From e7ed6dd522b457f6969288a96c3b6ed3a39bb79b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=8E=E4=B8=80=E5=8A=9B?= Date: Fri, 26 Jan 2024 14:09:14 +0800 Subject: [PATCH 1/4] add bisenetv2 based pytorch MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit link #I8Z24Z add bisenetv2 based pytorch Signed-off-by: ζŽδΈ€εŠ› --- .../bisenetv2/pytorch/.gitignore | 125 + .../bisenetv2/pytorch/LICENSE | 21 + .../bisenetv2/pytorch/README.md | 172 + .../bisenetv2/pytorch/configs/__init__.py | 21 + .../pytorch/configs/bisenet_customer.py | 23 + .../pytorch/configs/bisenetv1_ade20k.py | 24 + .../pytorch/configs/bisenetv1_city.py | 23 + .../pytorch/configs/bisenetv1_coco.py | 23 + .../pytorch/configs/bisenetv2_ade20k.py | 25 + .../pytorch/configs/bisenetv2_city.py | 24 + .../pytorch/configs/bisenetv2_coco.py | 24 + .../pytorch/datasets/cityscapes/train.txt | 2975 +++++++++++++++++ .../pytorch/datasets/cityscapes/val.txt | 500 +++ 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files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +## Coin: +play.py +preprocess_data.py +res/ +adj.md +tensorrt/build/* +datasets/coco/train.txt +datasets/coco/val.txt +datasets/ade20k/train.txt +datasets/ade20k/val.txt +pretrained/* +run.sh +openvino/build/* +openvino/output* +ncnn/models/* +*.onnx +tis/cpp_client/build/* +log*txt + +tvm/ diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/LICENSE b/cv/semantic_segmentation/bisenetv2/pytorch/LICENSE new file mode 100755 index 000000000..4a1a5721f --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 CoinCheung + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/README.md b/cv/semantic_segmentation/bisenetv2/pytorch/README.md new file mode 100755 index 000000000..602925835 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/README.md @@ -0,0 +1,172 @@ +# BiSeNetV1 & BiSeNetV2 + +My implementation of [BiSeNetV1](https://arxiv.org/abs/1808.00897) and [BiSeNetV2](https://arxiv.org/abs/2004.02147). + + +mIOUs and fps on cityscapes val set: +| none | ss | ssc | msf | mscf | fps(fp32/fp16/int8) | link | +|------|:--:|:---:|:---:|:----:|:---:|:----:| +| bisenetv1 | 75.44 | 76.94 | 77.45 | 78.86 | 25/78/141 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v1_city_new.pth) | +| bisenetv2 | 74.95 | 75.58 | 76.53 | 77.08 | 26/67/95 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v2_city.pth) | + +mIOUs on cocostuff val2017 set: +| none | ss | ssc | msf | mscf | link | +|------|:--:|:---:|:---:|:----:|:----:| +| bisenetv1 | 31.49 | 31.42 | 32.46 | 32.55 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v1_coco_new.pth) | +| bisenetv2 | 30.49 | 30.55 | 31.81 | 31.73 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v2_coco.pth) | + +mIOUs on ade20k val set: +| none | ss | ssc | msf | mscf | link | +|------|:--:|:---:|:---:|:----:|:----:| +| bisenetv1 | 36.15 | 36.04 | 37.27 | 36.58 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v1_ade20k.pth) | +| bisenetv2 | 32.53 | 32.43 | 33.23 | 31.72 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v2_ade20k.pth) | + +Tips: + +1. **ss** means single scale evaluation, **ssc** means single scale crop evaluation, **msf** means multi-scale evaluation with flip augment, and **mscf** means multi-scale crop evaluation with flip evaluation. The eval scales and crop size of multi-scales evaluation can be found in [configs](./configs/). + +2. The fps is tested in different way from the paper. For more information, please see [here](./tensorrt). + +3. The authors of bisenetv2 used cocostuff-10k, while I used cocostuff-123k(do not know how to say, just same 118k train and 5k val images as object detection). Thus the results maybe different from paper. + +4. The authors did not report results on ade20k, thus there is no official training settings, here I simply provide a "make it work" result. Maybe the results on ade20k can be boosted with better settings. + +5. The model has a big variance, which means that the results of training for many times would vary within a relatively big margin. For example, if you train bisenetv2 on cityscapes for many times, you will observe that the result of **ss** evaluation of bisenetv2 varies between 73.1-75.1. + + +## deploy trained models + +1. tensorrt +You can go to [tensorrt](./tensorrt) for details. + +2. ncnn +You can go to [ncnn](./ncnn) for details. + +3. openvino +You can go to [openvino](./openvino) for details. + +4. tis +Triton Inference Server(TIS) provides a service solution of deployment. You can go to [tis](./tis) for details. + + +## platform + +My platform is like this: + +* ubuntu 18.04 +* nvidia Tesla T4 gpu, driver 450.80.02 +* cuda 10.2/11.3 +* cudnn 8 +* miniconda python 3.8.8 +* pytorch 1.11.0 + + +## get start + +With a pretrained weight, you can run inference on an single image like this: + +``` +$ python tools/demo.py --config configs/bisenetv2_city.py --weight-path /path/to/your/weights.pth --img-path ./example.png +``` + +This would run inference on the image and save the result image to `./res.jpg`. + +Or you can run inference on a video like this: +``` +$ python tools/demo_video.py --config configs/bisenetv2_coco.py --weight-path res/model_final.pth --input ./video.mp4 --output res.mp4 +``` +This would generate segmentation file as `res.mp4`. If you want to read from camera, you can set `--input camera_id` rather than `input ./video.mp4`. + + +## prepare dataset + +1.cityscapes + +Register and download the dataset from the official [website](https://www.cityscapes-dataset.com/). Then decompress them into the `datasets/cityscapes` directory: +``` +$ mv /path/to/leftImg8bit_trainvaltest.zip datasets/cityscapes +$ mv /path/to/gtFine_trainvaltest.zip datasets/cityscapes +$ cd datasets/cityscapes +$ unzip leftImg8bit_trainvaltest.zip +$ unzip gtFine_trainvaltest.zip +``` + +2.cocostuff + +Download `train2017.zip`, `val2017.zip` and `stuffthingmaps_trainval2017.zip` split from official [website](https://cocodataset.org/#download). Then do as following: +``` +$ unzip train2017.zip +$ unzip val2017.zip +$ mv train2017/ /path/to/BiSeNet/datasets/coco/images +$ mv val2017/ /path/to/BiSeNet/datasets/coco/images + +$ unzip stuffthingmaps_trainval2017.zip +$ mv train2017/ /path/to/BiSeNet/datasets/coco/labels +$ mv val2017/ /path/to/BiSeNet/datasets/coco/labels + +$ cd /path/to/BiSeNet +$ python tools/gen_dataset_annos.py --dataset coco +``` + +3.ade20k + +Download `ADEChallengeData2016.zip` from this [website](http://sceneparsing.csail.mit.edu/) and unzip it. Then we can move the uncompressed folders to `datasets/ade20k`, and generate the txt files with the script I prepared for you: +``` +$ unzip ADEChallengeData2016.zip +$ mv ADEChallengeData2016/images /path/to/BiSeNet/datasets/ade20k/ +$ mv ADEChallengeData2016/annotations /path/to/BiSeNet/datasets/ade20k/ +$ python tools/gen_dataset_annos.py --ade20k +``` + + +4.custom dataset + +If you want to train on your own dataset, you should generate annotation files first with the format like this: +``` +munster_000002_000019_leftImg8bit.png,munster_000002_000019_gtFine_labelIds.png +frankfurt_000001_079206_leftImg8bit.png,frankfurt_000001_079206_gtFine_labelIds.png +... +``` +Each line is a pair of training sample and ground truth image path, which are separated by a single comma `,`. + +I recommand you to check the information of your dataset with the script: +``` +$ python tools/check_dataset_info.py --im_root /path/to/your/data_root --im_anns /path/to/your/anno_file +``` +This will print some of the information of your dataset. + +Then you need to change the field of `im_root` and `train/val_im_anns` in the config file. I prepared a demo config file for you named [`bisenet_customer.py`](./configs/bisenet_customer.py). You can start from this conig file. + + +## train + +Training commands I used to train the models can be found in [here](./dist_train.sh). + +Note: +1. though `bisenetv2` has fewer flops, it requires much more training iterations. The the training time of `bisenetv1` is shorter. +2. I used overall batch size of 16 to train all models. Since cocostuff has 171 categories, it requires more memory to train models on it. I split the 16 images into more gpus than 2, as I do with cityscapes. + + +## finetune from trained model + +You can also load the trained model weights and finetune from it, like this: +``` +$ export CUDA_VISIBLE_DEVICES=0,1 +$ torchrun --nproc_per_node=2 tools/train_amp.py --finetune-from ./res/model_final.pth --config ./configs/bisenetv2_city.py # or bisenetv1 +``` + + +## eval pretrained models +You can also evaluate a trained model like this: +``` +$ python tools/evaluate.py --config configs/bisenetv1_city.py --weight-path /path/to/your/weight.pth +``` +or you can use multi gpus: +``` +$ torchrun --nproc_per_node=2 tools/evaluate.py --config configs/bisenetv1_city.py --weight-path /path/to/your/weight.pth +``` + + +### Be aware that this is the refactored version of the original codebase. You can go to the `old` directory for original implementation if you need, though I believe you will not need it. + + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/configs/__init__.py b/cv/semantic_segmentation/bisenetv2/pytorch/configs/__init__.py new file mode 100755 index 000000000..d36a83d02 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/configs/__init__.py @@ -0,0 +1,21 @@ + + +import importlib + + +class cfg_dict(object): + + def __init__(self, d): + self.__dict__ = d + self.get = d.get + + +def set_cfg_from_file(cfg_path): + spec = importlib.util.spec_from_file_location('cfg_file', cfg_path) + cfg_file = importlib.util.module_from_spec(spec) + spec_loader = spec.loader.exec_module(cfg_file) + cfg = cfg_file.cfg + return cfg_dict(cfg) + + + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenet_customer.py b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenet_customer.py new file mode 100755 index 000000000..22f09c7a4 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenet_customer.py @@ -0,0 +1,23 @@ + +cfg = dict( + model_type='bisenetv1', + n_cats=20, + num_aux_heads=2, + lr_start=1e-2, + weight_decay=5e-4, + warmup_iters=1000, + max_iter=80000, + dataset='CustomerDataset', + im_root='./datasets/cityscapes', + train_im_anns='./datasets/cityscapes/train.txt', + val_im_anns='./datasets/cityscapes/val.txt', + scales=[0.75, 2.], + cropsize=[512, 512], + eval_crop=[512, 512], + eval_scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + ims_per_gpu=8, + eval_ims_per_gpu=2, + use_fp16=True, + use_sync_bn=False, + respth='./res', +) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv1_ade20k.py b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv1_ade20k.py new file mode 100755 index 000000000..03619e7cd --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv1_ade20k.py @@ -0,0 +1,24 @@ + +cfg = dict( + model_type='bisenetv1', + n_cats=150, + num_aux_heads=2, + lr_start=4e-2, + weight_decay=1e-4, + warmup_iters=1000, + max_iter=40000, + dataset='ADE20k', + im_root='./datasets/ade20k', + train_im_anns='./datasets/ade20k/train.txt', + val_im_anns='./datasets/ade20k/val.txt', + scales=[0.5, 2.], + cropsize=[512, 512], + eval_crop=[512, 512], + eval_scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + eval_start_shortside=512, + ims_per_gpu=8, + eval_ims_per_gpu=1, + use_fp16=True, + use_sync_bn=True, + respth='./res', +) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv1_city.py b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv1_city.py new file mode 100755 index 000000000..a1aff0bc9 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv1_city.py @@ -0,0 +1,23 @@ + +cfg = dict( + model_type='bisenetv1', + n_cats=19, + num_aux_heads=2, + lr_start=1e-2, + weight_decay=5e-4, + warmup_iters=1000, + max_iter=80000, + dataset='CityScapes', + im_root='./datasets/cityscapes', + train_im_anns='./datasets/cityscapes/train.txt', + val_im_anns='./datasets/cityscapes/val.txt', + scales=[0.75, 2.], + cropsize=[1024, 1024], + eval_crop=[1024, 1024], + eval_scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + ims_per_gpu=8, + eval_ims_per_gpu=2, + use_fp16=True, + use_sync_bn=False, + respth='./res', +) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv1_coco.py b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv1_coco.py new file mode 100755 index 000000000..914f10d1f --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv1_coco.py @@ -0,0 +1,23 @@ + +cfg = dict( + model_type='bisenetv1', + n_cats=171, + num_aux_heads=2, + lr_start=1e-2, + weight_decay=1e-4, + warmup_iters=1000, + max_iter=90000, + dataset='CocoStuff', + im_root='./datasets/coco', + train_im_anns='./datasets/coco/train.txt', + val_im_anns='./datasets/coco/val.txt', + scales=[0.5, 2.], + cropsize=[512, 512], + eval_crop=[512, 512], + eval_scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + ims_per_gpu=4, + eval_ims_per_gpu=1, + use_fp16=True, + use_sync_bn=True, + respth='./res', +) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv2_ade20k.py b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv2_ade20k.py new file mode 100755 index 000000000..cee581292 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv2_ade20k.py @@ -0,0 +1,25 @@ + +## bisenetv2 +cfg = dict( + model_type='bisenetv2', + n_cats=150, + num_aux_heads=4, + lr_start=5e-3, + weight_decay=1e-4, + warmup_iters=1000, + max_iter=160000, + dataset='ADE20k', + im_root='./datasets/ade20k', + train_im_anns='./datasets/ade20k/train.txt', + val_im_anns='./datasets/ade20k/val.txt', + scales=[0.5, 2.], + cropsize=[640, 640], + eval_crop=[640, 640], + eval_start_shortside=640, + eval_scales=[0.5, 0.75, 1, 1.25, 1.5, 1.75], + ims_per_gpu=2, + eval_ims_per_gpu=1, + use_fp16=True, + use_sync_bn=True, + respth='./res', +) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv2_city.py b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv2_city.py new file mode 100755 index 000000000..1898aaf76 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv2_city.py @@ -0,0 +1,24 @@ + +## bisenetv2 +cfg = dict( + model_type='bisenetv2', + n_cats=19, + num_aux_heads=4, + lr_start=5e-3, + weight_decay=5e-4, + warmup_iters=1000, + max_iter=150000, + dataset='CityScapes', + im_root='./datasets/cityscapes', + train_im_anns='./datasets/cityscapes/train.txt', + val_im_anns='./datasets/cityscapes/val.txt', + scales=[0.25, 2.], + cropsize=[512, 1024], + eval_crop=[1024, 1024], + eval_scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + ims_per_gpu=8, + eval_ims_per_gpu=2, + use_fp16=True, + use_sync_bn=True, + respth='./res', +) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv2_coco.py b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv2_coco.py new file mode 100755 index 000000000..ed8e67835 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/configs/bisenetv2_coco.py @@ -0,0 +1,24 @@ + +## bisenetv2 +cfg = dict( + model_type='bisenetv2', + n_cats=171, + num_aux_heads=4, + lr_start=5e-3, + weight_decay=1e-4, + warmup_iters=1000, + max_iter=180000, + dataset='CocoStuff', + im_root='./datasets/coco', + train_im_anns='./datasets/coco/train.txt', + val_im_anns='./datasets/coco/val.txt', + scales=[0.75, 2.], + cropsize=[640, 640], + eval_crop=[640, 640], + eval_scales=[0.5, 0.75, 1, 1.25, 1.5, 1.75], + ims_per_gpu=2, + eval_ims_per_gpu=1, + use_fp16=True, + use_sync_bn=True, + respth='./res', +) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/datasets/cityscapes/train.txt b/cv/semantic_segmentation/bisenetv2/pytorch/datasets/cityscapes/train.txt new file mode 100755 index 000000000..11a52d1b9 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/datasets/cityscapes/train.txt @@ -0,0 +1,2975 @@ 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b/cv/semantic_segmentation/bisenetv2/pytorch/dist_train.sh @@ -0,0 +1,46 @@ + +''' +NOTE: replace torchrun with torch.distributed.launch if you use older version of pytorch. I suggest you use the same version as I do since I have not tested compatibility with older version after updating. +''' + + +## bisenetv1 cityscapes +export CUDA_VISIBLE_DEVICES=0,1 +cfg_file=configs/bisenetv1_city.py +NGPUS=2 +torchrun --nproc_per_node=$NGPUS tools/train_amp.py --config $cfg_file + + +## bisenetv2 cityscapes +export CUDA_VISIBLE_DEVICES=0,1 +cfg_file=configs/bisenetv2_city.py +NGPUS=2 +torchrun --nproc_per_node=$NGPUS tools/train_amp.py --config $cfg_file + + +## bisenetv1 cocostuff +export CUDA_VISIBLE_DEVICES=0,1,2,3 +cfg_file=configs/bisenetv1_coco.py +NGPUS=4 +torchrun --nproc_per_node=$NGPUS tools/train_amp.py --config $cfg_file + + +## bisenetv2 cocostuff +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +cfg_file=configs/bisenetv2_coco.py +NGPUS=8 +torchrun --nproc_per_node=$NGPUS tools/train_amp.py --config $cfg_file + + +## bisenetv1 ade20k +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +cfg_file=configs/bisenetv1_ade20k.py +NGPUS=8 +torchrun 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zFl4jX#sB~SC3HntbYx+4WjbwdWNBu305UK!H7zkREif`vF*7 0 or pad_w > 0: + im = np.pad(im, ((pad_h, pad_h), (pad_w, pad_w), (0, 0))) + lb = np.pad(lb, ((pad_h, pad_h), (pad_w, pad_w)), 'constant', constant_values=255) + + im_h, im_w, _ = im.shape + sh, sw = np.random.random(2) + sh, sw = int(sh * (im_h - crop_h)), int(sw * (im_w - crop_w)) + return dict( + im=im[sh:sh+crop_h, sw:sw+crop_w, :].copy(), + lb=lb[sh:sh+crop_h, sw:sw+crop_w].copy() + ) + + + +class RandomHorizontalFlip(object): + + def __init__(self, p=0.5): + self.p = p + + def __call__(self, im_lb): + if np.random.random() < self.p: + return im_lb + im, lb = im_lb['im'], im_lb['lb'] + assert im.shape[:2] == lb.shape[:2] + return dict( + im=im[:, ::-1, :], + lb=lb[:, ::-1], + ) + + + +class ColorJitter(object): + + def __init__(self, brightness=None, contrast=None, saturation=None): + if not brightness is None and brightness >= 0: + self.brightness = [max(1-brightness, 0), 1+brightness] + if not contrast is None and contrast >= 0: + self.contrast = [max(1-contrast, 0), 1+contrast] + if not saturation is None and saturation >= 0: + self.saturation = [max(1-saturation, 0), 1+saturation] + + def __call__(self, im_lb): + im, lb = im_lb['im'], im_lb['lb'] + assert im.shape[:2] == lb.shape[:2] + if not self.brightness is None: + rate = np.random.uniform(*self.brightness) + im = self.adj_brightness(im, rate) + if not self.contrast is None: + rate = np.random.uniform(*self.contrast) + im = self.adj_contrast(im, rate) + if not self.saturation is None: + rate = np.random.uniform(*self.saturation) + im = self.adj_saturation(im, rate) + return dict(im=im, lb=lb,) + + def adj_saturation(self, im, rate): + M = np.float32([ + [1+2*rate, 1-rate, 1-rate], + [1-rate, 1+2*rate, 1-rate], + [1-rate, 1-rate, 1+2*rate] + ]) + shape = im.shape + im = np.matmul(im.reshape(-1, 3), M).reshape(shape)/3 + im = np.clip(im, 0, 255).astype(np.uint8) + return im + + def adj_brightness(self, im, rate): + table = np.array([ + i * rate for i in range(256) + ]).clip(0, 255).astype(np.uint8) + return table[im] + + def adj_contrast(self, im, rate): + table = np.array([ + 74 + (i - 74) * rate for i in range(256) + ]).clip(0, 255).astype(np.uint8) + return table[im] + + + + +class ToTensor(object): + ''' + mean and std should be of the channel order 'bgr' + ''' + def __init__(self, mean=(0, 0, 0), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, im_lb): + im, lb = im_lb['im'], im_lb['lb'] + im = im.transpose(2, 0, 1).astype(np.float32) + im = torch.from_numpy(im).div_(255) + dtype, device = im.dtype, im.device + mean = torch.as_tensor(self.mean, dtype=dtype, device=device)[:, None, None] + std = torch.as_tensor(self.std, dtype=dtype, device=device)[:, None, None] + im = im.sub_(mean).div_(std).clone() + if not lb is None: + lb = torch.from_numpy(lb.astype(np.int64).copy()).clone() + return dict(im=im, lb=lb) + + +class Compose(object): + + def __init__(self, do_list): + self.do_list = do_list + + def __call__(self, im_lb): + for comp in self.do_list: + im_lb = comp(im_lb) + return im_lb + + +class TransformationTrain(object): + + def __init__(self, scales, cropsize): + self.trans_func = Compose([ + RandomResizedCrop(scales, cropsize), + RandomHorizontalFlip(), + ColorJitter( + brightness=0.4, + contrast=0.4, + saturation=0.4 + ), + ]) + + def __call__(self, im_lb): + im_lb = self.trans_func(im_lb) + return im_lb + + +class TransformationVal(object): + + def __call__(self, im_lb): + im, lb = im_lb['im'], im_lb['lb'] + return dict(im=im, lb=lb) + + + +if __name__ == '__main__': + pass + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/lib/logger.py b/cv/semantic_segmentation/bisenetv2/pytorch/lib/logger.py new file mode 100755 index 000000000..29f6054c0 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/lib/logger.py @@ -0,0 +1,51 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +import os.path as osp +import time +import logging + +import torch.distributed as dist + + +def setup_logger(name, logpth): + logfile = '{}-{}.log'.format(name, time.strftime('%Y-%m-%d-%H-%M-%S')) + logfile = osp.join(logpth, logfile) + FORMAT = '%(levelname)s %(filename)s(%(lineno)d): %(message)s' + log_level = logging.INFO + if dist.is_initialized() and dist.get_rank() != 0: + log_level = logging.WARNING + try: + logging.basicConfig(level=log_level, format=FORMAT, filename=logfile, force=True) + except Exception: + for hl in logging.root.handlers: logging.root.removeHandler(hl) + logging.basicConfig(level=log_level, format=FORMAT, filename=logfile) + logging.root.addHandler(logging.StreamHandler()) + + +def print_log_msg(it, max_iter, lr, time_meter, loss_meter, loss_pre_meter, + loss_aux_meters): + t_intv, eta = time_meter.get() + loss_avg, _ = loss_meter.get() + loss_pre_avg, _ = loss_pre_meter.get() + loss_aux_avg = ', '.join(['{}: {:.4f}'.format(el.name, el.get()[0]) for el in loss_aux_meters]) + msg = ', '.join([ + 'iter: {it}/{max_it}', + 'lr: {lr:4f}', + 'eta: {eta}', + 'time: {time:.2f}', + 'loss: {loss:.4f}', + 'loss_pre: {loss_pre:.4f}', + ]).format( + it=it+1, + max_it=max_iter, + lr=lr, + time=t_intv, + eta=eta, + loss=loss_avg, + loss_pre=loss_pre_avg, + ) + msg += ', ' + loss_aux_avg + logger = logging.getLogger() + logger.info(msg) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/lib/lr_scheduler.py b/cv/semantic_segmentation/bisenetv2/pytorch/lib/lr_scheduler.py new file mode 100755 index 000000000..b09623aa2 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/lib/lr_scheduler.py @@ -0,0 +1,165 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import math +from bisect import bisect_right +import torch + + +class WarmupLrScheduler(torch.optim.lr_scheduler._LRScheduler): + + def __init__( + self, + optimizer, + warmup_iter=500, + warmup_ratio=5e-4, + warmup='exp', + last_epoch=-1, + ): + self.warmup_iter = warmup_iter + self.warmup_ratio = warmup_ratio + self.warmup = warmup + super(WarmupLrScheduler, self).__init__(optimizer, last_epoch) + + def get_lr(self): + ratio = self.get_lr_ratio() + lrs = [ratio * lr for lr in self.base_lrs] + return lrs + + def get_lr_ratio(self): + if self.last_epoch < self.warmup_iter: + ratio = self.get_warmup_ratio() + else: + ratio = self.get_main_ratio() + return ratio + + def get_main_ratio(self): + raise NotImplementedError + + def get_warmup_ratio(self): + assert self.warmup in ('linear', 'exp') + alpha = self.last_epoch / self.warmup_iter + if self.warmup == 'linear': + ratio = self.warmup_ratio + (1 - self.warmup_ratio) * alpha + elif self.warmup == 'exp': + ratio = self.warmup_ratio ** (1. - alpha) + return ratio + + +class WarmupPolyLrScheduler(WarmupLrScheduler): + + def __init__( + self, + optimizer, + power, + max_iter, + warmup_iter=500, + warmup_ratio=5e-4, + warmup='exp', + last_epoch=-1, + ): + self.power = power + self.max_iter = max_iter + super(WarmupPolyLrScheduler, self).__init__( + optimizer, warmup_iter, warmup_ratio, warmup, last_epoch) + + def get_main_ratio(self): + real_iter = self.last_epoch - self.warmup_iter + real_max_iter = self.max_iter - self.warmup_iter + alpha = real_iter / real_max_iter + ratio = (1 - alpha) ** self.power + return ratio + + +class WarmupExpLrScheduler(WarmupLrScheduler): + + def __init__( + self, + optimizer, + gamma, + interval=1, + warmup_iter=500, + warmup_ratio=5e-4, + warmup='exp', + last_epoch=-1, + ): + self.gamma = gamma + self.interval = interval + super(WarmupExpLrScheduler, self).