to(device) +optimG = torch.optim.Adam(netG.parameters(),lr=LR,betas=(0.5,0.99)) +#netD = torch.load(MODELD_PATH).to(device) +optimD = torch.optim.Adam(netD.parameters(),lr=LR,betas=(0.5,0.99)) +StepLR = torch.optim.lr_scheduler.StepLR(optimD, step_size=step_size, gamma=0.65) +StepLR = torch.optim.lr_scheduler.StepLR(optimG, step_size=step_size, gamma=0.65) +#check the validity of the model + +with torch.no_grad(): + input_img = torch.rand((1,3,224,224)).to(device) + output = netG(input_img) + masks,output_img = output[0], output[-1] +''' + plot(output_img[0]) + for mask in masks: + plot(mask_to_image(mask[0])) +''' + + +for epoch in range(0,1+EPOCHES): + loss_D_t , loss_G_t = 0,0 + count = 0 + for i, (datas,gts) in enumerate(train_loader): + + count += 1 + if count == 20: + print(count) + masks = [] + for i in range(datas.shape[0]): + masks.append(get_mask(datas[i],gts[i])) + masks = np.array(masks) + + datas,masks,gts = torch_variable(datas,True), torch_variable(masks,False), torch_variable(gts,False) + As, T1, T2, T3 = netG(datas) + Ts = [T1,T2,T3] + Os = T3 + #print("GENERATOR") + #loss of generator: attention loss + perceptual loss + multiscale loss + GAN loss + loss_G = ATTENTIVE_AUTOENCODER*AttentionLoss(As, masks) + loss_G += PerceptualLoss(gts, T3) + loss_G += MultiScaleLoss(Ts,gts.cpu(),batch=BATCH_SZ) + + D_map_O, D_fake = netD(Os) + loss_G += LAMBDA*GANLoss(D_fake,True) + + #print("DISCRIMINATOR") + #loss of discriminator: map loss + GANloss + D_map_R, D_real = netD(gts) + loss_D = GANLoss(D_fake,False) + GANLoss(D_real,True) + GAMMA*MAPLoss(D_map_O,D_map_R,As[-1].detach()) + print('epoch :',epoch) + print('loss D',loss_D.item()) + print('loss G',loss_G.item()) + optimG.zero_grad() + loss_G.backward(retain_graph=True) + optimG.step() + + optimD.zero_grad() + loss_D.backward() + optimD.step() + + loss_G_t += loss_G.item() + loss_D_t += loss_D.item() + with open('loss.txt','a+') as loss_file: + loss_file.write('loss_G'+","+str(loss_G.item())+"\n"); + loss_file.write('loss_D' + "," + str(loss_D.item()) + "\n"); + loss_G_t, loss_D_t = loss_G_t/count, loss_D_t/count + if epoch % 10 == 0: + for i in range(4): + cv2.imshow("",As[i].cpu().squeeze(0).permute(1,2,0).numpy()*255) + cv2.waitKey() + for i in range(Os.shape[0]): + #print(datas[i],Os[i],mask_to_image(As[-1][i]),mask_to_image(masks[i]),gts[i]) + img = torch.cat((datas[i],Os[i],mask_to_image(As[-1][i]),mask_to_image(masks[i]),gts[i]),dim=1) + img=img.permute(2,1,0).detach().cpu().numpy()*255 + # save(img,'./images/epoch_'+str(epoch)+"_ID_"+ID+'_'+str(i)+'.png') + path = './Images/epoch_'+str(epoch)+"_ID_"+ID+'_'+str(i)+'.png'; + print(path); + print("save") + # cv2.imshow("123",img); + # cv2.waitKey(); + cv2.imwrite(path,img); + print("EPOCH: ", epoch+200) + print("LOSS G: ", loss_G_t) + print("LOSS D: ", loss_D_t) + if epoch in [20,40,60,80,100,120,140,160,180,200]: + torch.save(netG,'./weights/netG_epoch_'+str(epoch)+"_ID_"+ID+"_LOSS_"+str(truncate(loss_G_t))) + torch.save(netD,'./weights/netD_epoch_'+str(epoch)+"_ID_"+ID+"_LOSS_"+str(truncate(loss_D_t))) \ No newline at end of file diff --git "a/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/train.sbatch" "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/train.sbatch" new file mode 100644 index 0000000000000000000000000000000000000000..74ee004f23b542433b8b237b29f5e0f951f045ff --- /dev/null +++ "b/code/2021_autumn/\345\256\213\345\255\220\346\235\260-\345\237\272\344\272\216CNN\347\232\204\345\215\225\345\271\205\351\233\250\345\244\251\345\233\276\345\203\217\345\244\215\345\216\237/train.sbatch" @@ -0,0 +1,10 @@ +#!/bin/bash +#SBATCH --account=gaozhi +#SBATCH --partition=gpu +#SBATCH --nodes=1 +#SBATCH --gres=gpu:2 +#SBATCH --time=72:00:00 + +export TORCH_HOME=/home/caomin/dat01/anaconda_cache/torch +module load nvidia/cuda/9.0 +python train.py \ No newline at end of file