__init__( + optimizer, warmup_iter, warmup_ratio, warmup, last_epoch) + + def get_main_ratio(self): + real_iter = self.last_epoch - self.warmup_iter + ratio = self.gamma ** (real_iter // self.interval) + return ratio + + +class WarmupCosineLrScheduler(WarmupLrScheduler): + + def __init__( + self, + optimizer, + max_iter, + eta_ratio=0, + warmup_iter=500, + warmup_ratio=5e-4, + warmup='exp', + last_epoch=-1, + ): + self.eta_ratio = eta_ratio + self.max_iter = max_iter + super(WarmupCosineLrScheduler, self).__init__( + optimizer, warmup_iter, warmup_ratio, warmup, last_epoch) + + def get_main_ratio(self): + real_iter = self.last_epoch - self.warmup_iter + real_max_iter = self.max_iter - self.warmup_iter + return self.eta_ratio + (1 - self.eta_ratio) * ( + 1 + math.cos(math.pi * self.last_epoch / real_max_iter)) / 2 + + +class WarmupStepLrScheduler(WarmupLrScheduler): + + def __init__( + self, + optimizer, + milestones: list, + gamma=0.1, + warmup_iter=500, + warmup_ratio=5e-4, + warmup='exp', + last_epoch=-1, + ): + self.milestones = milestones + self.gamma = gamma + super(WarmupStepLrScheduler, self).__init__( + optimizer, warmup_iter, warmup_ratio, warmup, last_epoch) + + def get_main_ratio(self): + real_iter = self.last_epoch - self.warmup_iter + ratio = self.gamma ** bisect_right(self.milestones, real_iter) + return ratio + + +if __name__ == "__main__": + model = torch.nn.Conv2d(3, 16, 3, 1, 1) + optim = torch.optim.SGD(model.parameters(), lr=1e-3) + + max_iter = 20000 + lr_scheduler = WarmupPolyLrScheduler(optim, 0.9, max_iter, 200, 0.1, 'linear', -1) + lrs = [] + for _ in range(max_iter): + lr = lr_scheduler.get_lr()[0] + print(lr) + lrs.append(lr) + lr_scheduler.step() + import matplotlib + import matplotlib.pyplot as plt + import numpy as np + lrs = np.array(lrs) + n_lrs = len(lrs) + plt.plot(np.arange(n_lrs), lrs) + plt.grid() + plt.show() + + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/lib/meters.py b/cv/semantic_segmentation/bisenetv2/pytorch/lib/meters.py new file mode 100755 index 000000000..897343189 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/lib/meters.py @@ -0,0 +1,43 @@ + +import time +import datetime + +class TimeMeter(object): + + def __init__(self, max_iter): + self.iter = 0 + self.max_iter = max_iter + self.st = time.time() + self.global_st = self.st + self.curr = self.st + + def update(self): + self.iter += 1 + + def get(self): + self.curr = time.time() + interv = self.curr - self.st + global_interv = self.curr - self.global_st + eta = int((self.max_iter-self.iter) * (global_interv / (self.iter+1))) + eta = str(datetime.timedelta(seconds=eta)) + self.st = self.curr + return interv, eta + + +class AvgMeter(object): + + def __init__(self, name): + self.name = name + self.seq = [] + self.global_seq = [] + + def update(self, val): + self.seq.append(val) + self.global_seq.append(val) + + def get(self): + avg = sum(self.seq) / len(self.seq) + global_avg = sum(self.global_seq) / len(self.global_seq) + self.seq = [] + return avg, global_avg + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/__init__.py b/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/__init__.py new file mode 100755 index 000000000..2c7350378 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/__init__.py @@ -0,0 +1,10 @@ + + +from .bisenetv1 import BiSeNetV1 +from .bisenetv2 import BiSeNetV2 + + +model_factory = { + 'bisenetv1': BiSeNetV1, + 'bisenetv2': BiSeNetV2, +} diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/bisenetv1.py b/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/bisenetv1.py new file mode 100755 index 000000000..678b6f856 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/bisenetv1.py @@ -0,0 +1,327 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from .resnet import Resnet18 + +from torch.nn import BatchNorm2d + + +class ConvBNReLU(nn.Module): + + def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs): + super(ConvBNReLU, self).__init__() + self.conv = nn.Conv2d(in_chan, + out_chan, + kernel_size = ks, + stride = stride, + padding = padding, + bias = False) + self.bn = BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + x = self.relu(x) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + +class UpSample(nn.Module): + + def __init__(self, n_chan, factor=2): + super(UpSample, self).__init__() + out_chan = n_chan * factor * factor + self.proj = nn.Conv2d(n_chan, out_chan, 1, 1, 0) + self.up = nn.PixelShuffle(factor) + self.init_weight() + + def forward(self, x): + feat = self.proj(x) + feat = self.up(feat) + return feat + + def init_weight(self): + nn.init.xavier_normal_(self.proj.weight, gain=1.) + + +class BiSeNetOutput(nn.Module): + + def __init__(self, in_chan, mid_chan, n_classes, up_factor=32, *args, **kwargs): + super(BiSeNetOutput, self).__init__() + self.up_factor = up_factor + out_chan = n_classes + self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) + self.conv_out = nn.Conv2d(mid_chan, out_chan, kernel_size=1, bias=True) + self.up = nn.Upsample(scale_factor=up_factor, + mode='bilinear', align_corners=False) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = self.conv_out(x) + x = self.up(x) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.modules.batchnorm._BatchNorm): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class AttentionRefinementModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(AttentionRefinementModule, self).__init__() + self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) + self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False) + self.bn_atten = BatchNorm2d(out_chan) + # self.sigmoid_atten = nn.Sigmoid() + self.init_weight() + + def forward(self, x): + feat = self.conv(x) + atten = torch.mean(feat, dim=(2, 3), keepdim=True) + atten = self.conv_atten(atten) + atten = self.bn_atten(atten) + # atten = self.sigmoid_atten(atten) + atten = atten.sigmoid() + out = torch.mul(feat, atten) + return out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + +class ContextPath(nn.Module): + def __init__(self, *args, **kwargs): + super(ContextPath, self).__init__() + self.resnet = Resnet18() + self.arm16 = AttentionRefinementModule(256, 128) + self.arm32 = AttentionRefinementModule(512, 128) + self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) + self.up32 = nn.Upsample(scale_factor=2.) + self.up16 = nn.Upsample(scale_factor=2.) + + self.init_weight() + + def forward(self, x): + feat8, feat16, feat32 = self.resnet(x) + + avg = torch.mean(feat32, dim=(2, 3), keepdim=True) + avg = self.conv_avg(avg) + + feat32_arm = self.arm32(feat32) + feat32_sum = feat32_arm + avg + feat32_up = self.up32(feat32_sum) + feat32_up = self.conv_head32(feat32_up) + + feat16_arm = self.arm16(feat16) + feat16_sum = feat16_arm + feat32_up + feat16_up = self.up16(feat16_sum) + feat16_up = self.conv_head16(feat16_up) + + return feat16_up, feat32_up # x8, x16 + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.modules.batchnorm._BatchNorm): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class SpatialPath(nn.Module): + def __init__(self, *args, **kwargs): + super(SpatialPath, self).__init__() + self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3) + self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0) + self.init_weight() + + def forward(self, x): + feat = self.conv1(x) + feat = self.conv2(feat) + feat = self.conv3(feat) + feat = self.conv_out(feat) + return feat + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.modules.batchnorm._BatchNorm): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class FeatureFusionModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(FeatureFusionModule, self).__init__() + self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) + ## use conv-bn instead of 2 layer mlp, so that tensorrt 7.2.3.4 can work for fp16 + self.conv = nn.Conv2d(out_chan, + out_chan, + kernel_size = 1, + stride = 1, + padding = 0, + bias = False) + self.bn = nn.BatchNorm2d(out_chan) + # self.conv1 = nn.Conv2d(out_chan, + # out_chan//4, + # kernel_size = 1, + # stride = 1, + # padding = 0, + # bias = False) + # self.conv2 = nn.Conv2d(out_chan//4, + # out_chan, + # kernel_size = 1, + # stride = 1, + # padding = 0, + # bias = False) + # self.relu = nn.ReLU(inplace=True) + self.init_weight() + + def forward(self, fsp, fcp): + fcat = torch.cat([fsp, fcp], dim=1) + feat = self.convblk(fcat) + atten = torch.mean(feat, dim=(2, 3), keepdim=True) + atten = self.conv(atten) + atten = self.bn(atten) + # atten = self.conv1(atten) + # atten = self.relu(atten) + # atten = self.conv2(atten) + atten = atten.sigmoid() + feat_atten = torch.mul(feat, atten) + feat_out = feat_atten + feat + return feat_out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.modules.batchnorm._BatchNorm): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class BiSeNetV1(nn.Module): + + def __init__(self, n_classes, aux_mode='train', *args, **kwargs): + super(BiSeNetV1, self).__init__() + self.cp = ContextPath() + self.sp = SpatialPath() + self.ffm = FeatureFusionModule(256, 256) + self.conv_out = BiSeNetOutput(256, 256, n_classes, up_factor=8) + self.aux_mode = aux_mode + if self.aux_mode == 'train': + self.conv_out16 = BiSeNetOutput(128, 64, n_classes, up_factor=8) + self.conv_out32 = BiSeNetOutput(128, 64, n_classes, up_factor=16) + self.init_weight() + + def forward(self, x): + H, W = x.size()[2:] + feat_cp8, feat_cp16 = self.cp(x) + feat_sp = self.sp(x) + feat_fuse = self.ffm(feat_sp, feat_cp8) + + feat_out = self.conv_out(feat_fuse) + if self.aux_mode == 'train': + feat_out16 = self.conv_out16(feat_cp8) + feat_out32 = self.conv_out32(feat_cp16) + return feat_out, feat_out16, feat_out32 + elif self.aux_mode == 'eval': + return feat_out, + elif self.aux_mode == 'pred': + feat_out = feat_out.argmax(dim=1) + return feat_out + else: + raise NotImplementedError + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], [] + for name, child in self.named_children(): + child_wd_params, child_nowd_params = child.get_params() + if isinstance(child, (FeatureFusionModule, BiSeNetOutput)): + lr_mul_wd_params += child_wd_params + lr_mul_nowd_params += child_nowd_params + else: + wd_params += child_wd_params + nowd_params += child_nowd_params + return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params + + +if __name__ == "__main__": + net = BiSeNetV1(19) + net.cuda() + net.eval() + in_ten = torch.randn(16, 3, 640, 480).cuda() + out, out16, out32 = net(in_ten) + print(out.shape) + print(out16.shape) + print(out32.shape) + + net.get_params() diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/bisenetv2.py b/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/bisenetv2.py new file mode 100755 index 000000000..c8307d85b --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/bisenetv2.py @@ -0,0 +1,443 @@ + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.model_zoo as modelzoo + +backbone_url = 'https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/backbone_v2.pth' + + +class ConvBNReLU(nn.Module): + + def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, + dilation=1, groups=1, bias=False): + super(ConvBNReLU, self).__init__() + self.conv = nn.Conv2d( + in_chan, out_chan, kernel_size=ks, stride=stride, + padding=padding, dilation=dilation, + groups=groups, bias=bias) + self.bn = nn.BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + feat = self.conv(x) + feat = self.bn(feat) + feat = self.relu(feat) + return feat + + +class UpSample(nn.Module): + + def __init__(self, n_chan, factor=2): + super(UpSample, self).__init__() + out_chan = n_chan * factor * factor + self.proj = nn.Conv2d(n_chan, out_chan, 1, 1, 0) + self.up = nn.PixelShuffle(factor) + self.init_weight() + + def forward(self, x): + feat = self.proj(x) + feat = self.up(feat) + return feat + + def init_weight(self): + nn.init.xavier_normal_(self.proj.weight, gain=1.) + + + +class DetailBranch(nn.Module): + + def __init__(self): + super(DetailBranch, self).__init__() + self.S1 = nn.Sequential( + ConvBNReLU(3, 64, 3, stride=2), + ConvBNReLU(64, 64, 3, stride=1), + ) + self.S2 = nn.Sequential( + ConvBNReLU(64, 64, 3, stride=2), + ConvBNReLU(64, 64, 3, stride=1), + ConvBNReLU(64, 64, 3, stride=1), + ) + self.S3 = nn.Sequential( + ConvBNReLU(64, 128, 3, stride=2), + ConvBNReLU(128, 128, 3, stride=1), + ConvBNReLU(128, 128, 3, stride=1), + ) + + def forward(self, x): + feat = self.S1(x) + feat = self.S2(feat) + feat = self.S3(feat) + return feat + + +class StemBlock(nn.Module): + + def __init__(self): + super(StemBlock, self).__init__() + self.conv = ConvBNReLU(3, 16, 3, stride=2) + self.left = nn.Sequential( + ConvBNReLU(16, 8, 1, stride=1, padding=0), + ConvBNReLU(8, 16, 3, stride=2), + ) + self.right = nn.MaxPool2d( + kernel_size=3, stride=2, padding=1, ceil_mode=False) + self.fuse = ConvBNReLU(32, 16, 3, stride=1) + + def forward(self, x): + feat = self.conv(x) + feat_left = self.left(feat) + feat_right = self.right(feat) + feat = torch.cat([feat_left, feat_right], dim=1) + feat = self.fuse(feat) + return feat + + +class CEBlock(nn.Module): + + def __init__(self): + super(CEBlock, self).__init__() + self.bn = nn.BatchNorm2d(128) + self.conv_gap = ConvBNReLU(128, 128, 1, stride=1, padding=0) + #TODO: in paper here is naive conv2d, no bn-relu + self.conv_last = ConvBNReLU(128, 128, 3, stride=1) + + def forward(self, x): + feat = torch.mean(x, dim=(2, 3), keepdim=True) + feat = self.bn(feat) + feat = self.conv_gap(feat) + feat = feat + x + feat = self.conv_last(feat) + return feat + + +class GELayerS1(nn.Module): + + def __init__(self, in_chan, out_chan, exp_ratio=6): + super(GELayerS1, self).__init__() + mid_chan = in_chan * exp_ratio + self.conv1 = ConvBNReLU(in_chan, in_chan, 3, stride=1) + self.dwconv = nn.Sequential( + nn.Conv2d( + in_chan, mid_chan, kernel_size=3, stride=1, + padding=1, groups=in_chan, bias=False), + nn.BatchNorm2d(mid_chan), + nn.ReLU(inplace=True), # not shown in paper + ) + self.conv2 = nn.Sequential( + nn.Conv2d( + mid_chan, out_chan, kernel_size=1, stride=1, + padding=0, bias=False), + nn.BatchNorm2d(out_chan), + ) + self.conv2[1].last_bn = True + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + feat = self.conv1(x) + feat = self.dwconv(feat) + feat = self.conv2(feat) + feat = feat + x + feat = self.relu(feat) + return feat + + +class GELayerS2(nn.Module): + + def __init__(self, in_chan, out_chan, exp_ratio=6): + super(GELayerS2, self).__init__() + mid_chan = in_chan * exp_ratio + self.conv1 = ConvBNReLU(in_chan, in_chan, 3, stride=1) + self.dwconv1 = nn.Sequential( + nn.Conv2d( + in_chan, mid_chan, kernel_size=3, stride=2, + padding=1, groups=in_chan, bias=False), + nn.BatchNorm2d(mid_chan), + ) + self.dwconv2 = nn.Sequential( + nn.Conv2d( + mid_chan, mid_chan, kernel_size=3, stride=1, + padding=1, groups=mid_chan, bias=False), + nn.BatchNorm2d(mid_chan), + nn.ReLU(inplace=True), # not shown in paper + ) + self.conv2 = nn.Sequential( + nn.Conv2d( + mid_chan, out_chan, kernel_size=1, stride=1, + padding=0, bias=False), + nn.BatchNorm2d(out_chan), + ) + self.conv2[1].last_bn = True + self.shortcut = nn.Sequential( + nn.Conv2d( + in_chan, in_chan, kernel_size=3, stride=2, + padding=1, groups=in_chan, bias=False), + nn.BatchNorm2d(in_chan), + nn.Conv2d( + in_chan, out_chan, kernel_size=1, stride=1, + padding=0, bias=False), + nn.BatchNorm2d(out_chan), + ) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + feat = self.conv1(x) + feat = self.dwconv1(feat) + feat = self.dwconv2(feat) + feat = self.conv2(feat) + shortcut = self.shortcut(x) + feat = feat + shortcut + feat = self.relu(feat) + return feat + + +class SegmentBranch(nn.Module): + + def __init__(self): + super(SegmentBranch, self).__init__() + self.S1S2 = StemBlock() + self.S3 = nn.Sequential( + GELayerS2(16, 32), + GELayerS1(32, 32), + ) + self.S4 = nn.Sequential( + GELayerS2(32, 64), + GELayerS1(64, 64), + ) + self.S5_4 = nn.Sequential( + GELayerS2(64, 128), + GELayerS1(128, 128), + GELayerS1(128, 128), + GELayerS1(128, 128), + ) + self.S5_5 = CEBlock() + + def forward(self, x): + feat2 = self.S1S2(x) + feat3 = self.S3(feat2) + feat4 = self.S4(feat3) + feat5_4 = self.S5_4(feat4) + feat5_5 = self.S5_5(feat5_4) + return feat2, feat3, feat4, feat5_4, feat5_5 + + +class BGALayer(nn.Module): + + def __init__(self): + super(BGALayer, self).__init__() + self.left1 = nn.Sequential( + nn.Conv2d( + 128, 128, kernel_size=3, stride=1, + padding=1, groups=128, bias=False), + nn.BatchNorm2d(128), + nn.Conv2d( + 128, 128, kernel_size=1, stride=1, + padding=0, bias=False), + ) + self.left2 = nn.Sequential( + nn.Conv2d( + 128, 128, kernel_size=3, stride=2, + padding=1, bias=False), + nn.BatchNorm2d(128), + nn.AvgPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False) + ) + self.right1 = nn.Sequential( + nn.Conv2d( + 128, 128, kernel_size=3, stride=1, + padding=1, bias=False), + nn.BatchNorm2d(128), + ) + self.right2 = nn.Sequential( + nn.Conv2d( + 128, 128, kernel_size=3, stride=1, + padding=1, groups=128, bias=False), + nn.BatchNorm2d(128), + nn.Conv2d( + 128, 128, kernel_size=1, stride=1, + padding=0, bias=False), + ) + self.up1 = nn.Upsample(scale_factor=4) + self.up2 = nn.Upsample(scale_factor=4) + ##TODO: does this really has no relu? + self.conv = nn.Sequential( + nn.Conv2d( + 128, 128, kernel_size=3, stride=1, + padding=1, bias=False), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), # not shown in paper + ) + + def forward(self, x_d, x_s): + dsize = x_d.size()[2:] + left1 = self.left1(x_d) + left2 = self.left2(x_d) + right1 = self.right1(x_s) + right2 = self.right2(x_s) + right1 = self.up1(right1) + left = left1 * torch.sigmoid(right1) + right = left2 * torch.sigmoid(right2) + right = self.up2(right) + out = self.conv(left + right) + return out + + + +class SegmentHead(nn.Module): + + def __init__(self, in_chan, mid_chan, n_classes, up_factor=8, aux=True): + super(SegmentHead, self).__init__() + self.conv = ConvBNReLU(in_chan, mid_chan, 3, stride=1) + self.drop = nn.Dropout(0.1) + self.up_factor = up_factor + + out_chan = n_classes + mid_chan2 = up_factor * up_factor if aux else mid_chan + up_factor = up_factor // 2 if aux else up_factor + self.conv_out = nn.Sequential( + nn.Sequential( + nn.Upsample(scale_factor=2), + ConvBNReLU(mid_chan, mid_chan2, 3, stride=1) + ) if aux else nn.Identity(), + nn.Conv2d(mid_chan2, out_chan, 1, 1, 0, bias=True), + nn.Upsample(scale_factor=up_factor, mode='bilinear', align_corners=False) + ) + + def forward(self, x): + feat = self.conv(x) + feat = self.drop(feat) + feat = self.conv_out(feat) + return feat + + +class BiSeNetV2(nn.Module): + + def __init__(self, n_classes, aux_mode='train'): + super(BiSeNetV2, self).__init__() + self.aux_mode = aux_mode + self.detail = DetailBranch() + self.segment = SegmentBranch() + self.bga = BGALayer() + + ## TODO: what is the number of mid chan ? + self.head = SegmentHead(128, 1024, n_classes, up_factor=8, aux=False) + if self.aux_mode == 'train': + self.aux2 = SegmentHead(16, 128, n_classes, up_factor=4) + self.aux3 = SegmentHead(32, 128, n_classes, up_factor=8) + self.aux4 = SegmentHead(64, 128, n_classes, up_factor=16) + self.aux5_4 = SegmentHead(128, 128, n_classes, up_factor=32) + + self.init_weights() + + def forward(self, x): + size = x.size()[2:] + feat_d = self.detail(x) + feat2, feat3, feat4, feat5_4, feat_s = self.segment(x) + feat_head = self.bga(feat_d, feat_s) + + logits = self.head(feat_head) + if self.aux_mode == 'train': + logits_aux2 = self.aux2(feat2) + logits_aux3 = self.aux3(feat3) + logits_aux4 = self.aux4(feat4) + logits_aux5_4 = self.aux5_4(feat5_4) + return logits, logits_aux2, logits_aux3, logits_aux4, logits_aux5_4 + elif self.aux_mode == 'eval': + return logits, + elif self.aux_mode == 'pred': + pred = logits.argmax(dim=1) + return pred + else: + raise NotImplementedError + + def init_weights(self): + for name, module in self.named_modules(): + if isinstance(module, (nn.Conv2d, nn.Linear)): + nn.init.kaiming_normal_(module.weight, mode='fan_out') + if not module.bias is None: nn.init.constant_(module.bias, 0) + elif isinstance(module, nn.modules.batchnorm._BatchNorm): + if hasattr(module, 'last_bn') and module.last_bn: + nn.init.zeros_(module.weight) + else: + nn.init.ones_(module.weight) + nn.init.zeros_(module.bias) + self.load_pretrain() + + + def load_pretrain(self): + state = modelzoo.load_url(backbone_url) + for name, child in self.named_children(): + if name in state.keys(): + child.load_state_dict(state[name], strict=True) + + def get_params(self): + def add_param_to_list(mod, wd_params, nowd_params): + for param in mod.parameters(): + if param.dim() == 1: + nowd_params.append(param) + elif param.dim() == 4: + wd_params.append(param) + else: + print(name) + + wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], [] + for name, child in self.named_children(): + if 'head' in name or 'aux' in name: + add_param_to_list(child, lr_mul_wd_params, lr_mul_nowd_params) + else: + add_param_to_list(child, wd_params, nowd_params) + return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params + + + +if __name__ == "__main__": + # x = torch.randn(16, 3, 1024, 2048) + # detail = DetailBranch() + # feat = detail(x) + # print('detail', feat.size()) + # + # x = torch.randn(16, 3, 1024, 2048) + # stem = StemBlock() + # feat = stem(x) + # print('stem', feat.size()) + # + # x = torch.randn(16, 128, 16, 32) + # ceb = CEBlock() + # feat = ceb(x) + # print(feat.size()) + # + # x = torch.randn(16, 32, 16, 32) + # ge1 = GELayerS1(32, 32) + # feat = ge1(x) + # print(feat.size()) + # + # x = torch.randn(16, 16, 16, 32) + # ge2 = GELayerS2(16, 32) + # feat = ge2(x) + # print(feat.size()) + # + # left = torch.randn(16, 128, 64, 128) + # right = torch.randn(16, 128, 16, 32) + # bga = BGALayer() + # feat = bga(left, right) + # print(feat.size()) + # + # x = torch.randn(16, 128, 64, 128) + # head = SegmentHead(128, 128, 19) + # logits = head(x) + # print(logits.size()) + # + # x = torch.randn(16, 3, 1024, 2048) + # segment = SegmentBranch() + # feat = segment(x)[0] + # print(feat.size()) + # + x = torch.randn(16, 3, 1024, 2048) + model = BiSeNetV2(n_classes=19) + outs = model(x) + for out in outs: + print(out.size()) + # print(logits.size()) + + # for name, param in model.named_parameters(): + # if len(param.size()) == 1: + # print(name) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/resnet.py b/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/resnet.py new file mode 100755 index 000000000..7b039ed73 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/lib/models/resnet.py @@ -0,0 +1,113 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.model_zoo as modelzoo + +resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' + + +from torch.nn import BatchNorm2d + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + def __init__(self, in_chan, out_chan, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(in_chan, out_chan, stride) + self.bn1 = BatchNorm2d(out_chan) + self.conv2 = conv3x3(out_chan, out_chan) + self.bn2 = BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.downsample = None + if in_chan != out_chan or stride != 1: + self.downsample = nn.Sequential( + nn.Conv2d(in_chan, out_chan, + kernel_size=1, stride=stride, bias=False), + BatchNorm2d(out_chan), + ) + + def forward(self, x): + residual = self.conv1(x) + residual = self.bn1(residual) + residual = self.relu(residual) + residual = self.conv2(residual) + residual = self.bn2(residual) + + shortcut = x + if self.downsample is not None: + shortcut = self.downsample(x) + + out = shortcut + residual + out = self.relu(out) + return out + + +def create_layer_basic(in_chan, out_chan, bnum, stride=1): + layers = [BasicBlock(in_chan, out_chan, stride=stride)] + for i in range(bnum-1): + layers.append(BasicBlock(out_chan, out_chan, stride=1)) + return nn.Sequential(*layers) + + +class Resnet18(nn.Module): + def __init__(self): + super(Resnet18, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) + self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) + self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) + self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) + self.init_weight() + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + feat8 = self.layer2(x) # 1/8 + feat16 = self.layer3(feat8) # 1/16 + feat32 = self.layer4(feat16) # 1/32 + return feat8, feat16, feat32 + + def init_weight(self): + state_dict = modelzoo.load_url(resnet18_url) + self_state_dict = self.state_dict() + for k, v in state_dict.items(): + if 'fc' in k: continue + self_state_dict.update({k: v}) + self.load_state_dict(self_state_dict) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.modules.batchnorm._BatchNorm): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +if __name__ == "__main__": + net = Resnet18() + x = torch.randn(16, 3, 224, 224) + out = net(x) + print(out[0].size()) + print(out[1].size()) + print(out[2].size()) + net.get_params() diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/lib/ohem_ce_loss.py b/cv/semantic_segmentation/bisenetv2/pytorch/lib/ohem_ce_loss.py new file mode 100755 index 000000000..850a9cb00 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/lib/ohem_ce_loss.py @@ -0,0 +1,46 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +# import ohem_cpp +# class OhemCELoss(nn.Module): +# +# def __init__(self, thresh, lb_ignore=255): +# super(OhemCELoss, self).__init__() +# self.score_thresh = thresh +# self.lb_ignore = lb_ignore +# self.criteria = nn.CrossEntropyLoss(ignore_index=lb_ignore, reduction='mean') +# +# def forward(self, logits, labels): +# n_min = labels[labels != self.lb_ignore].numel() // 16 +# labels = ohem_cpp.score_ohem_label( +# logits, labels, self.lb_ignore, self.score_thresh, n_min).detach() +# loss = self.criteria(logits, labels) +# return loss + + +class OhemCELoss(nn.Module): + + def __init__(self, thresh, lb_ignore=255): + super(OhemCELoss, self).__init__() + self.thresh = -torch.log(torch.tensor(thresh, requires_grad=False, dtype=torch.float)).cuda() + self.lb_ignore = lb_ignore + self.criteria = nn.CrossEntropyLoss(ignore_index=lb_ignore, reduction='none') + + def forward(self, logits, labels): + n_min = labels[labels != self.lb_ignore].numel() // 16 + loss = self.criteria(logits, labels).view(-1) + loss_hard = loss[loss > self.thresh] + if loss_hard.numel() < n_min: + loss_hard, _ = loss.topk(n_min) + return torch.mean(loss_hard) + + +if __name__ == '__main__': + pass + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/ncnn/CMakeLists.txt b/cv/semantic_segmentation/bisenetv2/pytorch/ncnn/CMakeLists.txt new file mode 100755 index 000000000..a6e80ad1b --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/ncnn/CMakeLists.txt @@ -0,0 +1,16 @@ +CMAKE_MINIMUM_REQUIRED(VERSION 3.15) + +PROJECT(segment) + +set(CMAKE_CXX_FLAGS "-std=c++14 -O2") + + +set (ncnn_DIR ${NCNN_ROOT}/lib/cmake/ncnn) +find_package(OpenCV REQUIRED) +find_package(OpenMP REQUIRED) +find_package(ncnn REQUIRED) + + +add_executable(segment segment.cpp) +target_include_directories(segment PUBLIC ${OpenCV_INCLUDE_DIRS}) +target_link_libraries(segment ${OpenCV_LIBRARIES} ncnn OpenMP::OpenMP_CXX) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/ncnn/README.md b/cv/semantic_segmentation/bisenetv2/pytorch/ncnn/README.md new file mode 100755 index 000000000..fb27b93ac --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/ncnn/README.md @@ -0,0 +1,80 @@ + +### My platform + +* raspberry pi 3b +* 2022-04-04-raspios-bullseye-armhf-lite.img +* cpu: 4 core armv8, memory: 1G + + + +### Install ncnn + +Just follow the ncnn official tutoral of [build-for-linux](https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-linux) to install ncnn. Following steps are all carried out on my raspberry pi: + +**step 1:** install dependencies +``` +$ sudo apt install build-essential git cmake libprotobuf-dev protobuf-compiler libopencv-dev +``` + +**step 2:** (optional) install vulkan + +**step 3:** build +I am using commit `6869c81ed3e7170dc0`, and I have not tested over other commits. +``` +$ git clone https://github.com/Tencent/ncnn.git +$ cd ncnn +$ git reset --hard 6869c81ed3e7170dc0 +$ git submodule update --init +$ mkdir -p build +$ cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_VULKAN=OFF -DNCNN_BUILD_TOOLS=ON -DCMAKE_TOOLCHAIN_FILE=../toolchains/pi3.toolchain.cmake .. +$ make -j2 +$ make install +``` + +### Convert pytorch model to ncnn model + +#### 1. dependencies +``` +$ python -m pip install onnx-simplifier +``` + +#### 2. convert pytorch model to ncnn model via onnx +On your training platform: +``` +$ cd BiSeNet/ +$ python tools/export_onnx.py --aux-mode eval --config configs/bisenetv2_city.py --weight-path /path/to/your/model.pth --outpath ./model_v2.onnx +$ python -m onnxsim model_v2.onnx model_v2_sim.onnx +``` + +Then copy your `model_v2_sim.onnx` from training platform to raspberry device. + +On raspberry device: +``` +$ /path/to/ncnn/build/tools/onnx/onnx2ncnn model_v2_sim.onnx model_v2_sim.param model_v2_sim.bin +``` + +You can optimize the ncnn model by fusing the layers and save the weights with fp16 datatype. +On raspberry device: +``` +$ /path/to/ncnn/build/tools/ncnnoptimize model_v2_sim.param model_v2_sim.bin model_v2_sim_opt.param model_v2_sim_opt.bin 65536 +$ mv model_v2_sim_opt.param model_v2_sim.param +$ mv model_v2_sim_opt.bin model_v2_sim.bin +``` + +You can also quantize the model for int8 inference, following this [tutorial](https://github.com/Tencent/ncnn/wiki/quantized-int8-inference). Make sure your device support int8 inference. + + +### build and run the demo +#### 1. compile demo code +On raspberry device: +``` +$ mkdir -p BiSeNet/ncnn/build +$ cd BiSeNet/ncnn/build +$ cmake .. -DNCNN_ROOT=/path/to/ncnn/build/install +$ make +``` + +#### 2. run demo +``` +./segment +``` diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/ncnn/segment.cpp b/cv/semantic_segmentation/bisenetv2/pytorch/ncnn/segment.cpp new file mode 100755 index 000000000..e570b0995 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/ncnn/segment.cpp @@ -0,0 +1,141 @@ + +#include "net.h" +#include "mat.h" + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + + +using std::string; +using std::vector; +using cv::Mat; + + +vector> get_color_map(); +void inference(); + + +int main(int argc, char** argv) { + inference(); + return 0; +} + + +void inference() { + int nthreads = 4; + string mod_param = "../models/model_v2_sim.param"; + string mod_model = "../models/model_v2_sim.bin"; + int oH{512}, oW{1024}, n_classes{19}; + float mean[3] = {0.3257f, 0.3690f, 0.3223f}; + float var[3] = {0.2112f, 0.2148f, 0.2115f}; + string impth = "../../example.png"; + string savepth = "out.png"; + + // load model + ncnn::Net mod; +#if NCNN_VULKAN + int gpu_count = ncnn::get_gpu_count(); + if (gpu_count <= 0) { + fprintf(stderr, "we do not have gpu device\n"); + return; + } + mod.opt.use_vulkan_compute = 1; + mod.set_vulkan_device(1); +#endif + //// switch off fp16 + // bool use_fp16 = false; + // mod.opt.use_fp16_packed = use_fp16; + // mod.opt.use_fp16_storage = use_fp16; + // mod.opt.use_fp16_arithmetic = use_fp16; + //// switch on bf16 + // mod.opt.use_packing_layout = true; + // mod.opt.use_ff16_storage = true; + //// reduce cpu usage + // net.opt.openmp_blocktime = 0; + mod.opt.use_winograd_convolution = true; + + // we should set opt before load model + mod.load_param(mod_param.c_str()); + mod.load_model(mod_model.c_str()); + + // load image, and copy to ncnn mat + cv::Mat im = cv::imread(impth); + if (im.empty()) { + fprintf(stderr, "cv::imread failed\n"); + return; + } + + ncnn::Mat inp = ncnn::Mat::from_pixels_resize( + im.data, ncnn::Mat::PIXEL_BGR, im.cols, im.rows, oW, oH); + for (float &el : mean) el *= 255.; + for (float &el : var) el = 1. / (255. * el); + inp.substract_mean_normalize(mean, var); + + // set input, run, get output + ncnn::Extractor ex = mod.create_extractor(); + ex.set_light_mode(true); + ex.set_num_threads(nthreads); +#if NCNN_VULKAN + ex.set_vulkan_compute(true); +#endif + + ex.input("input_image", inp); + ncnn::Mat out; + ex.extract("preds", out); // output is nchw, as onnx, where here n=1 + + // generate colorful output, and dump + vector> color_map = get_color_map(); + Mat pred(cv::Size(oW, oH), CV_8UC3); + int offset = oH * oW; + omp_set_num_threads(omp_get_max_threads()); + #pragma omp parallel for + for (int i=0; i < oH; ++i) { + uint8_t *ptr = pred.ptr(i); + for (int j{0}; j < oW; ++j) { + // compute argmax + int idx, argmax{0}; + float max; + idx = i * oW + j; + max = out[idx]; + for (int k{1}; k < n_classes; ++k) { + idx += offset; + if (max < out[idx]) { + max = out[idx]; + argmax = k; + } + } + // color the result + ptr[0] = color_map[argmax][0]; + ptr[1] = color_map[argmax][1]; + ptr[2] = color_map[argmax][2]; + ptr += 3; + } + } + cv::imwrite(savepth, pred); + + ex.clear(); // must have this, or error + mod.clear(); + +} + + +vector> get_color_map() { + vector> color_map(256, vector(3)); + std::minstd_rand rand_eng(123); + std::uniform_int_distribution u(0, 255); + for (int i{0}; i < 256; ++i) { + for (int j{0}; j < 3; ++j) { + color_map[i][j] = u(rand_eng); + } + } + return color_map; +} diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/openvino/CMakeLists.txt b/cv/semantic_segmentation/bisenetv2/pytorch/openvino/CMakeLists.txt new file mode 100755 index 000000000..a5db3df35 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/openvino/CMakeLists.txt @@ -0,0 +1,35 @@ + +cmake_minimum_required (VERSION 3.10) + +cmake_policy(SET CMP0025 NEW) + +project(Samples) + + +set (CMAKE_CXX_STANDARD 14) +set(CMAKE_BUILD_TYPE "Release") +set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wuninitialized -Winit-self") +set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall") + + +find_package(OpenCV REQUIRED) +find_package(InferenceEngine REQUIRED) +find_package(ngraph REQUIRED) + + +include_directories( + ${CMAKE_CURRENT_SOURCE_DIR} + ${CMAKE_CURRENT_BINARY_DIR} + ${OpenCV_INCLUDE_DIRS} +) + +add_executable(segment main.cpp) +target_link_libraries( + segment + ${InferenceEngine_LIBRARIES} + ${NGRAPH_LIBRARIES} + ${OpenCV_LIBS} +) + + + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/openvino/README.md b/cv/semantic_segmentation/bisenetv2/pytorch/openvino/README.md new file mode 100755 index 000000000..0f1eda8e5 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/openvino/README.md @@ -0,0 +1,99 @@ + + +## A demo of using openvino to deploy + +Openvino is used to deploy model on intel cpus or "gpu inside cpu". + +My platform: +* Ubuntu 18.04 +* Intel(R) Xeon(R) Gold 6240 CPU @ 2.60GHz +* openvino_2021.4.689 + + +### preparation + +1.Train the model and export it to onnx +``` +$ cd BiSeNet/ +$ python tools/export_onnx.py --config configs/bisenetv2_city.py --weight-path /path/to/your/model.pth --outpath ./model_v2.onnx +``` +(Optional) 2.Install 'onnx-simplifier' to simplify the generated onnx model: +``` +$ python -m pip install onnx-simplifier +$ python -m onnxsim model_v2.onnx model_v2_sim.onnx +``` + + +### Install and configure openvino + +1.pull docker image +``` +$ docker pull openvino/ubuntu18_dev +``` + +2.start a docker container and mount code into it +``` +$ docker run -itu root -v /path/to/BiSeNet:/BiSeNet openvino/ubuntu18_dev --device /dev/dri:/dev/dri bash + +``` +If your cpu does not have intel "gpu inside of cpu" or you do not want to use it, you can remove the option of `--device /dev/dri:/dev/dri`. + +After running the above command, you will be in the container. + +(optional) 3.install gpu dependencies +If you want to use gpu, you also need to install some dependencies inside the container: +``` +# mkdir -p /tmp/opencl && cd /tmp/opencl +# useradd -ms /bin/bash -G video,users openvino +# chown openvino -R /home/openvino +# apt update +# apt install -y --no-install-recommends ocl-icd-libopencl1 +# curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-gmmlib_19.3.2_amd64.deb" --output "intel-gmmlib_19.3.2_amd64.deb" +# curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-igc-core_1.0.2597_amd64.deb" --output "intel-igc-core_1.0.2597_amd64.deb" +# curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-igc-opencl_1.0.2597_amd64.deb" --output "intel-igc-opencl_1.0.2597_amd64.deb" +# curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-opencl_19.41.14441_amd64.deb" --output "intel-opencl_19.41.14441_amd64.deb" +# curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-ocloc_19.41.14441_amd64.deb" --output "intel-ocloc_19.04.12237_amd64.deb" +# dpkg -i /tmp/opencl/*.deb +# apt --fix-broken install +# ldconfig +``` + +I got the above commands from the official docs but I did not test it since my cpu does not have integrated gpu. + +You can check if your platform has intel gpu with this command: +``` +$ sudo lspci | grep -i vga +``` + +4.configure environment +just run this script, and the environment would be ready: +``` +# source /opt/intel/openvino_2021.4.689/bin/setupvars.sh +``` + + +### convert model and run demo + +1.convert onnx to openvino IR +In the docker container: +``` +# cd /opt/intel/openvino_2021.4.689/deployment_tools/model_optimizer +# python3 mo.py --input_model /BiSeNet/model_v2.onnx --output_dir /BiSeNet/openvino/output_v2 +``` + +2.compile and run the demo +``` +# cd /BiSeNet/openvino +# mkdir -p build && cd build +# cmake .. && make +# ./segment +``` +After this, you will see a segmentation result image named `res.jpg` generated. + + + +### Tipes + +1. GPU support: openvino supports intel cpu and intel "gpu inside cpu". Until now(2021.11), other popular isolated gpus are not supported, such as nvidia/amd gpus. Also, other integrated gpus are not supported, such as aspeed graphics family. + +2. About low-precision: precision is optimized automatically, and the model will be run in one or several precision mode. We can also manually enforce to use bf16, as long as our cpu have `avx512_bf16` supports. If cpu does not support bf16, it will use simulation which would slow down the inference. If neither native bf16 nor simulation is supported, an error would occur. diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/openvino/main.cpp b/cv/semantic_segmentation/bisenetv2/pytorch/openvino/main.cpp new file mode 100755 index 000000000..96342de50 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/openvino/main.cpp @@ -0,0 +1,226 @@ + +#include +#include +#include +#include +#include + +#include +#include + + +std::string mdpth("../output_v2/model_v2_city.xml"); +std::string device("CPU"); // GNA does not support argmax, my cpu does not has integrated gpu +std::string impth("../../example.png"); +std::string savepth("./res.jpg"); + + + +void get_image(std::string, std::vector, float*); +std::vector> get_color_map(); +void save_predict(std::string, int*, + std::vector, std::vector); +void print_infos(); +void inference(); +void test_speed(); + + +int main() { + // print_infos(); + inference(); + // test_speed(); + return 0; +} + + +void inference() { + + // model setup + std::cout << "load network: " << mdpth << std::endl; + InferenceEngine::Core ie; + InferenceEngine::CNNNetwork model = ie.ReadNetwork(mdpth); + model.setBatchSize(1U); + + InferenceEngine::InputsDataMap inputs(model.getInputsInfo()); + InferenceEngine::InputInfo::Ptr input_info = inputs.begin()->second; + input_info->setPrecision(InferenceEngine::Precision::FP32); + input_info->setLayout(InferenceEngine::Layout::NCHW); + + InferenceEngine::DataPtr output_info = model.getOutputsInfo().begin()->second; + output_info->setPrecision(InferenceEngine::Precision::I32); + + + InferenceEngine::ExecutableNetwork network = ie.LoadNetwork(model, device); + InferenceEngine::InferRequest infer_request = network.CreateInferRequest(); + + + // set input data + std::cout << "set input data from: " << impth << std::endl; + std::string in_name = inputs.begin()->first; + auto insize = input_info->getTensorDesc().getDims(); + + InferenceEngine::Blob::Ptr inblob = infer_request.GetBlob(in_name); + InferenceEngine::MemoryBlob::Ptr minput = InferenceEngine::as(inblob); + if (!minput) { + std::cerr << "We expect MemoryBlob from inferRequest, but by fact we " + "were not able to cast inputBlob to MemoryBlob" + << std::endl; + std::abort(); + } + auto minputHolder = minput->wmap(); + float* p_inp = minputHolder.as(); + + get_image(impth, insize, p_inp); + + + // inference synchronized + std::cout << "do inference " << std::endl; + infer_request.Infer(); + + + // fetch output data + std::cout << "save result to: " << savepth << std::endl; + std::string out_name = model.getOutputsInfo().begin()->first; + auto outsize = output_info->getTensorDesc().getDims(); + InferenceEngine::Blob::Ptr outblob = infer_request.GetBlob(out_name); + InferenceEngine::MemoryBlob::Ptr moutput = InferenceEngine::as(outblob); + auto moutputHolder = moutput->rmap(); + int* p_outp = moutputHolder.as(); + + save_predict(savepth, p_outp, insize, outsize); + +} + + +void get_image(std::string impth, std::vector insize, float* data) { + int iH = insize[2]; + int iW = insize[3]; + cv::Mat im = cv::imread(impth); + if (im.empty()) { + std::cerr << "cv::imread failed: " << impth << std::endl; + std::abort(); + } + int orgH{im.rows}, orgW{im.cols}; + if ((orgH != iH) || orgW != iW) { + std::cout << "resize orignal image of (" << orgH << "," << orgW + << ") to (" << iH << ", " << iW << ") according to model requirement\n"; + cv::resize(im, im, cv::Size(iW, iH), cv::INTER_CUBIC); + } + + float mean[3] = {0.3257f, 0.3690f, 0.3223f}; + float var[3] = {0.2112f, 0.2148f, 0.2115f}; + float scale = 1.f / 255.f; + for (float &el : var) el = 1.f / el; + for (int h{0}; h < iH; ++h) { + cv::Vec3b *p = im.ptr(h); + for (int w{0}; w < iW; ++w) { + for (int c{0}; c < 3; ++c) { + int idx = (2 - c) * iH * iW + h * iW + w; // to rgb order + data[idx] = (p[w][c] * scale - mean[c]) * var[c]; + } + } + } +} + + +std::vector> get_color_map() { + std::vector> color_map(256, + std::vector(3)); + std::minstd_rand rand_eng(123); + std::uniform_int_distribution u(0, 255); + for (int i{0}; i < 256; ++i) { + for (int j{0}; j < 3; ++j) { + color_map[i][j] = u(rand_eng); + } + } + return color_map; +} + + +void save_predict(std::string savename, int* data, + std::vector insize, + std::vector outsize) { + + std::vector> color_map = get_color_map(); + int oH = outsize[1]; + int oW = outsize[2]; + cv::Mat pred(cv::Size(oW, oH), CV_8UC3); + int idx{0}; + for (int i{0}; i < oH; ++i) { + uint8_t *ptr = pred.ptr(i); + for (int j{0}; j < oW; ++j) { + ptr[0] = color_map[data[idx]][0]; + ptr[1] = color_map[data[idx]][1]; + ptr[2] = color_map[data[idx]][2]; + ptr += 3; + ++idx; + } + } + cv::imwrite(savename, pred); +} + + +void print_infos() { + + InferenceEngine::Core ie; + // ie.SetConfig({{CONFIG_KEY(ENFORCE_BF16), CONFIG_VALUE(YES)}}, "CPU"); + InferenceEngine::CNNNetwork model = ie.ReadNetwork(mdpth); + InferenceEngine::ExecutableNetwork network = ie.LoadNetwork(model, device); + + auto inp = model.getInputsInfo().begin(); + auto insize = inp->second->getTensorDesc().getDims(); + auto outp = model.getOutputsInfo().begin(); + auto outsize = outp->second->getTensorDesc().getDims(); + + std::cout << "----- supported optimizations ----- \n"; + auto cpuOptimizationCapabilities = ie.GetMetric("CPU", METRIC_KEY(OPTIMIZATION_CAPABILITIES)).as>(); + for (auto &el:cpuOptimizationCapabilities) { + std::cout << " " << el << std::endl; + } + + // std::string enforceBF16 = network.GetConfig(InferenceEngine::PluginConfigParams::KEY_ENFORCE_BF16).as(); + // std::cout << "support BF16" << enforceBF16 << std::endl; + + std::cout << "\n----- model info -----\n"; + std::cout << " model batchsize: " << model.getBatchSize() << std::endl; + std::cout << " input name: " << inp->first << std::endl; + std::cout << " input size: (" + << insize[0] << ", " + << insize[1] << ", " + << insize[2] << ", " + << insize[3] << ") \n"; + std::cout << " output name: " << model.getOutputsInfo().begin()->first << std::endl; + std::cout << " output size: (" + << outsize[0] << ", " + << outsize[1] << ", " + << outsize[2] << ") \n"; + std::cout << "----------------------\n\n"; +} + + + +void test_speed() { + + std::cout << "load network: " << mdpth << std::endl; + InferenceEngine::Core ie; + /* if we enforce using bf16 and platform does not support avx512_bf16, then simulation would be used which would drag down speed. If simulation is not supported, there would be exception. */ + // ie.SetConfig({{CONFIG_KEY(ENFORCE_BF16), CONFIG_VALUE(YES)}}, "CPU"); + InferenceEngine::CNNNetwork model = ie.ReadNetwork(mdpth); + model.setBatchSize(1U); + InferenceEngine::ExecutableNetwork network = ie.LoadNetwork(model, device); + InferenceEngine::InferRequest infer_request = network.CreateInferRequest(); + + + std::cout << "test speed ... \n"; + const int n_loops{500}; + auto start = std::chrono::steady_clock::now(); + for (int i{0}; i < n_loops; ++i) { + infer_request.Infer(); + } + auto end = std::chrono::steady_clock::now(); + double duration = std::chrono::duration(end - start).count(); + duration /= 1000.; + std::cout << "running " << n_loops << " times, use time: " + << duration << "s" << std::endl; + std::cout << "fps is: " << static_cast(n_loops) / duration << std::endl; +} diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/requirements.txt b/cv/semantic_segmentation/bisenetv2/pytorch/requirements.txt new file mode 100755 index 000000000..819a3d3f8 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/requirements.txt @@ -0,0 +1,2 @@ +tabulate +urllib=1.25.10 diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/CMakeLists.txt b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/CMakeLists.txt new file mode 100755 index 000000000..8ffbd39eb --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/CMakeLists.txt @@ -0,0 +1,27 @@ +CMAKE_MINIMUM_REQUIRED(VERSION 3.17) + +PROJECT(segment) + +set(CMAKE_CXX_FLAGS "-std=c++14 -O2") +set(CMAKE_NVCC_FLAGS "-std=c++14 -O2") + + +link_directories(/usr/local/cuda/lib64) +link_directories(${PROJECT_SOURCE_DIR}/build) +# include_directories(/root/build/TensorRT-8.2.5.1/include) +# link_directories(/root/build/TensorRT-8.2.5.1/lib) + + +find_package(CUDA REQUIRED) +find_package(OpenCV REQUIRED) + +cuda_add_library(kernels STATIC kernels.cu) + +add_executable(segment segment.cpp trt_dep.cpp read_img.cpp) +target_include_directories( + segment PUBLIC ${CUDA_INCLUDE_DIRS} ${CUDNN_INCLUDE_DIRS} ${OpenCV_INCLUDE_DIRS}) +target_link_libraries( + segment -lnvinfer -lnvinfer_plugin -lnvparsers -lnvonnxparser -lkernels + ${CUDA_LIBRARIES} + ${OpenCV_LIBRARIES}) + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/README.md b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/README.md new file mode 100755 index 000000000..4dafb11ff --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/README.md @@ -0,0 +1,112 @@ + + +## Deploy with Tensorrt + +Firstly, We should export our trained model to onnx model: +``` +$ cd BiSeNet/ +$ python tools/export_onnx.py --config configs/bisenetv2_city.py --weight-path /path/to/your/model.pth --outpath ./model.onnx --aux-mode eval +``` + +**NOTE:** I use cropsize of `1024x2048` here in my example, you should change it according to your specific application. The inference cropsize is fixed from this step on, so you should decide the inference cropsize when you export the model here. + +Then we can use either c++ or python to compile the model and run inference. + + +### Using C++ + +#### 1. My platform + +* ubuntu 18.04 +* nvidia Tesla T4 gpu, driver newer than 450.80 +* cuda 11.3, cudnn 8 +* cmake 3.22.0 +* opencv built from source +* tensorrt 8.2.5.1 + + + +#### 2. Build with source code +Just use the standard cmake build method: +``` +mkdir -p tensorrt/build +cd tensorrt/build +cmake .. +make +``` +This would generate a `./segment` in the `tensorrt/build` directory. + + +#### 3. Convert onnx to tensorrt model +If you can successfully compile the source code, you can parse the onnx model to tensorrt model with one of the following commands. +For fp32, command is: +``` +$ ./segment compile /path/to/onnx.model /path/to/saved_model.trt +``` +If your gpu support acceleration with fp16 inferenece, you can add a `--fp16` option to in this step: +``` +$ ./segment compile /path/to/onnx.model /path/to/saved_model.trt --fp16 +``` +Building an int8 engine is also supported. Firstly, you should make sure your gpu support int8 inference, or you model will not be faster than fp16/fp32. Then you should prepare certain amount of images for int8 calibration. In this example, I use train set of cityscapes for calibration. The command is like this: +``` +$ rm calibrate_int8 # delete this if exists +$ ./segment compile /path/to/onnx.model /path/to/saved_model.trt --int8 /path/to/BiSeNet/datasets/cityscapes /path/to/BiSeNet/datasets/cityscapes/train.txt +``` +With the above commands, we will have an tensorrt engine named `saved_model.trt` generated. + +Note that I use the simplest method to parse the command line args, so please do **Not** change the order of the args in above command. + + +#### 4. Infer with one single image +Run inference like this: +``` +$ ./segment run /path/to/saved_model.trt /path/to/input/image.jpg /path/to/saved_img.jpg +``` + + +#### 5. Test speed +The speed depends on the specific gpu platform you are working on, you can test the fps on your gpu like this: +``` +$ ./segment test /path/to/saved_model.trt +``` + + +#### 6. Tips: +1. ~Since tensorrt 7.0.0 cannot parse well the `bilinear interpolation` op exported from pytorch, I replace them with pytorch `nn.PixelShuffle`, which would bring some performance overhead(more flops and parameters), and make inference a bit slower. Also due to the `nn.PixelShuffle` op, you **must** export the onnx model with input size to be *n* times of 32.~ +If you are using 7.2.3.4 or newer versions, you should not have problem with `interpolate` anymore. + +2. ~There would be some problem for tensorrt 7.0.0 to parse the `nn.AvgPool2d` op from pytorch with onnx opset11. So I use opset10 to export the model.~ +Likewise, you do not need to worry about this anymore with version newer than 7.2.3.4. + +3. The speed(fps) is tested on a single nvidia Tesla T4 gpu with `batchsize=1` and `cropsize=(1024,2048)`. Please note that T4 gpu is almost 2 times slower than 2080ti, you should evaluate the speed considering your own platform and cropsize. Also note that the performance would be affected if your gpu is concurrently working on other tasks. Please make sure no other program is running on your gpu when you test the speed. + +4. On my platform, after compiling with tensorrt, the model size of bisenetv1 is 29Mb(fp16) and 128Mb(fp32), and the size of bisenetv2 is 16Mb(fp16) and 42Mb(fp32). However, the fps of bisenetv1 is 68(fp16) and 23(fp32), while the fps of bisenetv2 is 59(fp16) and 21(fp32). It is obvious that bisenetv2 has fewer parameters than bisenetv1, but the speed is otherwise. I am not sure whether it is because tensorrt has worse optimization strategy in some ops used in bisenetv2(such as depthwise convolution) or because of the limitation of the gpu on different ops. Please tell me if you have better idea on this. + +5. int8 mode is not always greatly faster than fp16 mode. For example, I tested with bisenetv1-cityscapes and tensorrt 8.2.5.1. With v100 gpu and driver 515.65, the fp16/int8 fps is 185.89/186.85, while with t4 gpu and driver 450.80, it is 78.77/142.31. + + +### Using python + +You can also use python script to compile and run inference of your model. + + +#### 1. Compile model to onnx + +With this command: +``` +$ cd BiSeNet/tensorrt +$ python segment.py compile --onnx /path/to/model.onnx --savepth ./model.trt --quant fp16/fp32 +``` + +This will compile onnx model into tensorrt serialized engine, save save to `./model.trt`. + + +#### 2. Inference with Tensorrt + +Run Inference like this: +``` +$ python segment.py run --mdpth ./model.trt --impth ../example.png --outpth ./res.png +``` + +This will use the tensorrt model compiled above, and run inference with the example image. + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/batch_stream.hpp b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/batch_stream.hpp new file mode 100755 index 000000000..09d026231 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/batch_stream.hpp @@ -0,0 +1,148 @@ + +#ifndef BATCH_STREAM_HPP +#define BATCH_STREAM_HPP + + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "NvInfer.h" +#include "read_img.hpp" + +using nvinfer1::Dims; +using nvinfer1::Dims3; +using nvinfer1::Dims4; + + +class IBatchStream +{ +public: + virtual void reset(int firstBatch) = 0; + virtual bool next() = 0; + virtual void skip(int skipCount) = 0; + virtual float* getBatch() = 0; + virtual int getBatchesRead() const = 0; + virtual int getBatchSize() const = 0; + virtual nvinfer1::Dims4 getDims() const = 0; +}; + + +class BatchStream : public IBatchStream +{ +public: + BatchStream(int batchSize, int maxBatches, Dims indim, + const std::string& dataRoot, + const std::string& dataFile) + : mBatchSize{batchSize} + , mMaxBatches{maxBatches} + { + mDims = Dims3(indim.d[1], indim.d[2], indim.d[3]); + + readDataFile(dataFile, dataRoot); + mSampleSize = std::accumulate( + mDims.d, mDims.d + mDims.nbDims, 1, std::multiplies()) * sizeof(float); + mData.resize(mSampleSize * mBatchSize); + } + + void reset(int firstBatch) override + { + cout << "mBatchCount: " << mBatchCount << endl; + mBatchCount = firstBatch; + } + + bool next() override + { + if (mBatchCount >= mMaxBatches) + { + return false; + } + ++mBatchCount; + return true; + } + + void skip(int skipCount) override + { + mBatchCount += skipCount; + } + + float* getBatch() override + { + int offset = mBatchCount * mBatchSize; + for (int i{0}; i < mBatchSize; ++i) { + int ind = offset + i; + read_data(mPaths[ind], &mData[i * mSampleSize], mDims.d[1], mDims.d[2]); + } + return mData.data(); + } + + int getBatchesRead() const override + { + return mBatchCount; + } + + int getBatchSize() const override + { + return mBatchSize; + } + + nvinfer1::Dims4 getDims() const override + { + return Dims4{mBatchSize, mDims.d[0], mDims.d[1], mDims.d[2]}; + } + +private: + void readDataFile(const std::string& dataFilePath, const std::string& dataRootPath) + { + std::ifstream file(dataFilePath, std::ios::in); + if (!file.is_open()) { + cout << "file open failed: " << dataFilePath << endl; + std::abort(); + } + std::stringstream ss; + file >> ss.rdbuf(); + file.close(); + + std::string impth; + int n_imgs = 0; + while (std::getline(ss, impth)) ++n_imgs; + ss.clear(); ss.seekg(0, std::ios::beg); + if (n_imgs <= 0) { + cout << "ann file is empty, cannot read image paths for int8 calibration: " + << dataFilePath << endl; + std::abort(); + } + + mPaths.resize(n_imgs); + for (int i{0}; i < n_imgs; ++i) { + std::getline(ss, impth, ','); + mPaths[i] = dataRootPath + "/" + impth; + std::getline(ss, impth); + } + if (mMaxBatches < 0) { + mMaxBatches = n_imgs / mBatchSize - 1; + } + if (mMaxBatches <= 0) { + cout << "must have at least 1 batch for calibration\n"; + std::abort(); + } + cout << "mMaxBatches = " << mMaxBatches << endl; + } + + + int mBatchSize{0}; + int mBatchCount{0}; + int mMaxBatches{0}; + Dims3 mDims{}; + std::vector mPaths; + std::vector mData; + int mSampleSize{0}; +}; + + +#endif diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/entropy_calibrator.hpp b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/entropy_calibrator.hpp new file mode 100755 index 000000000..bc72ba47d --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/entropy_calibrator.hpp @@ -0,0 +1,160 @@ +/* + * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef ENTROPY_CALIBRATOR_HPP +#define ENTROPY_CALIBRATOR_HPP + +#include +#include +#include +#include "NvInfer.h" + +//! \class EntropyCalibratorImpl +//! +//! \brief Implements common functionality for Entropy calibrators. +//! +template +class EntropyCalibratorImpl +{ +public: + EntropyCalibratorImpl( + TBatchStream stream, int firstBatch, std::string cal_table_name, const char* inputBlobName, bool readCache = true) + : mStream{stream} + , mCalibrationTableName(cal_table_name) + , mInputBlobName(inputBlobName) + , mReadCache(readCache) + { + nvinfer1::Dims4 dims = mStream.getDims(); + mInputCount = std::accumulate( + dims.d, dims.d + dims.nbDims, 1, std::multiplies()); + cout << "dims.nbDims: " << dims.nbDims << endl; + for (int i{0}; i < dims.nbDims; ++i) { + cout << dims.d[i] << ", "; + } + cout << endl; + + cudaError_t state; + state = cudaMalloc(&mDeviceInput, mInputCount * sizeof(float)); + if (state) { + cout << "allocate memory failed\n"; + std::abort(); + } + cout << "mInputCount: " << mInputCount << endl; + mStream.reset(firstBatch); + } + + virtual ~EntropyCalibratorImpl() + { + cudaError_t state; + state = cudaFree(mDeviceInput); + if (state) { + cout << "free memory failed\n"; + std::abort(); + } + } + + int getBatchSize() const + { + return mStream.getBatchSize(); + } + + bool getBatch(void* bindings[], const char* names[], int nbBindings) + { + if (!mStream.next()) + { + return false; + } + cudaError_t state; + state = cudaMemcpy(mDeviceInput, mStream.getBatch(), mInputCount * sizeof(float), cudaMemcpyHostToDevice); + if (state) { + cout << "memory copy to device failed\n"; + std::abort(); + } + assert(!strcmp(names[0], mInputBlobName)); + bindings[0] = mDeviceInput; + return true; + } + + const void* readCalibrationCache(size_t& length) + { + mCalibrationCache.clear(); + std::ifstream input(mCalibrationTableName, std::ios::binary); + input >> std::noskipws; + if (mReadCache && input.good()) + { + std::copy(std::istream_iterator(input), std::istream_iterator(), + std::back_inserter(mCalibrationCache)); + } + length = mCalibrationCache.size(); + return length ? mCalibrationCache.data() : nullptr; + } + + void writeCalibrationCache(const void* cache, size_t length) + { + std::ofstream output(mCalibrationTableName, std::ios::binary); + output.write(reinterpret_cast(cache), length); + } + +private: + TBatchStream mStream; + size_t mInputCount; + std::string mCalibrationTableName; + const char* mInputBlobName; + bool mReadCache{true}; + void* mDeviceInput{nullptr}; + std::vector mCalibrationCache; +}; + +//! \class Int8EntropyCalibrator2 +//! +//! \brief Implements Entropy calibrator 2. +//! CalibrationAlgoType is kENTROPY_CALIBRATION_2. +//! +template +class Int8EntropyCalibrator2 : public nvinfer1::IInt8EntropyCalibrator2 +{ +public: + Int8EntropyCalibrator2( + TBatchStream stream, int firstBatch, const char* networkName, const char* inputBlobName, bool readCache = true) + : mImpl(stream, firstBatch, networkName, inputBlobName, readCache) + { + } + + int getBatchSize() const noexcept override + { + return mImpl.getBatchSize(); + } + + bool getBatch(void* bindings[], const char* names[], int nbBindings) noexcept override + { + return mImpl.getBatch(bindings, names, nbBindings); + } + + const void* readCalibrationCache(size_t& length) noexcept override + { + return mImpl.readCalibrationCache(length); + } + + void writeCalibrationCache(const void* cache, size_t length) noexcept override + { + mImpl.writeCalibrationCache(cache, length); + } + +private: + EntropyCalibratorImpl mImpl; +}; + +#endif // ENTROPY_CALIBRATOR_H diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/kernels.cu b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/kernels.cu new file mode 100755 index 000000000..b9bf4de12 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/kernels.cu @@ -0,0 +1,158 @@ + +#include +#include +#include +#include +#include +#include +#include +#include +#include "NvInfer.h" + + + +#define BLOCKSIZE 512 + +#define ivpair thrust::pair + + +template +__forceinline__ __device__ void reduce_max(ivpair* sdata, int blocksize, int tid) { + __syncthreads(); + for (int s{blocksize / 2}; s > 0; s >>= 1) { + if (tid < s) { + if (sdata[tid].first < sdata[tid + s].first) { + sdata[tid] = sdata[tid + s]; + } + } + __syncthreads(); + } +} + + +template +__global__ void arg_max_depth(const int n_size, + const int dimsize, const int m_size, + const scalar_t *inten, + int *oten) { + extern __shared__ __align__(sizeof(ivpair)) unsigned char sdata_raw[]; + ivpair *sdata = reinterpret_cast(sdata_raw); + sdata = sdata + blockDim.x * threadIdx.y; + + int sample_offset = gridDim.x * blockDim.y; + int bid = threadIdx.y + blockIdx.x * blockDim.y; + int samplesize = n_size * m_size; + + for (int i{bid}; i < samplesize; i += sample_offset) { + int n_idx = i / m_size; + int m_idx = i % m_size; + + /// NOTE: This is not memory-safe when dimsize < blockDim.x + int idx = n_idx * dimsize * m_size + threadIdx.x * m_size + m_idx; + ivpair maxp = thrust::make_pair(inten[idx], threadIdx.x); + int j = threadIdx.x + blockDim.x; + for (; j < dimsize; j += blockDim.x) { + idx += blockDim.x * m_size; + scalar_t val = inten[idx]; + if (val > maxp.first) { + maxp = thrust::make_pair(val, j); + } + } + sdata[threadIdx.x] = maxp; + __syncthreads(); + reduce_max(sdata, blockDim.x, threadIdx.x); + + idx = n_idx * m_size + m_idx; + oten[idx] = sdata[0].second; + } +} + + +template +__global__ void arg_max_spatial(const int n_size, + const int dimsize, const int m_size, + const scalar_t *inten, + int *oten) { + + int sample_offset = gridDim.x * blockDim.x; + int tid = threadIdx.x + blockIdx.x * blockDim.x; + int samplesize = n_size * m_size; + + for (int i{tid}; i < samplesize; i += sample_offset) { + int n_idx = i / m_size; + int m_idx = i % m_size; + + // obtain max + int idx = n_idx * dimsize * m_size + m_idx; + scalar_t max_val = inten[idx]; + int res = 0; + for (int j{1}; j < dimsize; ++j) { + idx += m_size; + scalar_t val = inten[idx]; + if (val > max_val) { + max_val = val; + res = j; + } + } + idx = n_idx * m_size + m_idx; + oten[idx] = res; + } +} + + +void argMaxFunc(const void *inten, + void *oten, const int n_size, + const int dimsize, const int m_size, + cudaStream_t* stream) { + if (inten == nullptr or oten == nullptr) std::abort(); + + int samplesize = n_size * m_size; + int shm_size = 0; + dim3 grid, block; + + if (dimsize <= 256) { + int blockx, gridx; + cudaOccupancyMaxPotentialBlockSize(&gridx, &blockx, + arg_max_spatial, 0, samplesize); + gridx = std::min(4096, gridx << 2); + block.x = blockx; grid.x = gridx; + + if (stream == nullptr) { + arg_max_spatial<<>>( + n_size, dimsize, m_size, + reinterpret_cast(inten), + reinterpret_cast(oten)); + } else { + arg_max_spatial<<>>( + n_size, dimsize, m_size, + reinterpret_cast(inten), + reinterpret_cast(oten)); + } + + } else { + int blockx, blocky, gridx; + shm_size = (sizeof(float) + sizeof(int)) * BLOCKSIZE; + int block_lmt = std::min(BLOCKSIZE, dimsize); + blockx = 32; + while (blockx <= block_lmt) blockx = (blockx << 1); + blockx = (blockx >> 1); // must make sure dimsize > blockx + blocky = BLOCKSIZE / blockx; + gridx = std::min(4096, samplesize / blocky); + block.x = blockx; block.y = blocky; grid.x = gridx; + + if (stream == nullptr) { + arg_max_depth<<>>( + n_size, dimsize, m_size, + reinterpret_cast(inten), + reinterpret_cast(oten)); + } else { + arg_max_depth<<>>( + n_size, dimsize, m_size, + reinterpret_cast(inten), + reinterpret_cast(oten)); + } + } + + +} + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/kernels.hpp b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/kernels.hpp new file mode 100755 index 000000000..db05f6ea2 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/kernels.hpp @@ -0,0 +1,13 @@ +#ifndef _KERNELS_HPP_ +#define _KERNELS_HPP_ + +#include +#include + + +void argMaxFunc(const void *inten, + void *oten, const int n_size, + const int dimsize, const int m_size, + cudaStream_t* stream); + +#endif diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/read_img.cpp b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/read_img.cpp new file mode 100755 index 000000000..60657dac2 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/read_img.cpp @@ -0,0 +1,56 @@ + +#include +#include +#include +#include +#include +#include + + +using std::cout; +using std::endl; +using std::vector; +using std::string; +using cv::Mat; + + +void read_data(std::string impth, float *data, int iH, int iW, + int& orgH, int& orgW) { + vector mean{0.485f, 0.456f, 0.406f}; // rgb order + vector variance{0.229f, 0.224f, 0.225f}; + + Mat im = cv::imread(impth); + if (im.empty()) { + cout << "cannot read image \n"; + std::abort(); + } + + orgH = im.rows; orgW = im.cols; + if ((orgH != iH) || orgW != iW) { + cout << "resize orignal image of (" << orgH << "," << orgW + << ") to (" << iH << ", " << iW << ") according to model require\n"; + cv::resize(im, im, cv::Size(iW, iH), cv::INTER_CUBIC); + } + + // normalize and convert to rgb + float scale = 1.f / 255.f; + for (int i{0}; i < variance.size(); ++ i) { + variance[i] = 1.f / variance[i]; + } + for (int h{0}; h < iH; ++h) { + cv::Vec3b *p = im.ptr(h); + for (int w{0}; w < iW; ++w) { + for (int c{0}; c < 3; ++c) { + int idx = c * iH * iW + h * iW + w; + data[idx] = (p[w][2 - c] * scale - mean[c]) * variance[c]; + } + } + } +} + + +void read_data(std::string impth, float *data, int iH, int iW) { + int tmp1, tmp2; + read_data(impth, data, iH, iW, tmp1, tmp2); +} + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/read_img.hpp b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/read_img.hpp new file mode 100755 index 000000000..08d930bde --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/read_img.hpp @@ -0,0 +1,26 @@ + +#ifndef _READ_IMAGE_HPP_ +#define _READ_IMAGE_HPP_ + + +#include +#include +#include +#include +#include +#include + + +using std::cout; +using std::endl; +using std::vector; +using std::string; +using cv::Mat; + + +void read_data(std::string impth, float *data, + int iH, int iW, int& orgH, int& orgW); +void read_data(std::string impth, float *data, int iH, int iW); + + +#endif diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/segment.cpp b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/segment.cpp new file mode 100755 index 000000000..a71b4ad60 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/segment.cpp @@ -0,0 +1,171 @@ +#include "NvInfer.h" +#include "NvOnnxParser.h" +#include "NvInferPlugin.h" +#include +#include "NvInferRuntimeCommon.h" + +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "trt_dep.hpp" +#include "read_img.hpp" + + +using nvinfer1::IHostMemory; +using nvinfer1::IBuilder; +using nvinfer1::INetworkDefinition; +using nvinfer1::ICudaEngine; +using nvinfer1::IInt8Calibrator; +using nvinfer1::IBuilderConfig; +using nvinfer1::IRuntime; +using nvinfer1::IExecutionContext; +using nvinfer1::ILogger; +using nvinfer1::Dims3; +using nvinfer1::Dims2; +using Severity = nvinfer1::ILogger::Severity; + +using std::string; +using std::ios; +using std::ofstream; +using std::ifstream; +using std::vector; +using std::cout; +using std::endl; +using std::array; + +using cv::Mat; + + + + +vector> get_color_map(); + +void compile_onnx(vector args); +void run_with_trt(vector args); +void test_speed(vector args); + + +int main(int argc, char* argv[]) { + if (argc < 3) { + cout << "usage is ./segment compile/run/test\n"; + std::abort(); + } + + vector args; + for (int i{1}; i < argc; ++i) args.emplace_back(argv[i]); + + if (args[0] == "compile") { + if (argc < 4) { + cout << "usage is: ./segment compile input.onnx output.trt [--fp16|--fp32]\n"; + cout << "or ./segment compile input.onnx output.trt --int8 /path/to/data_root /path/to/ann_file\n"; + std::abort(); + } + compile_onnx(args); + } else if (args[0] == "run") { + if (argc < 5) { + cout << "usage is ./segment run ./xxx.trt input.jpg result.jpg\n"; + std::abort(); + } + run_with_trt(args); + } else if (args[0] == "test") { + if (argc < 3) { + cout << "usage is ./segment test ./xxx.trt\n"; + std::abort(); + } + test_speed(args); + } + + return 0; +} + + +void compile_onnx(vector args) { + string quant("fp32"); + string data_root("none"); + string data_file("none"); + if ((args.size() >= 4)) { + if (args[3] == "--fp32") { + quant = "fp32"; + } else if (args[3] == "--fp16") { + quant = "fp16"; + } else if (args[3] == "--int8") { + quant = "int8"; + data_root = args[4]; + data_file = args[5]; + } else { + cout << "invalid args of quantization: " << args[3] << endl; + std::abort(); + } + } + + TrtSharedEnginePtr engine = parse_to_engine(args[1], quant, data_root, data_file); + serialize(engine, args[2]); +} + + +void run_with_trt(vector args) { + + TrtSharedEnginePtr engine = deserialize(args[1]); + + Dims3 i_dims = static_cast( + engine->getBindingDimensions(engine->getBindingIndex("input_image"))); + Dims3 o_dims = static_cast( + engine->getBindingDimensions(engine->getBindingIndex("preds"))); + const int iH{i_dims.d[2]}, iW{i_dims.d[3]}; + const int oH{o_dims.d[2]}, oW{o_dims.d[3]}; + + // prepare image and resize + vector data; data.resize(iH * iW * 3); + int orgH, orgW; + read_data(args[2], &data[0], iH, iW, orgH, orgW); + + // call engine + vector res = infer_with_engine(engine, data); + + // generate colored out + vector> color_map = get_color_map(); + Mat pred(cv::Size(oW, oH), CV_8UC3); + int idx{0}; + for (int i{0}; i < oH; ++i) { + uint8_t *ptr = pred.ptr(i); + for (int j{0}; j < oW; ++j) { + ptr[0] = color_map[res[idx]][0]; + ptr[1] = color_map[res[idx]][1]; + ptr[2] = color_map[res[idx]][2]; + ptr += 3; + ++idx; + } + } + + // resize back and save + if ((orgH != oH) || (orgW != oW)) { + cv::resize(pred, pred, cv::Size(orgW, orgH), cv::INTER_CUBIC); + } + cv::imwrite(args[3], pred); +} + + +vector> get_color_map() { + vector> color_map(256, vector(3)); + std::minstd_rand rand_eng(123); + std::uniform_int_distribution u(0, 255); + for (int i{0}; i < 256; ++i) { + for (int j{0}; j < 3; ++j) { + color_map[i][j] = u(rand_eng); + } + } + return color_map; +} + + +void test_speed(vector args) { + TrtSharedEnginePtr engine = deserialize(args[1]); + test_fps_with_engine(engine); +} diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/segment.py b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/segment.py new file mode 100755 index 000000000..32e34d42a --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/segment.py @@ -0,0 +1,157 @@ + +import os +import os.path as osp +import cv2 +import numpy as np +import logging +import argparse + +import tensorrt as trt +import pycuda.driver as cuda +import pycuda.autoinit + + +parser = argparse.ArgumentParser() +subparsers = parser.add_subparsers(dest="command") +compile_parser = subparsers.add_parser('compile') +compile_parser.add_argument('--onnx') +compile_parser.add_argument('--quant', default='fp32') +compile_parser.add_argument('--savepth', default='./model.trt') +run_parser = subparsers.add_parser('run') +run_parser.add_argument('--mdpth') +run_parser.add_argument('--impth') +run_parser.add_argument('--outpth', default='./res.png') +args = parser.parse_args() + + +np.random.seed(123) +in_datatype = trt.nptype(trt.float32) +out_datatype = trt.nptype(trt.int32) +palette = np.random.randint(0, 256, (256, 3)).astype(np.uint8) + +ctx = pycuda.autoinit.context +trt.init_libnvinfer_plugins(None, "") +TRT_LOGGER = trt.Logger() + + + +def get_image(impth, size): + mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)[:, None, None] + var = np.array([0.229, 0.224, 0.225], dtype=np.float32)[:, None, None] + iH, iW = size[0], size[1] + img = cv2.imread(impth)[:, :, ::-1] + orgH, orgW, _ = img.shape + img = cv2.resize(img, (iW, iH)).astype(np.float32) + img = img.transpose(2, 0, 1) / 255. + img = (img - mean) / var + return img, (orgH, orgW) + + + +def allocate_buffers(engine): + h_input = cuda.pagelocked_empty( + trt.volume(engine.get_binding_shape(0)), dtype=in_datatype) + print(engine.get_binding_shape(0)) + d_input = cuda.mem_alloc(h_input.nbytes) + h_outputs, d_outputs = [], [] + n_outs = 1 + for i in range(n_outs): + h_output = cuda.pagelocked_empty( + trt.volume(engine.get_binding_shape(i+1)), + dtype=out_datatype) + d_output = cuda.mem_alloc(h_output.nbytes) + h_outputs.append(h_output) + d_outputs.append(d_output) + stream = cuda.Stream() + return ( + stream, + h_input, + d_input, + h_outputs, + d_outputs, + ) + + +def build_engine_from_onnx(onnx_file_path): + engine = None ## add this to avoid return deleted engine + EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, builder.create_builder_config() as config, trt.OnnxParser(network, TRT_LOGGER) as parser, trt.Runtime(TRT_LOGGER) as runtime: + + # Parse model file + print(f'Loading ONNX file from path {onnx_file_path}...') + assert os.path.exists(onnx_file_path), f'cannot find {onnx_file_path}' + with open(onnx_file_path, 'rb') as fr: + if not parser.parse(fr.read()): + print ('ERROR: Failed to parse the ONNX file.') + for error in range(parser.num_errors): + print (parser.get_error(error)) + assert False + + # build settings + builder.max_batch_size = 128 + config.max_workspace_size = 1 << 30 # 1G + if args.quant == 'fp16': + config.set_flag(trt.BuilderFlag.FP16) + + print("Start to build Engine") + plan = builder.build_serialized_network(network, config) + engine = runtime.deserialize_cuda_engine(plan) + return engine + + +def serialize_engine_to_file(engine, savepth): + plan = engine.serialize() + with open(savepth, "wb") as fw: + fw.write(plan) + + +def deserialize_engine_from_file(savepth): + with open(savepth, 'rb') as fr, trt.Runtime(TRT_LOGGER) as runtime: + engine = runtime.deserialize_cuda_engine(fr.read()) + return engine + + +def main(): + if args.command == 'compile': + engine = build_engine_from_onnx(args.onnx) + serialize_engine_to_file(engine, args.savepth) + + elif args.command == 'run': + engine = deserialize_engine_from_file(args.mdpth) + + ishape = engine.get_binding_shape(0) + img, (orgH, orgW) = get_image(args.impth, ishape[2:]) + + ## create engine and allocate bffers + ( + stream, + h_input, + d_input, + h_outputs, + d_outputs, + ) = allocate_buffers(engine) + ctx.push() + context = engine.create_execution_context() + ctx.pop() + bds = [int(d_input), ] + [int(el) for el in d_outputs] + + h_input = np.ascontiguousarray(img) + cuda.memcpy_htod_async(d_input, h_input, stream) + context.execute_async( + bindings=bds, stream_handle=stream.handle) + for h_output, d_output in zip(h_outputs, d_outputs): + cuda.memcpy_dtoh_async(h_output, d_output, stream) + stream.synchronize() + + oshape = engine.get_binding_shape(1) + pred = np.argmax(h_outputs[0].reshape(oshape), axis=1) + out = palette[pred] + out = out.reshape(*oshape[2:], 3) + out = cv2.resize(out, (orgW, orgH)) + cv2.imwrite(args.outpth, out) + + + +if __name__ == '__main__': + main() + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/trt_dep.cpp b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/trt_dep.cpp new file mode 100755 index 000000000..71f105c4d --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/trt_dep.cpp @@ -0,0 +1,311 @@ + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "trt_dep.hpp" +#include "batch_stream.hpp" +#include "entropy_calibrator.hpp" +#include "kernels.hpp" + + +using nvinfer1::IHostMemory; +using nvinfer1::IBuilder; +using nvinfer1::INetworkDefinition; +using nvinfer1::ICudaEngine; +using nvinfer1::IInt8Calibrator; +using nvinfer1::IBuilderConfig; +using nvinfer1::IRuntime; +using nvinfer1::IExecutionContext; +using nvinfer1::ILogger; +using nvinfer1::Dims3; +using nvinfer1::Dims2; +using Severity = nvinfer1::ILogger::Severity; + +using std::string; +using std::ios; +using std::ofstream; +using std::ifstream; +using std::vector; +using std::cout; +using std::endl; +using std::array; + + +Logger gLogger; + + +TrtSharedEnginePtr shared_engine_ptr(ICudaEngine* ptr) { + return TrtSharedEnginePtr(ptr, TrtDeleter()); +} + + +TrtSharedEnginePtr parse_to_engine(string onnx_pth, + string quant, string data_root, string data_file) { + unsigned int maxBatchSize{1}; + long memory_limit = 1UL << 32; // 4G + + auto builder = TrtUnqPtr(nvinfer1::createInferBuilder(gLogger)); + if (!builder) { + cout << "create builder failed\n"; + std::abort(); + } + + const auto explicitBatch = 1U << static_cast( + nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH); + auto network = TrtUnqPtr( + builder->createNetworkV2(explicitBatch)); + if (!network) { + cout << "create network failed\n"; + std::abort(); + } + + auto config = TrtUnqPtr(builder->createBuilderConfig()); + if (!config) { + cout << "create builder config failed\n"; + std::abort(); + } + + auto parser = TrtUnqPtr(nvonnxparser::createParser(*network, gLogger)); + if (!parser) { + cout << "create parser failed\n"; + std::abort(); + } + + int verbosity = (int)nvinfer1::ILogger::Severity::kWARNING; + bool state = parser->parseFromFile(onnx_pth.c_str(), verbosity); + if (!state) { + cout << "parse model failed\n"; + std::abort(); + } + + config->setMaxWorkspaceSize(memory_limit); + if ((quant == "fp16" or quant == "int8") && builder->platformHasFastFp16()) { + config->setFlag(nvinfer1::BuilderFlag::kFP16); // fp16 + } + std::unique_ptr calibrator; + if (quant == "int8" && builder->platformHasFastInt8()) { + config->setFlag(nvinfer1::BuilderFlag::kINT8); //int8 + int batchsize = 32; + int n_cal_batches = -1; + string cal_table_name = "calibrate_int8"; + string input_name = "input_image"; + + Dims indim = network->getInput(0)->getDimensions(); + BatchStream calibrationStream( + batchsize, n_cal_batches, indim, + data_root, data_file); + calibrator.reset(new Int8EntropyCalibrator2( + calibrationStream, 0, cal_table_name.c_str(), input_name.c_str())); + config->setInt8Calibrator(calibrator.get()); + } + + auto output = network->getOutput(0); + // output->setType(nvinfer1::DataType::kINT32); + output->setType(nvinfer1::DataType::kFLOAT); + + cout << " start to build \n"; + CudaStreamUnqPtr stream(new cudaStream_t); + if (cudaStreamCreate(stream.get())) { + cout << "create stream failed\n"; + std::abort(); + } + config->setProfileStream(*stream); + + auto plan = TrtUnqPtr(builder->buildSerializedNetwork(*network, *config)); + if (!plan) { + cout << "serialization failed\n"; + std::abort(); + } + + auto runtime = TrtUnqPtr(nvinfer1::createInferRuntime(gLogger)); + if (!plan) { + cout << "create runtime failed\n"; + std::abort(); + } + + TrtSharedEnginePtr engine = shared_engine_ptr( + runtime->deserializeCudaEngine(plan->data(), plan->size())); + if (!engine) { + cout << "create engine failed\n"; + std::abort(); + } + cout << "done build engine \n"; + + return engine; +} + + +void serialize(TrtSharedEnginePtr engine, string save_path) { + + auto trt_stream = TrtUnqPtr(engine->serialize()); + if (!trt_stream) { + cout << "serialize engine failed\n"; + std::abort(); + } + + ofstream ofile(save_path, ios::out | ios::binary); + ofile.write((const char*)trt_stream->data(), trt_stream->size()); + + ofile.close(); +} + + +TrtSharedEnginePtr deserialize(string serpth) { + + ifstream ifile(serpth, ios::in | ios::binary); + if (!ifile) { + cout << "read serialized file failed\n"; + std::abort(); + } + + ifile.seekg(0, ios::end); + const int mdsize = ifile.tellg(); + ifile.clear(); + ifile.seekg(0, ios::beg); + vector buf(mdsize); + ifile.read(&buf[0], mdsize); + ifile.close(); + cout << "model size: " << mdsize << endl; + + auto runtime = TrtUnqPtr(nvinfer1::createInferRuntime(gLogger)); + TrtSharedEnginePtr engine = shared_engine_ptr( + runtime->deserializeCudaEngine((void*)&buf[0], mdsize)); + return engine; +} + + +vector infer_with_engine(TrtSharedEnginePtr engine, vector& data) { + Dims3 out_dims = static_cast( + engine->getBindingDimensions(engine->getBindingIndex("preds"))); + + const int batchsize{1}, H{out_dims.d[2]}, W{out_dims.d[3]}; + const int n_classes{out_dims.d[1]}; + const int in_size{static_cast(data.size())}; + const int logits_size{batchsize * n_classes * H * W}; + const int out_size{batchsize * H * W}; + vector buffs(3); + vector res(out_size); + + auto context = TrtUnqPtr(engine->createExecutionContext()); + if (!context) { + cout << "create execution context failed\n"; + std::abort(); + } + + cudaError_t state; + state = cudaMalloc(&buffs[0], in_size * sizeof(float)); + if (state) { + cout << "allocate memory failed\n"; + std::abort(); + } + state = cudaMalloc(&buffs[1], logits_size * sizeof(float)); + if (state) { + cout << "allocate memory failed\n"; + std::abort(); + } + state = cudaMalloc(&buffs[2], out_size * sizeof(int)); + if (state) { + cout << "allocate memory failed\n"; + std::abort(); + } + CudaStreamUnqPtr stream(new cudaStream_t); + if (cudaStreamCreate(stream.get())) { + cout << "create stream failed\n"; + std::abort(); + } + + state = cudaMemcpyAsync( + buffs[0], &data[0], in_size * sizeof(float), + cudaMemcpyHostToDevice, *stream); + if (state) { + cout << "transmit to device failed\n"; + std::abort(); + } + + context->enqueueV2(&buffs[0], *stream, nullptr); + // context->enqueue(1, &buffs[0], stream, nullptr); + argMaxFunc(buffs[1], buffs[2], batchsize, n_classes, H * W, stream.get()); + + state = cudaMemcpyAsync( + &res[0], buffs[2], out_size * sizeof(int), + cudaMemcpyDeviceToHost, *stream); + if (state) { + cout << "transmit to host failed \n"; + std::abort(); + } + + cudaStreamSynchronize(*stream); + + cudaFree(buffs[0]); + cudaFree(buffs[1]); + cudaFree(buffs[2]); + + return res; +} + + +void test_fps_with_engine(TrtSharedEnginePtr engine) { + Dims3 in_dims = static_cast( + engine->getBindingDimensions(engine->getBindingIndex("input_image"))); + Dims3 out_dims = static_cast( + engine->getBindingDimensions(engine->getBindingIndex("preds"))); + + const int batchsize{1}; + const int oH{out_dims.d[2]}, oW{out_dims.d[3]}; + const int n_classes{out_dims.d[1]}; + const int iH{in_dims.d[2]}, iW{in_dims.d[3]}; + const int in_size{batchsize * 3 * iH * iW}; + const int logits_size{batchsize * n_classes * oH * oW}; + const int out_size{batchsize * oH * oW}; + + auto context = TrtUnqPtr(engine->createExecutionContext()); + if (!context) { + cout << "create execution context failed\n"; + std::abort(); + } + + vector buffs(3); + cudaError_t state; + state = cudaMalloc(&buffs[0], in_size * sizeof(float)); + if (state) { + cout << "allocate memory failed\n"; + std::abort(); + } + state = cudaMalloc(&buffs[1], logits_size * sizeof(float)); + if (state) { + cout << "allocate memory failed\n"; + std::abort(); + } + state = cudaMalloc(&buffs[2], out_size * sizeof(int)); + if (state) { + cout << "allocate memory failed\n"; + std::abort(); + } + + cout << "\ntest with cropsize of (" << iH << ", " << iW << ") ...\n"; + auto start = std::chrono::steady_clock::now(); + const int n_loops{1000}; + for (int i{0}; i < n_loops; ++i) { + // context->execute(1, &buffs[0]); + context->executeV2(&buffs[0]); + argMaxFunc(buffs[1], buffs[2], batchsize, n_classes, oH * oW, nullptr); + } + auto end = std::chrono::steady_clock::now(); + double duration = std::chrono::duration(end - start).count(); + duration /= 1000.; + cout << "running " << n_loops << " times, use time: " + << duration << "s" << endl; + cout << "fps is: " << static_cast(n_loops) / duration << endl; + + + cudaFree(buffs[0]); + cudaFree(buffs[1]); + cudaFree(buffs[2]); +} + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/trt_dep.hpp b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/trt_dep.hpp new file mode 100755 index 000000000..2b794dc63 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tensorrt/trt_dep.hpp @@ -0,0 +1,64 @@ +#ifndef _TRT_DEP_HPP_ +#define _TRT_DEP_HPP_ + +#include "NvInfer.h" +#include "NvOnnxParser.h" +#include "NvInferPlugin.h" +#include + +#include +#include +#include +#include + + +using std::string; +using std::vector; +using std::cout; +using std::endl; + +using nvinfer1::ICudaEngine; +using nvinfer1::ILogger; +using Severity = nvinfer1::ILogger::Severity; + + +class Logger: public ILogger { + public: + void log(Severity severity, const char* msg) noexcept override { + if (severity != Severity::kINFO) { + std::cout << msg << std::endl; + } + } +}; + +struct TrtDeleter { + template + void operator()(T* obj) const { + delete obj; + } +}; + +struct CudaStreamDeleter { + void operator()(cudaStream_t* stream) const { + cudaStreamDestroy(*stream); + } +}; + +template +using TrtUnqPtr = std::unique_ptr; +using CudaStreamUnqPtr = std::unique_ptr; +using TrtSharedEnginePtr = std::shared_ptr; + + +extern Logger gLogger; + + +TrtSharedEnginePtr shared_engine_ptr(ICudaEngine* ptr); +TrtSharedEnginePtr parse_to_engine(string onnx_path, string quant, + string data_root, string data_file); +void serialize(TrtSharedEnginePtr engine, string save_path); +TrtSharedEnginePtr deserialize(string serpth); +vector infer_with_engine(TrtSharedEnginePtr engine, vector& data); +void test_fps_with_engine(TrtSharedEnginePtr engine); + +#endif diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/README.md b/cv/semantic_segmentation/bisenetv2/pytorch/tis/README.md new file mode 100755 index 000000000..4d7c6fe9b --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/README.md @@ -0,0 +1,123 @@ + + +## A simple demo of using trition-inference-serving + +### Platform + +* ubuntu 18.04 +* cmake-3.22.0 +* 8 Tesla T4 gpu + + +### Serving Model + +#### 1. prepare model repository + +We need to export our model to onnx and copy it to model repository: +``` +$ cd BiSeNet +$ python tools/export_onnx.py --config configs/bisenetv1_city.py --weight-path /path/to/your/model.pth --outpath ./model.onnx +$ cp -riv ./model.onnx tis/models/bisenetv1/1 + +$ python tools/export_onnx.py --config configs/bisenetv2_city.py --weight-path /path/to/your/model.pth --outpath ./model.onnx +$ cp -riv ./model.onnx tis/models/bisenetv2/1 +``` + +#### 2. prepare the preprocessing backend +We can use either python backend or cpp backend for preprocessing in the server side. +Firstly, we pull the docker image, and start a serving container: +``` +$ docker pull nvcr.io/nvidia/tritonserver:22.07-py3 +$ docker run -it --gpus all --rm -p8000:8000 -p8001:8001 -p8002:8002 -v /path/to/BiSeNet/tis/models:/models -v /path/to/BiSeNet/:/BiSeNet nvcr.io/nvidia/tritonserver:21.10-py3 bash +``` +From here on, we are in the container environment. Let's prepare the backends in the container: +``` +# ln -s /usr/local/bin/pip3.8 /usr/bin/pip3.8 +# /usr/bin/python3 -m pip install pillow +# apt update && apt install rapidjson-dev libopencv-dev +``` +Then we download cmake 3.22 and unzip in the container, we use this cmake 3.22 in the following operations. +We compile c++ backends: +``` +# cp -riv /BiSeNet/tis/self_backend /opt/tritonserver/backends +# chmod 777 /opt/tritonserver/backends/self_backend +# cd /opt/tritonserver/backends/self_backend +# mkdir -p build && cd build +# cmake .. && make -j4 +# mv -iuv libtriton_self_backend.so .. +``` +Utils now, we should have backends prepared. + + + +#### 3. start service +We start the server in the docker container, following the above steps: +``` +# tritonserver --model-repository=/models +``` +In general, the service would start now. You can check whether service has started by: +``` +$ curl -v localhost:8000/v2/health/ready +``` + +By default, we use gpu 0 and gpu 1, you can change configurations in the `config.pbtxt` file. + + +### Request with client + +We call the model service with both python and c++ method. + +From here on, we are at the client machine, rather than the server docker container. + + +#### 1. python method + +Firstly, we need to install dependency package: +``` +$ python -m pip install tritonclient[all]==2.15.0 +``` + +Then we can run the script for both http request and grpc request: +``` +$ cd BiSeNet/tis +$ python client_http.py # if you want to use http client +$ python client_grpc.py # if you want to use grpc client +``` + +This would generate a result file named `res.jpg` in `BiSeNet/tis` directory. + + +#### 2. c++ method + +We need to compile c++ client library from source: +``` +$ apt install rapidjson-dev +$ mkdir -p /data/ $$ cd /data/ +$ git clone https://github.com/triton-inference-server/client.git +$ cd client && git reset --hard da04158bc094925a56b +$ mkdir -p build && cd build +$ cmake -DCMAKE_INSTALL_PREFIX=/opt/triton_client -DTRITON_ENABLE_CC_HTTP=ON -DTRITON_ENABLE_CC_GRPC=ON -DTRITON_ENABLE_PERF_ANALYZER=OFF -DTRITON_ENABLE_PYTHON_HTTP=OFF -DTRITON_ENABLE_PYTHON_GRPC=OFF -DTRITON_ENABLE_JAVA_HTTP=OFF -DTRITON_ENABLE_GPU=ON -DTRITON_ENABLE_EXAMPLES=OFF -DTRITON_ENABLE_TESTS=ON .. +$ make cc-clients +``` +The above commands are exactly what I used to compile the library. I learned these commands from the official document. + +Also, We need to install `cmake` with version `3.22`. + +Optionally, I compiled opencv from source and install it to `/opt/opencv`. You can first skip this and see whether you meet problems. If you have problems about opencv in the following steps, you can compile opencv as what I do. + +After installing the dependencies, we can compile our c++ client: +``` +$ cd BiSeNet/tis/cpp_client +$ mkdir -p build && cd build +$ cmake .. && make +``` + +Finally, we run the client and see a result file named `res.jpg` generated: +``` + ./client +``` + + +### In the end + +This is a simple demo with only basic function. There are many other features that is useful, such as shared memory and dynamic batching. If you have interests on this, you can learn more in the official document. diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/client_backend.py b/cv/semantic_segmentation/bisenetv2/pytorch/tis/client_backend.py new file mode 100755 index 000000000..6784d291a --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/client_backend.py @@ -0,0 +1,81 @@ + + +import argparse +import sys +import numpy as np +import cv2 +import gevent.ssl + +import tritonclient.http as httpclient +from tritonclient.utils import InferenceServerException + + +np.random.seed(123) +palette = np.random.randint(0, 256, (100, 3)) + + +url = '10.128.61.8:8000' +# url = '127.0.0.1:8000' +model_name = 'preprocess_cpp' +model_version = '1' +inp_name = 'raw_img_bytes' +outp_name = 'processed_img' +inp_dtype = 'UINT8' +impth = '../example.png' +mean = [0.3257, 0.3690, 0.3223] # city, rgb +std = [0.2112, 0.2148, 0.2115] + + +## prepare image and mean/std +inp_data = np.fromfile(impth, dtype=np.uint8)[None, ...] +mean = np.array(mean, dtype=np.float32)[None, ...] +std = np.array(std, dtype=np.float32)[None, ...] +inputs = [] +inputs.append(httpclient.InferInput(inp_name, inp_data.shape, inp_dtype)) +inputs.append(httpclient.InferInput('channel_mean', mean.shape, 'FP32')) +inputs.append(httpclient.InferInput('channel_std', std.shape, 'FP32')) +inputs[0].set_data_from_numpy(inp_data, binary_data=True) +inputs[1].set_data_from_numpy(mean, binary_data=True) +inputs[2].set_data_from_numpy(std, binary_data=True) + +## client +triton_client = httpclient.InferenceServerClient( + url=url, verbose=False, concurrency=32) + +## infer +# sync +# results = triton_client.infer(model_name, inputs) + + +# async +# results = triton_client.async_infer( +# model_name, +# inputs, +# outputs=None, +# query_params=None, +# headers=None, +# request_compression_algorithm=None, +# response_compression_algorithm=None) +# results = results.get_result() # async infer only + + +## dynamic batching, this is not allowed, since different pictures has different raw size +results = [] +for i in range(10): + r = triton_client.async_infer( + model_name, + inputs, + outputs=None, + query_params=None, + headers=None, + request_compression_algorithm=None, + response_compression_algorithm=None) + results.append(r) +for i in range(10): + results[i].get_result() +results = results[i] + + +# get output +outp = results.as_numpy(outp_name).squeeze() +print(outp.shape) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/client_grpc.py b/cv/semantic_segmentation/bisenetv2/pytorch/tis/client_grpc.py new file mode 100755 index 000000000..5109c1e22 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/client_grpc.py @@ -0,0 +1,88 @@ + +import numpy as np +import cv2 + +import grpc + +from tritonclient.grpc import service_pb2, service_pb2_grpc +import tritonclient.grpc.model_config_pb2 as mc + + +np.random.seed(123) +palette = np.random.randint(0, 256, (100, 3)) + + + +url = '10.128.61.8:8001' +# url = '127.0.0.1:8001' +model_name = 'bisenetv1' +model_version = '1' +inp_name = 'raw_img_bytes' +outp_name = 'preds' +inp_dtype = 'UINT8' +outp_dtype = np.int64 +impth = '../example.png' +mean = [0.3257, 0.3690, 0.3223] # city, rgb +std = [0.2112, 0.2148, 0.2115] + + +## input data and mean/std +inp_data = np.fromfile(impth, dtype=np.uint8)[None, ...] +mean = np.array(mean, dtype=np.float32)[None, ...] +std = np.array(std, dtype=np.float32)[None, ...] +inputs = [service_pb2.ModelInferRequest().InferInputTensor() for _ in range(3)] +inputs[0].name = inp_name +inputs[0].datatype = inp_dtype +inputs[0].shape.extend(inp_data.shape) +inputs[1].name = 'channel_mean' +inputs[1].datatype = 'FP32' +inputs[1].shape.extend(mean.shape) +inputs[2].name = 'channel_std' +inputs[2].datatype = 'FP32' +inputs[2].shape.extend(std.shape) +inp_bytes = [inp_data.tobytes(), mean.tobytes(), std.tobytes()] + + +option = [ + ('grpc.max_receive_message_length', 1073741824), + ('grpc.max_send_message_length', 1073741824), + ] +channel = grpc.insecure_channel(url, options=option) +grpc_stub = service_pb2_grpc.GRPCInferenceServiceStub(channel) + + +metadata_request = service_pb2.ModelMetadataRequest( + name=model_name, version=model_version) +metadata_response = grpc_stub.ModelMetadata(metadata_request) +print(metadata_response) + +config_request = service_pb2.ModelConfigRequest( + name=model_name, + version=model_version) +config_response = grpc_stub.ModelConfig(config_request) +print(config_response) + + +request = service_pb2.ModelInferRequest() +request.model_name = model_name +request.model_version = model_version + +request.ClearField("inputs") +request.ClearField("raw_input_contents") +request.inputs.extend(inputs) +request.raw_input_contents.extend(inp_bytes) + + +# sync +# resp = grpc_stub.ModelInfer(request) +# async +resp = grpc_stub.ModelInfer.future(request) +resp = resp.result() + +outp_bytes = resp.raw_output_contents[0] +outp_shape = resp.outputs[0].shape + +out = np.frombuffer(outp_bytes, dtype=outp_dtype).reshape(*outp_shape).squeeze() + +out = palette[out] +cv2.imwrite('res.png', out) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/client_http.py b/cv/semantic_segmentation/bisenetv2/pytorch/tis/client_http.py new file mode 100755 index 000000000..d1fa501be --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/client_http.py @@ -0,0 +1,64 @@ + + +import argparse +import sys +import numpy as np +import cv2 +import gevent.ssl + +import tritonclient.http as httpclient +from tritonclient.utils import InferenceServerException + + +np.random.seed(123) +palette = np.random.randint(0, 256, (100, 3)) + + +url = '10.128.61.8:8000' +# url = '127.0.0.1:8000' +model_name = 'bisenetv2' +model_version = '1' +inp_name = 'raw_img_bytes' +outp_name = 'preds' +inp_dtype = 'UINT8' +impth = '../example.png' +mean = [0.3257, 0.3690, 0.3223] # city, rgb +std = [0.2112, 0.2148, 0.2115] + + +## prepare image and mean/std +inp_data = np.fromfile(impth, dtype=np.uint8)[None, ...] +mean = np.array(mean, dtype=np.float32)[None, ...] +std = np.array(std, dtype=np.float32)[None, ...] +inputs = [] +inputs.append(httpclient.InferInput(inp_name, inp_data.shape, inp_dtype)) +inputs.append(httpclient.InferInput('channel_mean', mean.shape, 'FP32')) +inputs.append(httpclient.InferInput('channel_std', std.shape, 'FP32')) +inputs[0].set_data_from_numpy(inp_data, binary_data=True) +inputs[1].set_data_from_numpy(mean, binary_data=True) +inputs[2].set_data_from_numpy(std, binary_data=True) + + +## client +triton_client = httpclient.InferenceServerClient( + url=url, verbose=False, concurrency=32) + +## infer +# sync +# results = triton_client.infer(model_name, inputs) + +# async +results = triton_client.async_infer( + model_name, + inputs, + outputs=None, + query_params=None, + headers=None, + request_compression_algorithm=None, + response_compression_algorithm=None) +results = results.get_result() # async infer only + +# get output +outp = results.as_numpy(outp_name).squeeze() +out = palette[outp] +cv2.imwrite('res.png', out) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/cpp_client/CMakeLists.txt b/cv/semantic_segmentation/bisenetv2/pytorch/tis/cpp_client/CMakeLists.txt new file mode 100755 index 000000000..15eb56f25 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/cpp_client/CMakeLists.txt @@ -0,0 +1,29 @@ +cmake_minimum_required (VERSION 3.18) + +project(Samples) + +set(CMAKE_CXX_FLAGS "-std=c++14 -O2") +set(CMAKE_BUILD_TYPE Release) + +set(CMAKE_PREFIX_PATH + /opt/triton_client/ + /opt/opencv/lib/cmake/opencv4) +find_package(OpenCV REQUIRED) + +include_directories( + ${CMAKE_CURRENT_SOURCE_DIR} + ${CMAKE_CURRENT_BINARY_DIR} + ${OpenCV_INCLUDE_DIRS} + /opt/triton_client/include +) +link_directories( + /opt/triton_client/lib + ) + + +add_executable(client main.cpp) +target_link_libraries(client PRIVATE + grpcclient + ${OpenCV_LIBS} + -lpthread + ) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/cpp_client/main.cpp b/cv/semantic_segmentation/bisenetv2/pytorch/tis/cpp_client/main.cpp new file mode 100755 index 000000000..39a326b44 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/cpp_client/main.cpp @@ -0,0 +1,326 @@ + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "grpc_client.h" +#include "common.h" + +#include + + +namespace tc = triton::client; + + +#define FAIL_IF_ERR(X, MSG) \ + { \ + tc::Error err = (X); \ + if (!err.IsOk()) { \ + std::cerr << "error: " << (MSG) << ": " << err << std::endl; \ + exit(1); \ + } \ + } + + +std::string url("10.128.61.8:8001"); +// std::string url("127.0.0.1:8001"); +std::string model_name("bisenetv1"); +std::string model_version("1"); +uint32_t client_timeout{5000000}; + +std::string impth("../../../example.png"); +std::string savepth("./res.jpg"); +std::vector mean{0.3257, 0.3690, 0.3223}; // city, rgb +std::vector var{0.2112, 0.2148, 0.2115}; +std::string inp_name("raw_img_bytes"); +std::string outp_name("preds"); +std::string inp_type("UINT8"); + + + +std::vector> get_color_map(); +std::vector get_image(std::string, std::vector&); +std::vector get_image_bytes(std::string); +void save_predict(std::string, int64_t*, std::vector); +void do_inference(); +void do_inference_with_bytes(std::vector&, bool); +void print_infos(); +void test_speed(); + + +int main() { + // print_infos(); + do_inference(); + // test_speed(); + return 0; +} + + +void do_inference() { + // create input + // std::vector inp_data = get_image(impth, inp_shape); + std::vector inp_data = get_image_bytes(impth); + std::cout << "read image: " << impth << std::endl; + do_inference_with_bytes(inp_data, true); +} + + +void do_inference_with_bytes(std::vector& inp_data, bool verbose) { + + // define client + std::unique_ptr client; + FAIL_IF_ERR( + tc::InferenceServerGrpcClient::Create(&client, url, false), // verbose=false + "unable to create grpc client"); + if (verbose) std::cout << "create client\n"; + + //// raw image + tc::InferInput* input; + FAIL_IF_ERR( + tc::InferInput::Create(&input, inp_name, + {1, static_cast(inp_data.size())}, inp_type), + "unable to get input data"); + std::shared_ptr input_ptr; + input_ptr.reset(input); + FAIL_IF_ERR(input_ptr->Reset(), "unable to reset input data"); + FAIL_IF_ERR(input_ptr->AppendRaw(inp_data), "unable to set data for input"); + //// mean/std + tc::InferInput *inp_mean, *inp_std; + FAIL_IF_ERR( + tc::InferInput::Create(&inp_mean, "channel_mean", {1, 3}, "FP32"), + "unable to get input mean"); + FAIL_IF_ERR( + tc::InferInput::Create(&inp_std, "channel_std", {1, 3}, "FP32"), + "unable to get input std"); + std::shared_ptr inp_mean_ptr, inp_std_ptr; + inp_mean_ptr.reset(inp_mean); + inp_std_ptr.reset(inp_std); + FAIL_IF_ERR(inp_mean_ptr->Reset(), "unable to reset input mean"); + FAIL_IF_ERR(inp_std_ptr->Reset(), "unable to reset input std"); + FAIL_IF_ERR( + inp_mean_ptr->AppendRaw(reinterpret_cast(&mean[0]), // must be uint8_t data type + mean.size() * sizeof(float)), + "unable to set data for input mean"); + FAIL_IF_ERR( + inp_std_ptr->AppendRaw(reinterpret_cast(&var[0]), + var.size() * sizeof(float)), + "unable to set data for input std"); + if (verbose) std::cout << "set input\n"; + + + // create output + tc::InferRequestedOutput* output; + FAIL_IF_ERR( + tc::InferRequestedOutput::Create(&output, outp_name), + "unable to get output"); + std::shared_ptr output_ptr; + output_ptr.reset(output); + if (verbose) std::cout << "set output\n"; + + // infer options + tc::InferOptions options(model_name); + options.model_version_ = model_version; + options.client_timeout_ = client_timeout; + tc::Headers http_headers; + grpc_compression_algorithm compression_algorithm = + grpc_compression_algorithm::GRPC_COMPRESS_NONE; + if (verbose) std::cout << "set options\n"; + + // inference + std::vector inputs = {input_ptr.get(), + inp_mean_ptr.get(), inp_std_ptr.get()}; + std::vector outputs = {output_ptr.get()}; + tc::InferResult* results; + FAIL_IF_ERR( + client->Infer( + &results, options, inputs, outputs, http_headers, + compression_algorithm), + "failed sending synchronous infer request"); + std::shared_ptr results_ptr; + results_ptr.reset(results); + FAIL_IF_ERR( + results_ptr->RequestStatus(), + "inference failed"); + if (verbose) std::cout << "send request and do inference\n"; + + // parse output + int64_t* raw_outp{nullptr}; // NOTE: int64_t is used according to model + size_t n_bytes{0}; + FAIL_IF_ERR( + results_ptr->RawData( + outp_name, (const uint8_t**)(&raw_outp), &n_bytes), + "fetch output failed"); + std::vector outp_shape; + FAIL_IF_ERR( + results_ptr->Shape(outp_name, &outp_shape), + "get output shape failed"); + if (n_bytes != std::accumulate(outp_shape.begin(), outp_shape.end(), 1, + std::multiplies()) * sizeof(int64_t)) { + std::cerr << "output shape is not set correctly\n"; + exit(1); + } + if (verbose) std::cout << "fetch output\n"; + + // save colorful result + save_predict(savepth, raw_outp, outp_shape); + if (verbose) std::cout << "save inference result to：" << savepth << std::endl; +} + + +std::vector get_image(std::string impth, std::vector& shape) { + int64_t iH = shape[2]; + int64_t iW = shape[3]; + cv::Mat im = cv::imread(impth); + if (im.empty()) { + std::cerr << "cv::imread failed: " << impth << std::endl; + exit(1); + } + int64_t orgH{im.rows}, orgW{im.cols}; + if ((orgH != iH) || orgW != iW) { + std::cout << "resize orignal image of (" << orgH << "," << orgW + << ") to (" << iH << ", " << iW << ") according to model requirement\n"; + cv::resize(im, im, cv::Size(iW, iH), cv::INTER_CUBIC); + } + + std::vector data(iH * iW * 3); + float mean[3] = {0.3257f, 0.3690f, 0.3223f}; + float var[3] = {0.2112f, 0.2148f, 0.2115f}; + float scale = 1.f / 255.f; + for (float &el : var) el = 1.f / el; + for (int h{0}; h < iH; ++h) { + cv::Vec3b *p = im.ptr(h); + for (int w{0}; w < iW; ++w) { + for (int c{0}; c < 3; ++c) { + int idx = (2 - c) * iH * iW + h * iW + w; // to rgb order + data[idx] = (p[w][c] * scale - mean[c]) * var[c]; + } + } + } + return data; +} + + +std::vector get_image_bytes(std::string impth) { + std::ifstream fin(impth, std::ios::in|std::ios::binary); + fin.seekg(0, fin.end); + int nbytes = fin.tellg(); + if (nbytes == -1) { + std::cerr << "image file read failed: " << impth << std::endl; + exit(1); + } + fin.clear(); + fin.seekg(0); + + std::vector res(nbytes); + fin.read(reinterpret_cast(&res[0]), nbytes); + fin.close(); + + return res; +} + +std::vector> get_color_map() { + std::vector> color_map(256, + std::vector(3)); + std::minstd_rand rand_eng(123); + std::uniform_int_distribution u(0, 255); + for (int i{0}; i < 256; ++i) { + for (int j{0}; j < 3; ++j) { + color_map[i][j] = u(rand_eng); + } + } + return color_map; +} + + +void save_predict(std::string savename, int64_t* data, + std::vector outsize) { + std::vector> color_map = get_color_map(); + int64_t oH = outsize[2]; // outsize is n1hw + int64_t oW = outsize[3]; + cv::Mat pred(cv::Size(oW, oH), CV_8UC3); + int idx{0}; + for (int i{0}; i < oH; ++i) { + uint8_t *ptr = pred.ptr(i); + for (int j{0}; j < oW; ++j) { + ptr[0] = color_map[data[idx]][0]; + ptr[1] = color_map[data[idx]][1]; + ptr[2] = color_map[data[idx]][2]; + ptr += 3; + ++idx; + } + } + cv::imwrite(savename, pred); +} + + + +void print_infos() { + // define client + std::unique_ptr client; + FAIL_IF_ERR( + tc::InferenceServerGrpcClient::Create(&client, url, false), + "unable to create grpc client"); + + tc::Headers http_headers; + inference::ModelConfigResponse model_config; + FAIL_IF_ERR( + client->ModelConfig( + &model_config, model_name, model_version, http_headers), + "unable to get config"); + + inference::ModelMetadataResponse model_metadata; + FAIL_IF_ERR( + client->ModelMetadata( + &model_metadata, model_name, model_version, http_headers), + "unable to get meta data"); + + std::cout << "---- model info ----" << std::endl; + auto input = model_metadata.inputs(0); + auto output = model_metadata.outputs(0); + std::cout << "name: " << model_metadata.name() << std::endl; + std::cout << "platform: " << model_metadata.platform() << std::endl; + std::cout << "max_batch_size: " << model_config.config().max_batch_size() << std::endl; + + int size; + size = input.shape().size(); + std::cout << input.name() << ": \n size: ("; + for (int i{0}; i < size; ++i) { + std::cout << input.shape()[i] << ", "; + } + std::cout << ")\n data_type: " << input.datatype() << std::endl;; + size = output.shape().size(); + std::cout << output.name() << ": \n size: ("; + for (int i{0}; i < size; ++i) { + std::cout << output.shape()[i] << ", "; + } + std::cout << ")\n data_type: " << output.datatype() << std::endl;; + std::cout << "--------------------" << std::endl; +} + + +void test_speed() { + + std::vector inp_data = get_image_bytes(impth); + // warmup + do_inference_with_bytes(inp_data, false); + + std::cout << "test speed ... \n"; + const int n_loops{500}; + auto start = std::chrono::steady_clock::now(); + for (int i{0}; i < n_loops; ++i) { + do_inference_with_bytes(inp_data, false); + } + auto end = std::chrono::steady_clock::now(); + + double duration = std::chrono::duration(end - start).count(); + duration /= 1000.; + std::cout << "running " << n_loops << " times, use time: " + << duration << "s" << std::endl; + std::cout << "fps is: " << static_cast(n_loops) / duration << std::endl; +} diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv1/config.pbtxt b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv1/config.pbtxt new file mode 100755 index 000000000..c35e2abd6 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv1/config.pbtxt @@ -0,0 +1,64 @@ +name: "bisenetv1" +platform: "ensemble" +max_batch_size: 256 +input [ + { + name: "raw_img_bytes" + data_type: TYPE_UINT8 + dims: [ -1 ] + }, + { + name: "channel_mean" + data_type: TYPE_FP32 + dims: [ 3 ] + }, + { + name: "channel_std" + data_type: TYPE_FP32 + dims: [ 3 ] + } +] +output [ + { + name: "preds" + data_type: TYPE_INT64 + dims: [1, 1024, 2048 ] + } +] + +ensemble_scheduling { + step [ + { + model_name: "preprocess_py" + model_version: 1 + input_map { + key: "raw_img_bytes" + value: "raw_img_bytes" + } + input_map { + key: "channel_mean" + value: "channel_mean" + } + input_map { + key: "channel_std" + value: "channel_std" + } + output_map { + key: "processed_img" + value: "processed_img" + } + }, + { + model_name: "bisenetv1_model" + model_version: 1 + input_map { + key: "input_image" + value: "processed_img" + } + output_map { + key: "preds" + value: "preds" + } + } + ] +} diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv1_model/config.pbtxt b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv1_model/config.pbtxt new file mode 100755 index 000000000..63ea2d3a2 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv1_model/config.pbtxt @@ -0,0 +1,31 @@ +name: "bisenetv1_model" +platform: "onnxruntime_onnx" +max_batch_size: 0 +input [ +{ + name: "input_image" + data_type: TYPE_FP32 + dims: [ 1, 3, 1024, 2048 ] +} +] +output [ +{ + name: "preds" + data_type: TYPE_INT64 + dims: [ 1, 1024, 2048 ] +} +] +optimization { execution_accelerators { # we use tensorrt backend, pure onnxruntime seems to have memory leackage problem + gpu_execution_accelerator : [ { + name : "tensorrt" + parameters { key: "precision_mode" value: "FP16" } + parameters { key: "max_workspace_size_bytes" value: "4294967296" } + }] +}} +instance_group [ +{ + count: 2 + kind: KIND_GPU + gpus: [ 0, 1 ] +} +] diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv2/config.pbtxt b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv2/config.pbtxt new file mode 100755 index 000000000..6db929b22 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv2/config.pbtxt @@ -0,0 +1,64 @@ +name: "bisenetv2" +platform: "ensemble" +max_batch_size: 256 +input [ + { + name: "raw_img_bytes" + data_type: TYPE_UINT8 + dims: [ -1 ] + }, + { + name: "channel_mean" + data_type: TYPE_FP32 + dims: [ 3 ] + }, + { + name: "channel_std" + data_type: TYPE_FP32 + dims: [ 3 ] + } +] +output [ + { + name: "preds" + data_type: TYPE_INT64 + dims: [1, 1024, 2048 ] + } +] + +ensemble_scheduling { + step [ + { + model_name: "preprocess_cpp" + model_version: 1 + input_map { + key: "raw_img_bytes" + value: "raw_img_bytes" + } + input_map { + key: "channel_mean" + value: "channel_mean" + } + input_map { + key: "channel_std" + value: "channel_std" + } + output_map { + key: "processed_img" + value: "processed_img" + } + }, + { + model_name: "bisenetv2_model" + model_version: 1 + input_map { + key: "input_image" + value: "processed_img" + } + output_map { + key: "preds" + value: "preds" + } + } + ] +} diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv2_model/config.pbtxt b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv2_model/config.pbtxt new file mode 100755 index 000000000..a9124a914 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/bisenetv2_model/config.pbtxt @@ -0,0 +1,31 @@ +name: "bisenetv2_model" +platform: "onnxruntime_onnx" +max_batch_size: 0 +input [ +{ + name: "input_image" + data_type: TYPE_FP32 + dims: [1, 3, 1024, 2048 ] +} +] +output [ +{ + name: "preds" + data_type: TYPE_INT64 + dims: [1, 1024, 2048 ] +} +] +optimization { execution_accelerators { # we use tensorrt backend, pure onnxruntime seems to have memory leackage problem + gpu_execution_accelerator : [ { + name : "tensorrt" + parameters { key: "precision_mode" value: "FP16" } + parameters { key: "max_workspace_size_bytes" value: "4294967296" } + }] +}} +instance_group [ +{ + count: 2 + kind: KIND_GPU + gpus: [ 0, 1 ] +} +] diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_cpp/1/.gitkeep b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_cpp/1/.gitkeep new file mode 100755 index 000000000..e69de29bb diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_cpp/config.pbtxt b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_cpp/config.pbtxt new file mode 100755 index 000000000..6dee44a3b --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_cpp/config.pbtxt @@ -0,0 +1,35 @@ +name: "preprocess_cpp" +backend: "self_backend" +max_batch_size: 256 +# dynamic_batching { ## this is not allowed, since we cannot know raw bytes size of each inputs from the server, as they just concat the bytes together +# max_queue_delay_microseconds: 5000000 +# } +input [ +{ + name: "raw_img_bytes" + data_type: TYPE_UINT8 + dims: [ -1 ] +}, +{ + name: "channel_mean" + data_type: TYPE_FP32 + dims: [ 3 ] +}, +{ + name: "channel_std" + data_type: TYPE_FP32 + dims: [ 3 ] +} +] +output [ + { + name: "processed_img" + data_type: TYPE_FP32 + dims: [ 1, 3, 1024, 2048 ] + } +] +instance_group [ + { + kind: KIND_CPU + } +] diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_py/1/model.py b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_py/1/model.py new file mode 100755 index 000000000..2af1128ea --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_py/1/model.py @@ -0,0 +1,122 @@ +import numpy as np +import sys +import json +import io + +# triton_python_backend_utils is available in every Triton Python model. You +# need to use this module to create inference requests and responses. It also +# contains some utility functions for extracting information from model_config +# and converting Triton input/output types to numpy types. +import triton_python_backend_utils as pb_utils + +from PIL import Image +import os + + +class TritonPythonModel: + """Your Python model must use the same class name. Every Python model + that is created must have "TritonPythonModel" as the class name. + """ + + def initialize(self, args): + """`initialize` is called only once when the model is being loaded. + Implementing `initialize` function is optional. This function allows + the model to intialize any state associated with this model. + + Parameters + ---------- + args : dict + Both keys and values are strings. The dictionary keys and values are: + * model_config: A JSON string containing the model configuration + * model_instance_kind: A string containing model instance kind + * model_instance_device_id: A string containing model instance device ID + * model_repository: Model repository path + * model_version: Model version + * model_name: Model name + """ + + # You must parse model_config. JSON string is not parsed here + self.model_config = model_config = json.loads(args['model_config']) + + # Get OUTPUT0 configuration + output0_config = pb_utils.get_output_config_by_name( + model_config, "processed_img") + + # Convert Triton types to numpy types + self.output0_dtype = pb_utils.triton_string_to_numpy( + output0_config['data_type']) + + self.output0_shape = output0_config['dims'] + + def execute(self, requests): + """`execute` MUST be implemented in every Python model. `execute` + function receives a list of pb_utils.InferenceRequest as the only + argument. This function is called when an inference request is made + for this model. Depending on the batching configuration (e.g. Dynamic + Batching) used, `requests` may contain multiple requests. Every + Python model, must create one pb_utils.InferenceResponse for every + pb_utils.InferenceRequest in `requests`. If there is an error, you can + set the error argument when creating a pb_utils.InferenceResponse + + Parameters + ---------- + requests : list + A list of pb_utils.InferenceRequest + + Returns + ------- + list + A list of pb_utils.InferenceResponse. The length of this list must + be the same as `requests` + """ + + output0_dtype = self.output0_dtype + N, C, H, W = self.output0_shape + + responses = [] + + # Every Python backend must iterate over everyone of the requests + # and create a pb_utils.InferenceResponse for each of them. + for request in requests: + # Get INPUT0 + im_bytes = pb_utils.get_input_tensor_by_name(request, "raw_img_bytes") + im_bytes = im_bytes.as_numpy().tobytes() + im = Image.open(io.BytesIO(im_bytes)) + im = im.resize((W, H), Image.ANTIALIAS) + im = np.array(im) + + # Get mean/std + mean = pb_utils.get_input_tensor_by_name(request, "channel_mean") + std = pb_utils.get_input_tensor_by_name(request, "channel_std") + mean = mean.as_numpy().reshape(1, 1, 3) + std = std.as_numpy().reshape(1, 1, 3) + + # preprocess + im = ((im / 255.) - mean) / std + im = im[None, ...].transpose(0, 3, 1, 2).astype(np.float32) + + + out_tensor_0 = pb_utils.Tensor("processed_img", im) + + # Create InferenceResponse. You can set an error here in case + # there was a problem with handling this inference request. + # Below is an example of how you can set errors in inference + # response: + # + # pb_utils.InferenceResponse( + # output_tensors=..., TritonError("An error occured")) + inference_response = pb_utils.InferenceResponse( + output_tensors=[out_tensor_0]) + responses.append(inference_response) + + # You should return a list of pb_utils.InferenceResponse. Length + # of this list must match the length of `requests` list. + return responses + + def finalize(self): + """`finalize` is called only once when the model is being unloaded. + Implementing `finalize` function is OPTIONAL. This function allows + the model to perform any necessary clean ups before exit. + """ + print('Cleaning up...') + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_py/config.pbtxt b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_py/config.pbtxt new file mode 100755 index 000000000..0ebdfd890 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/models/preprocess_py/config.pbtxt @@ -0,0 +1,30 @@ +name: "preprocess_py" +backend: "python" +max_batch_size: 256 +input [ +{ + name: "raw_img_bytes" + data_type: TYPE_UINT8 + dims: [ -1 ] +}, +{ + name: "channel_mean" + data_type: TYPE_FP32 + dims: [ 3 ] +}, +{ + name: "channel_std" + data_type: TYPE_FP32 + dims: [ 3 ] +} +] + +output [ +{ + name: "processed_img" + data_type: TYPE_FP32 + dims: [1, 3, 1024, 2048 ] +} +] + +instance_group [{ kind: KIND_CPU }] diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/CMakeLists.txt b/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/CMakeLists.txt new file mode 100755 index 000000000..051dc9d10 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/CMakeLists.txt @@ -0,0 +1,185 @@ +# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +cmake_minimum_required(VERSION 3.17) + +project(tutorialrecommendedbackend LANGUAGES C CXX) + +# +# Options +# +# Must include options required for this project as well as any +# projects included in this one by FetchContent. +# +# GPU support is disabled by default because recommended backend +# doesn't use GPUs. +# +option(TRITON_ENABLE_GPU "Enable GPU support in backend" OFF) +option(TRITON_ENABLE_STATS "Include statistics collections in backend" ON) + +set(TRITON_COMMON_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/common repo") +set(TRITON_CORE_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/core repo") +set(TRITON_BACKEND_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/backend repo") + +if(NOT CMAKE_BUILD_TYPE) + set(CMAKE_BUILD_TYPE Release) +endif() + +# +# Dependencies +# +# FetchContent requires us to include the transitive closure of all +# repos that we depend on so that we can override the tags. +# +include(FetchContent) + +FetchContent_Declare( + repo-common + GIT_REPOSITORY https://github.com/triton-inference-server/common.git + GIT_TAG ${TRITON_COMMON_REPO_TAG} + GIT_SHALLOW ON +) +FetchContent_Declare( + repo-core + GIT_REPOSITORY https://github.com/triton-inference-server/core.git + GIT_TAG ${TRITON_CORE_REPO_TAG} + GIT_SHALLOW ON +) +FetchContent_Declare( + repo-backend + GIT_REPOSITORY https://github.com/triton-inference-server/backend.git + GIT_TAG ${TRITON_BACKEND_REPO_TAG} + GIT_SHALLOW ON +) +FetchContent_MakeAvailable(repo-common repo-core repo-backend) + +find_package (OpenCV REQUIRED) + +# +# The backend must be built into a shared library. Use an ldscript to +# hide all symbols except for the TRITONBACKEND API. +# +configure_file(src/libtriton_recommended.ldscript libtriton_recommended.ldscript COPYONLY) + +add_library( + triton-self_backend-backend SHARED + src/recommended.cc +) + +add_library( + TutorialRecommendedBackend::triton-self_backend-backend ALIAS triton-self_backend-backend +) + +target_include_directories( + triton-self_backend-backend + PRIVATE + ${CMAKE_CURRENT_SOURCE_DIR}/src + ${OpenCV_INCLUDE_DIRS} +) + +target_compile_features(triton-self_backend-backend PRIVATE cxx_std_14) +target_compile_options( + triton-self_backend-backend PRIVATE + $<$,$,$>: + -Wall -Wextra -Wno-unused-parameter -Wno-type-limits -Werror> + $<$:/Wall /D_WIN32_WINNT=0x0A00 /EHsc> +) + +target_link_libraries( + triton-self_backend-backend + PRIVATE + triton-core-serverapi # from repo-core + triton-core-backendapi # from repo-core + triton-core-serverstub # from repo-core + triton-backend-utils # from repo-backend + ${OpenCV_LIBS} +) + +if(WIN32) + set_target_properties( + triton-self_backend-backend PROPERTIES + POSITION_INDEPENDENT_CODE ON + OUTPUT_NAME triton_self_backend + ) +else() + set_target_properties( + triton-self_backend-backend PROPERTIES + POSITION_INDEPENDENT_CODE ON + OUTPUT_NAME triton_self_backend + LINK_DEPENDS ${CMAKE_CURRENT_BINARY_DIR}/libtriton_recommended.ldscript + LINK_FLAGS "-Wl,--version-script libtriton_recommended.ldscript" + ) +endif() + +# +# Install +# +include(GNUInstallDirs) +set(INSTALL_CONFIGDIR ${CMAKE_INSTALL_LIBDIR}/cmake/TutorialRecommendedBackend) + +install( + TARGETS + triton-self_backend-backend + EXPORT + triton-self_backend-backend-targets + LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}/backends/self_backend + RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/backends/self_backend +) + +install( + EXPORT + triton-self_backend-backend-targets + FILE + TutorialRecommendedBackendTargets.cmake + NAMESPACE + TutorialRecommendedBackend:: + DESTINATION + ${INSTALL_CONFIGDIR} +) + +include(CMakePackageConfigHelpers) +configure_package_config_file( + ${CMAKE_CURRENT_LIST_DIR}/cmake/TutorialRecommendedBackendConfig.cmake.in + ${CMAKE_CURRENT_BINARY_DIR}/TutorialRecommendedBackendConfig.cmake + INSTALL_DESTINATION ${INSTALL_CONFIGDIR} +) + +install( + FILES + ${CMAKE_CURRENT_BINARY_DIR}/TutorialRecommendedBackendConfig.cmake + DESTINATION ${INSTALL_CONFIGDIR} +) + +# +# Export from build tree +# +export( + EXPORT triton-self_backend-backend-targets + FILE ${CMAKE_CURRENT_BINARY_DIR}/TutorialRecommendedBackendTargets.cmake + NAMESPACE TutorialRecommendedBackend:: +) + +export(PACKAGE TutorialRecommendedBackend) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/cmake/TutorialRecommendedBackendConfig.cmake.in b/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/cmake/TutorialRecommendedBackendConfig.cmake.in new file mode 100755 index 000000000..4007f9f8d --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/cmake/TutorialRecommendedBackendConfig.cmake.in @@ -0,0 +1,39 @@ +# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +include(CMakeFindDependencyMacro) + +get_filename_component( + TUTORIALRECOMMENDEDBACKEND_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH +) + +list(APPEND CMAKE_MODULE_PATH ${TUTORIALRECOMMENDEDBACKEND_CMAKE_DIR}) + +if(NOT TARGET TutorialRecommendedBackend::triton-recommended-backend) + include("${TUTORIALRECOMMENDEDBACKEND_CMAKE_DIR}/TutorialRecommendedBackendTargets.cmake") +endif() + +set(TUTORIALRECOMMENDEDBACKEND_LIBRARIES TutorialRecommendedBackend::triton-recommended-backend) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/src/libtriton_recommended.ldscript b/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/src/libtriton_recommended.ldscript new file mode 100755 index 000000000..748714d16 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/src/libtriton_recommended.ldscript @@ -0,0 +1,30 @@ +# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +{ + global: + TRITONBACKEND_*; + local: *; +}; diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/src/recommended.cc b/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/src/recommended.cc new file mode 100755 index 000000000..bb757cee7 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tis/self_backend/src/recommended.cc @@ -0,0 +1,916 @@ +// Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions +// are met: +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above copyright +// notice, this list of conditions and the following disclaimer in the +// documentation and/or other materials provided with the distribution. +// * Neither the name of NVIDIA CORPORATION nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +// OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#include "triton/backend/backend_common.h" +#include "triton/backend/backend_input_collector.h" +#include "triton/backend/backend_model.h" +#include "triton/backend/backend_model_instance.h" +#include "triton/backend/backend_output_responder.h" +#include "triton/core/tritonbackend.h" +#include +#include +#include +#include + +using std::cout; +using std::endl; + + +///////////// +// +// Coin: ModelEndPoint +// +// Wraps model end point information + +struct InModelEndPoint { + // Seems tis will take care of input buffer memory, so we just + // use the pointer without caring about memory release + const char *buffer; + size_t buffer_byte_size; + TRITONSERVER_MemoryType buffer_memory_type; + int64_t buffer_memory_type_id; +}; + +struct OutModelEndPoint { + // It is up to user to allocate output buffer memory, therefore + // we should take care of memory release with shared_ptr + std::shared_ptr buffer; + size_t buffer_byte_size; + TRITONSERVER_MemoryType buffer_memory_type; + int64_t buffer_memory_type_id; + std::vector shape; +}; + + +//// call back function +// Input is raw bytes, which will be decoded into image tensors +// It is not allowed to batch raw bytes inputs together to decode and preprocess +// Therefore we should not use the field of 'dynamic_batching' in the config.pbtxt +void callback_func(std::vector& inputs, + std::vector& outputs) { + + // decode image and resize + cv::Mat buffer = cv::Mat(cv::Size(1, inputs[0].buffer_byte_size), + CV_8UC1, const_cast(inputs[0].buffer)); + cv::Mat im = cv::imdecode(buffer, cv::IMREAD_COLOR); + cout << "image size: " << im.size() << endl; + int ndims = outputs[0].shape.size(); + int H = outputs[0].shape[ndims - 2]; // last two dims as hw + int W = outputs[0].shape[ndims - 1]; + cv::Mat imresized = im; + if (im.rows != H or im.cols != W) { + cv::resize(im, imresized, cv::Size(W, H), 0., 0., cv::INTER_CUBIC); + cout << "resize image into: " << imresized.size() << endl; + } + + // obtain mean/std + std::vector channel_mean(3), channel_std(3); + for (int i{0}; i < 3; ++i) { + channel_mean[i] = reinterpret_cast( + const_cast(inputs[1].buffer))[i]; + channel_std[i] = 1. / reinterpret_cast( + const_cast(inputs[2].buffer))[i]; + } + float scale = 1. / 255; + + // allocate output buffer + outputs[0].buffer = std::shared_ptr( + new char[outputs[0].buffer_byte_size], + [](const char* p) {cout<<"release output memory\n"; delete[] p;}); + float* obuf = reinterpret_cast(const_cast(outputs[0].buffer.get())); + + // divide 255 and then normalize with channel mean/std + for (int h{0}; h < H; ++h) { + cv::Vec3b *ptr = imresized.ptr(h); + for (int w{0}; w < W; ++w) { + for (int c{0}; c < 3; ++c) { + int ind = c * H * W + h * W + w; + obuf[ind] = (ptr[w][2 - c] * scale - channel_mean[c]) * channel_std[c]; + } + } + } +} + + +namespace triton { namespace backend { namespace recommended { + +// +// Backend that demonstrates the TRITONBACKEND API. This backend works +// for any model that has 1 input with any datatype and any shape and +// 1 output with the same shape and datatype as the input. The backend +// supports both batching and non-batching models. +// +// For each batch of requests, the backend returns the input tensor +// value in the output tensor. +// + +///////////// + +extern "C" { + +// Triton calls TRITONBACKEND_Initialize when a backend is loaded into +// Triton to allow the backend to create and initialize any state that +// is intended to be shared across all models and model instances that +// use the backend. The backend should also verify version +// compatibility with Triton in this function. +// +TRITONSERVER_Error* +TRITONBACKEND_Initialize(TRITONBACKEND_Backend* backend) +{ + const char* cname; + RETURN_IF_ERROR(TRITONBACKEND_BackendName(backend, &cname)); + std::string name(cname); + + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("TRITONBACKEND_Initialize: ") + name).c_str()); + + // Check the backend API version that Triton supports vs. what this + // backend was compiled against. Make sure that the Triton major + // version is the same and the minor version is >= what this backend + // uses. + uint32_t api_version_major, api_version_minor; + RETURN_IF_ERROR( + TRITONBACKEND_ApiVersion(&api_version_major, &api_version_minor)); + + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("Triton TRITONBACKEND API version: ") + + std::to_string(api_version_major) + "." + + std::to_string(api_version_minor)) + .c_str()); + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("'") + name + "' TRITONBACKEND API version: " + + std::to_string(TRITONBACKEND_API_VERSION_MAJOR) + "." + + std::to_string(TRITONBACKEND_API_VERSION_MINOR)) + .c_str()); + + if ((api_version_major != TRITONBACKEND_API_VERSION_MAJOR) || + (api_version_minor < TRITONBACKEND_API_VERSION_MINOR)) { + return TRITONSERVER_ErrorNew( + TRITONSERVER_ERROR_UNSUPPORTED, + "triton backend API version does not support this backend"); + } + + // The backend configuration may contain information needed by the + // backend, such as tritonserver command-line arguments. This + // backend doesn't use any such configuration but for this example + // print whatever is available. + TRITONSERVER_Message* backend_config_message; + RETURN_IF_ERROR( + TRITONBACKEND_BackendConfig(backend, &backend_config_message)); + + const char* buffer; + size_t byte_size; + RETURN_IF_ERROR(TRITONSERVER_MessageSerializeToJson( + backend_config_message, &buffer, &byte_size)); + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("backend configuration:\n") + buffer).c_str()); + + // This backend does not require any "global" state but as an + // example create a string to demonstrate. + std::string* state = new std::string("backend state"); + RETURN_IF_ERROR( + TRITONBACKEND_BackendSetState(backend, reinterpret_cast(state))); + + return nullptr; // success +} + +// Triton calls TRITONBACKEND_Finalize when a backend is no longer +// needed. +// +TRITONSERVER_Error* +TRITONBACKEND_Finalize(TRITONBACKEND_Backend* backend) +{ + // Delete the "global" state associated with the backend. + void* vstate; + RETURN_IF_ERROR(TRITONBACKEND_BackendState(backend, &vstate)); + std::string* state = reinterpret_cast(vstate); + + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("TRITONBACKEND_Finalize: state is '") + *state + "'") + .c_str()); + + delete state; + + return nullptr; // success +} + +} // extern "C" + +///////////// + +// +// ModelState +// +// State associated with a model that is using this backend. An object +// of this class is created and associated with each +// TRITONBACKEND_Model. ModelState is derived from BackendModel class +// provided in the backend utilities that provides many common +// functions. +// +class ModelState : public BackendModel { + public: + static TRITONSERVER_Error* Create( + TRITONBACKEND_Model* triton_model, ModelState** state); + virtual ~ModelState() = default; + + // Name of the input and output tensor + const std::string& InputTensorName(int ind) const { return input_names_[ind]; } + const std::string& OutputTensorName(int ind) const { return output_names_[ind]; } + + // Datatype of the input and output tensor + /// TRITONSERVER_DataType TensorDataType() const { return datatype_; } + TRITONSERVER_DataType InputTensorDataType(int ind) const { return inp_dtypes_[ind]; } + TRITONSERVER_DataType OutputTensorDataType(int ind) const { return outp_dtypes_[ind]; } + + // Shape of the input and output tensor as given in the model + // configuration file. This shape will not include the batch + // dimension (if the model has one). + const std::vector& InputTensorNonBatchShape(int ind) const { return inp_nb_shapes_[ind]; } + const std::vector& OutputTensorNonBatchShape(int ind) const { return outp_nb_shapes_[ind]; } + + // Shape of the input and output tensor, including the batch + // dimension (if the model has one). This method cannot be called + // until the model is completely loaded and initialized, including + // all instances of the model. In practice, this means that backend + // should only call it in TRITONBACKEND_ModelInstanceExecute. + TRITONSERVER_Error* InputTensorShape(std::vector& shape, int ind); + TRITONSERVER_Error* OutputTensorShape(std::vector& shape, int ind); + + // Number of inputs and outputs + int NumOfInputs() { return n_inps; } + int NumOfOutputs() { return n_outps; } + + // Validate that this model is supported by this backend. + TRITONSERVER_Error* ValidateModelConfig(); + + private: + ModelState(TRITONBACKEND_Model* triton_model); + + std::vector input_names_; + std::vector output_names_; + + std::vector inp_dtypes_; + std::vector outp_dtypes_; + + std::vector inp_shape_initialized_; + std::vector outp_shape_initialized_; + std::vector> inp_nb_shapes_; // no-batch shape + std::vector> inp_shapes_; + std::vector> outp_nb_shapes_; // no-batch shape + std::vector> outp_shapes_; + + int n_inps; + int n_outps; +}; + +ModelState::ModelState(TRITONBACKEND_Model* triton_model) + : BackendModel(triton_model) +{ + // Validate that the model's configuration matches what is supported + // by this backend. + THROW_IF_BACKEND_MODEL_ERROR(ValidateModelConfig()); +} + +TRITONSERVER_Error* +ModelState::Create(TRITONBACKEND_Model* triton_model, ModelState** state) +{ + try { + *state = new ModelState(triton_model); + } + catch (const BackendModelException& ex) { + RETURN_ERROR_IF_TRUE( + ex.err_ == nullptr, TRITONSERVER_ERROR_INTERNAL, + std::string("unexpected nullptr in BackendModelException")); + RETURN_IF_ERROR(ex.err_); + } + + return nullptr; // success +} + + +TRITONSERVER_Error* +ModelState::InputTensorShape(std::vector& shape, int ind) +{ + // This backend supports models that batch along the first dimension + // and those that don't batch. For non-batch models the output shape + // will be the shape from the model configuration. For batch models + // the output shape will be the shape from the model configuration + // prepended with [ -1 ] to represent the batch dimension. The + // backend "responder" utility used below will set the appropriate + // batch dimension value for each response. The shape needs to be + // initialized lazily because the SupportsFirstDimBatching function + // cannot be used until the model is completely loaded. + if (!inp_shape_initialized_[ind]) { + bool supports_first_dim_batching; + RETURN_IF_ERROR(SupportsFirstDimBatching(&supports_first_dim_batching)); + if (supports_first_dim_batching) { + inp_shapes_[ind].push_back(-1); + } + + inp_shapes_[ind].insert( + inp_shapes_[ind].end(), + inp_nb_shapes_[ind].begin(), + inp_nb_shapes_[ind].end()); + inp_shape_initialized_[ind] = true; + } + + shape = inp_shapes_[ind]; + + return nullptr; // success +} + + +TRITONSERVER_Error* +ModelState::OutputTensorShape(std::vector& shape, int ind) +{ + // This backend supports models that batch along the first dimension + // and those that don't batch. For non-batch models the output shape + // will be the shape from the model configuration. For batch models + // the output shape will be the shape from the model configuration + // prepended with [ -1 ] to represent the batch dimension. The + // backend "responder" utility used below will set the appropriate + // batch dimension value for each response. The shape needs to be + // initialized lazily because the SupportsFirstDimBatching function + // cannot be used until the model is completely loaded. + if (!outp_shape_initialized_[ind]) { + bool supports_first_dim_batching; + RETURN_IF_ERROR(SupportsFirstDimBatching(&supports_first_dim_batching)); + if (supports_first_dim_batching) { + outp_shapes_[ind].push_back(-1); + } + + outp_shapes_[ind].insert( + outp_shapes_[ind].end(), + outp_nb_shapes_[ind].begin(), + outp_nb_shapes_[ind].end()); + outp_shape_initialized_[ind] = true; + } + + shape = outp_shapes_[ind]; + + return nullptr; // success +} + + +TRITONSERVER_Error* +ModelState::ValidateModelConfig() +{ + // If verbose logging is enabled, dump the model's configuration as + // JSON into the console output. + if (TRITONSERVER_LogIsEnabled(TRITONSERVER_LOG_VERBOSE)) { + common::TritonJson::WriteBuffer buffer; + RETURN_IF_ERROR(ModelConfig().PrettyWrite(&buffer)); + LOG_MESSAGE( + TRITONSERVER_LOG_VERBOSE, + (std::string("model configuration:\n") + buffer.Contents()).c_str()); + } + + // ModelConfig is the model configuration as a TritonJson + // object. Use the TritonJson utilities to parse the JSON and + // determine if the configuration is supported by this backend. + common::TritonJson::Value inputs, outputs; + RETURN_IF_ERROR(ModelConfig().MemberAsArray("input", &inputs)); + RETURN_IF_ERROR(ModelConfig().MemberAsArray("output", &outputs)); + + + //// input name, data_type and shape from config.pbtxt + n_inps = inputs.ArraySize(); // num_inputs + inp_shapes_.resize(n_inps); + for (int i{0}; i < n_inps; ++i) { + + common::TritonJson::Value input; + RETURN_IF_ERROR(inputs.IndexAsObject(i, &input)); + + // name + const char* input_name; + size_t input_name_len; + RETURN_IF_ERROR(input.MemberAsString("name", &input_name, &input_name_len)); + input_names_.push_back(std::string(input_name)); + + // data type + std::string input_dtype; + RETURN_IF_ERROR(input.MemberAsString("data_type", &input_dtype)); + inp_dtypes_.push_back(ModelConfigDataTypeToTritonServerDataType(input_dtype)); + + // dims and shape + std::vector input_shape; + RETURN_IF_ERROR(backend::ParseShape(input, "dims", &input_shape)); + inp_nb_shapes_.push_back(input_shape); + inp_shape_initialized_.push_back(false); + + } + + //// out name, data_type and shape from config.pbtxt + n_outps = outputs.ArraySize(); // num_outputs + outp_shapes_.resize(n_outps); + for (int i{0}; i < n_outps; ++i) { + + common::TritonJson::Value output; + RETURN_IF_ERROR(outputs.IndexAsObject(i, &output)); + + // name + const char* output_name; + size_t output_name_len; + RETURN_IF_ERROR( + output.MemberAsString("name", &output_name, &output_name_len)); + output_names_.push_back(std::string(output_name)); + + // data type + std::string output_dtype; + RETURN_IF_ERROR(output.MemberAsString("data_type", &output_dtype)); + outp_dtypes_.push_back(ModelConfigDataTypeToTritonServerDataType(output_dtype)); + + // dims and shape + std::vector output_shape; + RETURN_IF_ERROR(backend::ParseShape(output, "dims", &output_shape)); + outp_nb_shapes_.push_back(output_shape); + outp_shape_initialized_.push_back(false); + } + + return nullptr; // success +} + +extern "C" { + +// Triton calls TRITONBACKEND_ModelInitialize when a model is loaded +// to allow the backend to create any state associated with the model, +// and to also examine the model configuration to determine if the +// configuration is suitable for the backend. Any errors reported by +// this function will prevent the model from loading. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelInitialize(TRITONBACKEND_Model* model) +{ + // Create a ModelState object and associate it with the + // TRITONBACKEND_Model. If anything goes wrong with initialization + // of the model state then an error is returned and Triton will fail + // to load the model. + ModelState* model_state; + RETURN_IF_ERROR(ModelState::Create(model, &model_state)); + RETURN_IF_ERROR( + TRITONBACKEND_ModelSetState(model, reinterpret_cast(model_state))); + + return nullptr; // success +} + +// Triton calls TRITONBACKEND_ModelFinalize when a model is no longer +// needed. The backend should cleanup any state associated with the +// model. This function will not be called until all model instances +// of the model have been finalized. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelFinalize(TRITONBACKEND_Model* model) +{ + void* vstate; + RETURN_IF_ERROR(TRITONBACKEND_ModelState(model, &vstate)); + ModelState* model_state = reinterpret_cast(vstate); + delete model_state; + + return nullptr; // success +} + +} // extern "C" + + +///////////// + +// +// ModelInstanceState +// +// State associated with a model instance. An object of this class is +// created and associated with each +// TRITONBACKEND_ModelInstance. ModelInstanceState is derived from +// BackendModelInstance class provided in the backend utilities that +// provides many common functions. +// +class ModelInstanceState : public BackendModelInstance { + public: + static TRITONSERVER_Error* Create( + ModelState* model_state, + TRITONBACKEND_ModelInstance* triton_model_instance, + ModelInstanceState** state); + virtual ~ModelInstanceState() = default; + + // Get the state of the model that corresponds to this instance. + ModelState* StateForModel() const { return model_state_; } + + private: + ModelInstanceState( + ModelState* model_state, + TRITONBACKEND_ModelInstance* triton_model_instance) + : BackendModelInstance(model_state, triton_model_instance), + model_state_(model_state) + { + } + + ModelState* model_state_; +}; + +TRITONSERVER_Error* +ModelInstanceState::Create( + ModelState* model_state, TRITONBACKEND_ModelInstance* triton_model_instance, + ModelInstanceState** state) +{ + try { + *state = new ModelInstanceState(model_state, triton_model_instance); + } + catch (const BackendModelInstanceException& ex) { + RETURN_ERROR_IF_TRUE( + ex.err_ == nullptr, TRITONSERVER_ERROR_INTERNAL, + std::string("unexpected nullptr in BackendModelInstanceException")); + RETURN_IF_ERROR(ex.err_); + } + + return nullptr; // success +} + +extern "C" { + +// Triton calls TRITONBACKEND_ModelInstanceInitialize when a model +// instance is created to allow the backend to initialize any state +// associated with the instance. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelInstanceInitialize(TRITONBACKEND_ModelInstance* instance) +{ + // Get the model state associated with this instance's model. + TRITONBACKEND_Model* model; + RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceModel(instance, &model)); + + void* vmodelstate; + RETURN_IF_ERROR(TRITONBACKEND_ModelState(model, &vmodelstate)); + ModelState* model_state = reinterpret_cast(vmodelstate); + + // Create a ModelInstanceState object and associate it with the + // TRITONBACKEND_ModelInstance. + ModelInstanceState* instance_state; + RETURN_IF_ERROR( + ModelInstanceState::Create(model_state, instance, &instance_state)); + RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceSetState( + instance, reinterpret_cast(instance_state))); + + return nullptr; // success +} + +// Triton calls TRITONBACKEND_ModelInstanceFinalize when a model +// instance is no longer needed. The backend should cleanup any state +// associated with the model instance. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelInstanceFinalize(TRITONBACKEND_ModelInstance* instance) +{ + void* vstate; + RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceState(instance, &vstate)); + ModelInstanceState* instance_state = + reinterpret_cast(vstate); + delete instance_state; + + return nullptr; // success +} + +} // extern "C" + +///////////// + +extern "C" { + +// When Triton calls TRITONBACKEND_ModelInstanceExecute it is required +// that a backend create a response for each request in the batch. A +// response may be the output tensors required for that request or may +// be an error that is returned in the response. +// +TRITONSERVER_Error* +TRITONBACKEND_ModelInstanceExecute( + TRITONBACKEND_ModelInstance* instance, TRITONBACKEND_Request** requests, + const uint32_t request_count) +{ + // Collect various timestamps during the execution of this batch or + // requests. These values are reported below before returning from + // the function. + + uint64_t exec_start_ns = 0; + SET_TIMESTAMP(exec_start_ns); + + // Triton will not call this function simultaneously for the same + // 'instance'. But since this backend could be used by multiple + // instances from multiple models the implementation needs to handle + // multiple calls to this function at the same time (with different + // 'instance' objects). Best practice for a high-performance + // implementation is to avoid introducing mutex/lock and instead use + // only function-local and model-instance-specific state. + ModelInstanceState* instance_state; + RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceState( + instance, reinterpret_cast(&instance_state))); + ModelState* model_state = instance_state->StateForModel(); + + // 'responses' is initialized as a parallel array to 'requests', + // with one TRITONBACKEND_Response object for each + // TRITONBACKEND_Request object. If something goes wrong while + // creating these response objects, the backend simply returns an + // error from TRITONBACKEND_ModelInstanceExecute, indicating to + // Triton that this backend did not create or send any responses and + // so it is up to Triton to create and send an appropriate error + // response for each request. RETURN_IF_ERROR is one of several + // useful macros for error handling that can be found in + // backend_common.h. + + std::vector responses; + responses.reserve(request_count); + for (uint32_t r = 0; r < request_count; ++r) { + TRITONBACKEND_Request* request = requests[r]; + TRITONBACKEND_Response* response; + RETURN_IF_ERROR(TRITONBACKEND_ResponseNew(&response, request)); + responses.push_back(response); + } + + // At this point, the backend takes ownership of 'requests', which + // means that it is responsible for sending a response for every + // request. From here, even if something goes wrong in processing, + // the backend must return 'nullptr' from this function to indicate + // success. Any errors and failures must be communicated via the + // response objects. + // + // To simplify error handling, the backend utilities manage + // 'responses' in a specific way and it is recommended that backends + // follow this same pattern. When an error is detected in the + // processing of a request, an appropriate error response is sent + // and the corresponding TRITONBACKEND_Response object within + // 'responses' is set to nullptr to indicate that the + // request/response has already been handled and no futher processing + // should be performed for that request. Even if all responses fail, + // the backend still allows execution to flow to the end of the + // function so that statistics are correctly reported by the calls + // to TRITONBACKEND_ModelInstanceReportStatistics and + // TRITONBACKEND_ModelInstanceReportBatchStatistics. + // RESPOND_AND_SET_NULL_IF_ERROR, and + // RESPOND_ALL_AND_SET_NULL_IF_ERROR are macros from + // backend_common.h that assist in this management of response + // objects. + + // The backend could iterate over the 'requests' and process each + // one separately. But for performance reasons it is usually + // preferred to create batched input tensors that are processed + // simultaneously. This is especially true for devices like GPUs + // that are capable of exploiting the large amount parallelism + // exposed by larger data sets. + // + // The backend utilities provide a "collector" to facilitate this + // batching process. The 'collector's ProcessTensor function will + // combine a tensor's value from each request in the batch into a + // single contiguous buffer. The buffer can be provided by the + // backend or 'collector' can create and manage it. In this backend, + // there is not a specific buffer into which the batch should be + // created, so use ProcessTensor arguments that cause collector to + // manage it. + + BackendInputCollector collector( + requests, request_count, &responses, model_state->TritonMemoryManager(), + false /* pinned_enabled */, nullptr /* stream*/); + + // To instruct ProcessTensor to "gather" the entire batch of input + // tensors into a single contiguous buffer in CPU memory, set the + // "allowed input types" to be the CPU ones (see tritonserver.h in + // the triton-inference-server/core repo for allowed memory types). + std::vector> allowed_input_types = + {{TRITONSERVER_MEMORY_CPU_PINNED, 0}, {TRITONSERVER_MEMORY_CPU, 0}}; + + /// Coin: Here we get input buffers and meta info, such that we can process + /// them and implement the logic of the backend + int n_inps = model_state->NumOfInputs(); + std::vector inputs(n_inps); + for (int i{0}; i < n_inps; ++i) { + RESPOND_ALL_AND_SET_NULL_IF_ERROR( + responses, request_count, + collector.ProcessTensor( + model_state->InputTensorName(i).c_str(), nullptr /* existing_buffer */, + 0 /* existing_buffer_byte_size */, allowed_input_types, + &inputs[i].buffer, + &inputs[i].buffer_byte_size, + &inputs[i].buffer_memory_type, + &inputs[i].buffer_memory_type_id)); + } + + int n_outps = model_state->NumOfOutputs(); + std::vector outputs(n_outps); + for (int i{0}; i < n_outps; ++i) { + outputs[i].buffer_memory_type = TRITONSERVER_MEMORY_CPU; + outputs[i].buffer_memory_type_id = 0; + model_state->OutputTensorShape(outputs[i].shape, i); + int64_t byte_size = TRITONSERVER_DataTypeByteSize( + model_state->OutputTensorDataType(i)); + int64_t n_pixels = std::accumulate( + outputs[i].shape.begin()+1, outputs[i].shape.end(), + 1, std::multiplies()); + outputs[i].buffer_byte_size = n_pixels * byte_size; + + /// print datatype + /// cout << TRITONSERVER_DataTypeString(model_state->OutputTensorDataType(i)) << endl; + } + + //// Coin: here we implement our logic + callback_func(inputs, outputs); + + + // Finalize the collector. If 'true' is returned, 'input_buffer' + // will not be valid until the backend synchronizes the CUDA + // stream or event that was used when creating the collector. For + // this backend, GPU is not supported and so no CUDA sync should + // be needed; so if 'true' is returned simply log an error. + const bool need_cuda_input_sync = collector.Finalize(); + if (need_cuda_input_sync) { + LOG_MESSAGE( + TRITONSERVER_LOG_ERROR, + "'recommended' backend: unexpected CUDA sync required by collector"); + } + + // 'input_buffer' contains the batched input tensor. The backend can + // implement whatever logic is necessary to produce the output + // tensor. This backend simply logs the input tensor value and then + // returns the input tensor value in the output tensor so no actual + // computation is needed. + + uint64_t compute_start_ns = 0; + SET_TIMESTAMP(compute_start_ns); + + /* + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("model ") + model_state->Name() + ": requests in batch " + + std::to_string(request_count)) + .c_str()); + std::string tstr; + IGNORE_ERROR(BufferAsTypedString( + tstr, input_buffer, input_buffer_byte_size, + model_state->TensorDataType())); + LOG_MESSAGE( + TRITONSERVER_LOG_INFO, + (std::string("batched " + model_state->InputTensorName() + " value: ") + + tstr) + .c_str()); + */ + + // const char* output_buffer = input_buffer; + // TRITONSERVER_MemoryType output_buffer_memory_type = input_buffer_memory_type; + // int64_t output_buffer_memory_type_id = input_buffer_memory_type_id; + + uint64_t compute_end_ns = 0; + SET_TIMESTAMP(compute_end_ns); + + bool supports_first_dim_batching; + RESPOND_ALL_AND_SET_NULL_IF_ERROR( + responses, request_count, + model_state->SupportsFirstDimBatching(&supports_first_dim_batching)); + + std::vector> outp_shapes(n_outps); + for (int i{0}; i < n_outps; ++i) { + RESPOND_ALL_AND_SET_NULL_IF_ERROR( + responses, request_count, model_state->OutputTensorShape( + outp_shapes[i], i)); + } + + // Because the output tensor values are concatenated into a single + // contiguous 'output_buffer', the backend must "scatter" them out + // to the individual response output tensors. The backend utilities + // provide a "responder" to facilitate this scattering process. + + // The 'responders's ProcessTensor function will copy the portion of + // 'output_buffer' corresonding to each request's output into the + // response for that request. + + BackendOutputResponder responder( + requests, request_count, &responses, model_state->TritonMemoryManager(), + supports_first_dim_batching, false /* pinned_enabled */, + nullptr /* stream*/); + + for (int i{0}; i < n_outps; ++i) { + const char* output_buffer = outputs[i].buffer.get(); + //const char* output_buffer = new char[16]; + responder.ProcessTensor( + model_state->OutputTensorName(i).c_str(), model_state->OutputTensorDataType(i), + outp_shapes[i], output_buffer, outputs[i].buffer_memory_type, + outputs[i].buffer_memory_type_id); + + } + + // Finalize the responder. If 'true' is returned, the output + // tensors' data will not be valid until the backend synchronizes + // the CUDA stream or event that was used when creating the + // responder. For this backend, GPU is not supported and so no CUDA + // sync should be needed; so if 'true' is returned simply log an + // error. + const bool need_cuda_output_sync = responder.Finalize(); + if (need_cuda_output_sync) { + LOG_MESSAGE( + TRITONSERVER_LOG_ERROR, + "'recommended' backend: unexpected CUDA sync required by responder"); + } + + // Send all the responses that haven't already been sent because of + // an earlier error. + for (auto& response : responses) { + if (response != nullptr) { + LOG_IF_ERROR( + TRITONBACKEND_ResponseSend( + response, TRITONSERVER_RESPONSE_COMPLETE_FINAL, nullptr), + "failed to send response"); + } + } + + uint64_t exec_end_ns = 0; + SET_TIMESTAMP(exec_end_ns); + +#ifdef TRITON_ENABLE_STATS + // For batch statistics need to know the total batch size of the + // requests. This is not necessarily just the number of requests, + // because if the model supports batching then any request can be a + // batched request itself. + size_t total_batch_size = 0; + if (!supports_first_dim_batching) { + total_batch_size = request_count; + } else { + for (uint32_t r = 0; r < request_count; ++r) { + auto& request = requests[r]; + TRITONBACKEND_Input* input = nullptr; + LOG_IF_ERROR( + TRITONBACKEND_RequestInputByIndex(request, 0 /* index */, &input), + "failed getting request input"); + if (input != nullptr) { + const int64_t* shape = nullptr; + LOG_IF_ERROR( + TRITONBACKEND_InputProperties( + input, nullptr, nullptr, &shape, nullptr, nullptr, nullptr), + "failed getting input properties"); + if (shape != nullptr) { + total_batch_size += shape[0]; + } + } + } + } +#else + (void)exec_start_ns; + (void)exec_end_ns; + (void)compute_start_ns; + (void)compute_end_ns; +#endif // TRITON_ENABLE_STATS + + // Report statistics for each request, and then release the request. + for (uint32_t r = 0; r < request_count; ++r) { + auto& request = requests[r]; + +#ifdef TRITON_ENABLE_STATS + LOG_IF_ERROR( + TRITONBACKEND_ModelInstanceReportStatistics( + instance_state->TritonModelInstance(), request, + (responses[r] != nullptr) /* success */, exec_start_ns, + compute_start_ns, compute_end_ns, exec_end_ns), + "failed reporting request statistics"); +#endif // TRITON_ENABLE_STATS + + LOG_IF_ERROR( + TRITONBACKEND_RequestRelease(request, TRITONSERVER_REQUEST_RELEASE_ALL), + "failed releasing request"); + } + +#ifdef TRITON_ENABLE_STATS + // Report batch statistics. + LOG_IF_ERROR( + TRITONBACKEND_ModelInstanceReportBatchStatistics( + instance_state->TritonModelInstance(), total_batch_size, + exec_start_ns, compute_start_ns, compute_end_ns, exec_end_ns), + "failed reporting batch request statistics"); +#endif // TRITON_ENABLE_STATS + + return nullptr; // success +} + +} // extern "C" + +}}} // namespace triton::backend::recommended diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/__init__.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/__init__.py new file mode 100755 index 000000000..e69de29bb diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/check_dataset_info.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/check_dataset_info.py new file mode 100755 index 000000000..37aaee5bd --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tools/check_dataset_info.py @@ -0,0 +1,118 @@ + +import os +import os.path as osp +import argparse +from tqdm import tqdm + +import cv2 +import numpy as np + + +parse = argparse.ArgumentParser() +parse.add_argument('--im_root', dest='im_root', type=str, default='./datasets/cityscapes',) +parse.add_argument('--im_anns', dest='im_anns', type=str, default='./datasets/cityscapes/train.txt',) +parse.add_argument('--lb_ignore', dest='lb_ignore', type=int, default=255) +args = parse.parse_args() + +lb_ignore = args.lb_ignore + + +with open(args.im_anns, 'r') as fr: + lines = fr.read().splitlines() + +n_pairs = len(lines) +impaths, lbpaths = [], [] +for l in lines: + impth, lbpth = l.split(',') + impth = osp.join(args.im_root, impth) + lbpth = osp.join(args.im_root, lbpth) + impaths.append(impth) + lbpaths.append(lbpth) + + +## shapes +max_shape_area, min_shape_area = [0, 0], [100000, 100000] +max_shape_height, min_shape_height = [0, 0], [100000, 100000] +max_shape_width, min_shape_width = [0, 0], [100000, 100000] +max_lb_val, min_lb_val = -1, 10000000 +for impth, lbpth in tqdm(zip(impaths, lbpaths), total=n_pairs): + im = cv2.imread(impth)[:, :, ::-1] + lb = cv2.imread(lbpth, 0) + assert im.shape[:2] == lb.shape + + shape = lb.shape + area = shape[0] * shape[1] + if area > max_shape_area[0] * max_shape_area[1]: + max_shape_area = shape + if area < min_shape_area[0] * min_shape_area[1]: + min_shape_area = shape + + if shape[0] > max_shape_height[0]: + max_shape_height = shape + if shape[0] < min_shape_height[0]: + min_shape_height = shape + + if shape[1] > max_shape_width[1]: + max_shape_width = shape + if shape[1] < min_shape_width[1]: + min_shape_width = shape + + lb = lb[lb != lb_ignore] + if lb.size > 0: + max_lb_val = max(max_lb_val, np.max(lb)) + min_lb_val = min(min_lb_val, np.min(lb)) + +## label info +lb_minlength = max_lb_val+1-min_lb_val +lb_hist = np.zeros(lb_minlength) +for lbpth in tqdm(lbpaths): + lb = cv2.imread(lbpth, 0) + lb = lb[lb != lb_ignore] - min_lb_val + lb_hist += np.bincount(lb, minlength=lb_minlength) + +lb_missing_vals = [ind + min_lb_val + for ind, el in enumerate(lb_hist.tolist()) if el == 0] +lb_ratios = (lb_hist / lb_hist.sum()).tolist() + + +## pixel mean/std +rgb_mean = np.zeros(3).astype(np.float32) +n_pixels = 0 +for impth in tqdm(impaths): + im = cv2.imread(impth)[:, :, ::-1].astype(np.float32) + im = im.reshape(-1, 3) / 255. + n_pixels += im.shape[0] + rgb_mean += im.sum(axis=0) +rgb_mean = (rgb_mean / n_pixels) + +rgb_std = np.zeros(3).astype(np.float32) +for impth in tqdm(impaths): + im = cv2.imread(impth)[:, :, ::-1].astype(np.float32) + im = im.reshape(-1, 3) / 255. + + a = (im - rgb_mean.reshape(1, 3)) ** 2 + rgb_std += a.sum(axis=0) +rgb_std = (rgb_std / n_pixels) ** 0.5 + +rgb_mean = rgb_mean.tolist() +rgb_std = rgb_std.tolist() + + +print('\n') +print(f'there are {n_pairs} lines in {args.im_anns}, which means {n_pairs} image/label image pairs') +print('\n') + +print(f'max and min image shapes by area are: {max_shape_area}, {min_shape_area}') +print(f'max and min image shapes by height are: {max_shape_height}, {min_shape_height}') +print(f'max and min image shapes by width are: {max_shape_width}, {min_shape_width}') +print('\n') + +print(f'we ignore label value of {args.lb_ignore} in label images') +print(f'label values are within range of [{min_lb_val}, {max_lb_val}]') +print(f'label values that are missing: {lb_missing_vals}') +print('ratios of each label value(from small to big, without ignored): ') +print('\t', lb_ratios) +print('\n') + +print('pixel mean rgb: ', rgb_mean) +print('pixel std rgb: ', rgb_std) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/conver_to_trt.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/conver_to_trt.py new file mode 100755 index 000000000..7b5d46cc3 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tools/conver_to_trt.py @@ -0,0 +1,39 @@ +import argparse +import os.path as osp +import sys +sys.path.insert(0, '.') + +import torch +from torch2trt import torch2trt + +from lib.models import model_factory +from configs import set_cfg_from_file + +torch.set_grad_enabled(False) + + +parse = argparse.ArgumentParser() +parse.add_argument('--config', dest='config', type=str, default='configs/bisenetv2.py',) +parse.add_argument('--weight-path', type=str, default='./res/model_final.pth',) +parse.add_argument('--fp16', action='store_true') +parse.add_argument('--outpath', dest='out_pth', type=str, + default='model.trt') +args = parse.parse_args() + + +cfg = set_cfg_from_file(args.config) +if cfg.use_sync_bn: cfg.use_sync_bn = False + +net = model_factory[cfg.model_type](cfg.n_cats, aux_mode='pred') +net.load_state_dict(torch.load(args.weight_path), strict=False) +net.cuda() +net.eval() + + +# dummy_input = torch.randn(1, 3, *cfg.crop_size) +dummy_input = torch.randn(1, 3, 1024, 2048).cuda() + +trt_model = torch2trt(net, [dummy_input, ], fp16_mode=args.fp16, max_workspace=1 << 30) + +with open(args.out_pth, 'wb') as fw: + fw.write(trt_model.engine.serialize()) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/demo.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/demo.py new file mode 100755 index 000000000..e437c1aff --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tools/demo.py @@ -0,0 +1,63 @@ + +import sys +sys.path.insert(0, '.') +import argparse +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from PIL import Image +import numpy as np +import cv2 + +import lib.data.transform_cv2 as T +from lib.models import model_factory +from configs import set_cfg_from_file + + +# uncomment the following line if you want to reduce cpu usage, see issue #231 +# torch.set_num_threads(4) + +torch.set_grad_enabled(False) +np.random.seed(123) + + +# args +parse = argparse.ArgumentParser() +parse.add_argument('--config', dest='config', type=str, default='configs/bisenetv2.py',) +parse.add_argument('--weight-path', type=str, default='./res/model_final.pth',) +parse.add_argument('--img-path', dest='img_path', type=str, default='./example.png',) +args = parse.parse_args() +cfg = set_cfg_from_file(args.config) + + +palette = np.random.randint(0, 256, (256, 3), dtype=np.uint8) + +# define model +net = model_factory[cfg.model_type](cfg.n_cats, aux_mode='eval') +net.load_state_dict(torch.load(args.weight_path, map_location='cpu'), strict=False) +net.eval() +net.cuda() + +# prepare data +to_tensor = T.ToTensor( + mean=(0.3257, 0.3690, 0.3223), # city, rgb + std=(0.2112, 0.2148, 0.2115), +) +im = cv2.imread(args.img_path)[:, :, ::-1] +im = to_tensor(dict(im=im, lb=None))['im'].unsqueeze(0).cuda() + +# shape divisor +org_size = im.size()[2:] +new_size = [math.ceil(el / 32) * 32 for el in im.size()[2:]] + +# inference +im = F.interpolate(im, size=new_size, align_corners=False, mode='bilinear') +out = net(im)[0] +out = F.interpolate(out, size=org_size, align_corners=False, mode='bilinear') +out = out.argmax(dim=1) + +# visualize +out = out.squeeze().detach().cpu().numpy() +pred = palette[out] +cv2.imwrite('./res.jpg', pred) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/demo_video.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/demo_video.py new file mode 100755 index 000000000..d3df05f7d --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tools/demo_video.py @@ -0,0 +1,141 @@ + +import sys +sys.path.insert(0, '.') +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.multiprocessing as mp +import time +from PIL import Image +import numpy as np +import cv2 + +import lib.data.transform_cv2 as T +from lib.models import model_factory +from configs import set_cfg_from_file + + +torch.set_grad_enabled(False) + + +# args +parse = argparse.ArgumentParser() +parse.add_argument('--config', dest='config', type=str, default='configs/bisenetv2.py',) +parse.add_argument('--weight-path', type=str, default='./res/model_final.pth',) +parse.add_argument('--input', dest='input', type=str, default='./example.mp4',) +parse.add_argument('--output', dest='output', type=str, default='./res.mp4',) +args = parse.parse_args() +cfg = set_cfg_from_file(args.config) + + + +# define model +def get_model(): + net = model_factory[cfg.model_type](cfg.n_cats, aux_mode='eval') + net.load_state_dict(torch.load(args.weight_path, map_location='cpu'), strict=False) + net.eval() + net.cuda() + return net + + +# fetch frames +def get_func(inpth, in_q, done): + cap = cv2.VideoCapture(args.input) + width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # type is float + height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # type is float + fps = cap.get(cv2.CAP_PROP_FPS) + + to_tensor = T.ToTensor( + mean=(0.3257, 0.3690, 0.3223), # city, rgb + std=(0.2112, 0.2148, 0.2115), + ) + + while cap.isOpened(): + ret, frame = cap.read() + if not ret: break + frame = frame[:, :, ::-1] + frame = to_tensor(dict(im=frame, lb=None))['im'].unsqueeze(0) + in_q.put(frame) + + in_q.put('quit') + done.wait() + + cap.release() + time.sleep(1) + print('input queue done') + + +# save to video +def save_func(inpth, outpth, out_q): + np.random.seed(123) + palette = np.random.randint(0, 256, (256, 3), dtype=np.uint8) + + cap = cv2.VideoCapture(args.input) + width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # type is float + height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # type is float + fps = cap.get(cv2.CAP_PROP_FPS) + cap.release() + + video_writer = cv2.VideoWriter(outpth, + cv2.VideoWriter_fourcc(*"mp4v"), + fps, (int(width), int(height))) + + while True: + out = out_q.get() + if out == 'quit': break + out = out.numpy() + preds = palette[out] + for pred in preds: + video_writer.write(pred) + video_writer.release() + print('output queue done') + + +# inference a list of frames +def infer_batch(frames): + frames = torch.cat(frames, dim=0).cuda() + H, W = frames.size()[2:] + frames = F.interpolate(frames, size=(768, 768), mode='bilinear', + align_corners=False) # must be divisible by 32 + out = net(frames)[0] + out = F.interpolate(out, size=(H, W), mode='bilinear', + align_corners=False).argmax(dim=1).detach().cpu() + out_q.put(out) + + + +if __name__ == '__main__': + mp.set_start_method('spawn') + + in_q = mp.Queue(1024) + out_q = mp.Queue(1024) + done = mp.Event() + + in_worker = mp.Process(target=get_func, + args=(args.input, in_q, done)) + out_worker = mp.Process(target=save_func, + args=(args.input, args.output, out_q)) + + in_worker.start() + out_worker.start() + + net = get_model() + + frames = [] + while True: + frame = in_q.get() + if frame == 'quit': break + + frames.append(frame) + if len(frames) == 8: + infer_batch(frames) + frames = [] + if len(frames) > 0: + infer_batch(frames) + + out_q.put('quit') + done.set() + + out_worker.join() + in_worker.join() diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/evaluate.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/evaluate.py new file mode 100755 index 000000000..435affc43 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tools/evaluate.py @@ -0,0 +1,454 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import sys +sys.path.insert(0, '.') +import os +import os.path as osp +import logging +import argparse +import math +from tabulate import tabulate + +from tqdm import tqdm +import numpy as np +import cv2 + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.distributed as dist + +from lib.models import model_factory +from configs import set_cfg_from_file +from lib.logger import setup_logger +from lib.data import get_data_loader + + +def get_round_size(size, divisor=32): + return [math.ceil(el / divisor) * divisor for el in size] + + +class SizePreprocessor(object): + + def __init__(self, shape=None, shortside=None, longside=None): + self.shape = shape + self.shortside = shortside + self.longside = longside + + def __call__(self, imgs): + new_size = None + if not self.shape is None: + new_size = self.shape + elif not self.shortside is None: + h, w = imgs.size()[2:] + ss = self.shortside + if h < w: h, w = ss, int(ss / h * w) + else: h, w = int(ss / w * h), ss + new_size = h, w + elif not self.longside is None: # long size limit + h, w = imgs.size()[2:] + if max(h, w) > self.longside: + ls = self.longside + if h < w: h, w = int(ls / w * h), ls + else: h, w = ls, int(ls / h * w) + new_size = h, w + + if not new_size is None: + imgs = F.interpolate(imgs, size=new_size, + mode='bilinear', align_corners=False) + return imgs + + + +class Metrics(object): + + def __init__(self, n_classes, lb_ignore=255): + self.n_classes = n_classes + self.lb_ignore = lb_ignore + self.confusion = torch.zeros((n_classes, n_classes)).cuda().detach() + + @torch.no_grad() + def update(self, preds, label): + keep = label != self.lb_ignore + preds, label = preds[keep], label[keep] + self.confusion += torch.bincount( + label * self.n_classes + preds, + minlength=self.n_classes ** 2 + ).view(self.n_classes, self.n_classes) + + @torch.no_grad() + def compute_metrics(self,): + if dist.is_initialized(): + dist.all_reduce(self.confusion, dist.ReduceOp.SUM) + + confusion = self.confusion + weights = confusion.sum(dim=1) / confusion.sum() + tps = confusion.diag() + fps = confusion.sum(dim=0) - tps + fns = confusion.sum(dim=1) - tps + + # iou and fw miou + # ious = confusion.diag() / (confusion.sum(dim=0) + confusion.sum(dim=1) - confusion.diag() + 1) + ious = tps / (tps + fps + fns + 1) + miou = ious.nanmean() + fw_miou = torch.sum(weights * ious) + + eps = 1e-6 + # macro f1 score + macro_precision = tps / (tps + fps + 1) + macro_recall = tps / (tps + fns + 1) + f1_scores = (2 * macro_precision * macro_recall) / ( + macro_precision + macro_recall + eps) + macro_f1 = f1_scores.nanmean(dim=0) + + # micro f1 score + tps_ = tps.sum(dim=0) + fps_ = fps.sum(dim=0) + fns_ = fns.sum(dim=0) + micro_precision = tps_ / (tps_ + fps_ + 1) + micro_recall = tps_ / (tps_ + fns_ + 1) + micro_f1 = (2 * micro_precision * micro_recall) / ( + micro_precision + micro_recall + eps) + + metric_dict = dict( + weights=weights.tolist(), + ious=ious.tolist(), + miou=miou.item(), + fw_miou=fw_miou.item(), + f1_scores=f1_scores.tolist(), + macro_f1=macro_f1.item(), + micro_f1=micro_f1.item(), + ) + return metric_dict + + + +class MscEvalV0(object): + + def __init__(self, n_classes, scales=(0.5, ), flip=False, lb_ignore=255, size_processor=None): + self.n_classes = n_classes + self.scales = scales + self.flip = flip + self.ignore_label = lb_ignore + self.sp = size_processor + self.metric_observer = Metrics(n_classes, lb_ignore) + + @torch.no_grad() + def __call__(self, net, dl): + ## evaluate + n_classes = self.n_classes + hist = torch.zeros(n_classes, n_classes).cuda().detach() + if dist.is_initialized() and dist.get_rank() != 0: + diter = enumerate(dl) + else: + diter = enumerate(tqdm(dl)) + for i, (imgs, label) in diter: + imgs = self.sp(imgs) + N, _, H, W = imgs.shape + label = label.squeeze(1).cuda() + size = label.size()[-2:] + probs = torch.zeros( + (N, n_classes, *size), + dtype=torch.float32).cuda().detach() + for scale in self.scales: + sH, sW = int(scale * H), int(scale * W) + sH, sW = get_round_size((sH, sW)) + im_sc = F.interpolate(imgs, size=(sH, sW), + mode='bilinear', align_corners=True) + + im_sc = im_sc.cuda() + logits = net(im_sc)[0] + logits = F.interpolate(logits, size=size, + mode='bilinear', align_corners=True) + probs += torch.softmax(logits, dim=1) + if self.flip: + im_sc = torch.flip(im_sc, dims=(3, )) + logits = net(im_sc)[0] + logits = torch.flip(logits, dims=(3, )) + logits = F.interpolate(logits, size=size, + mode='bilinear', align_corners=True) + probs += torch.softmax(logits, dim=1) + preds = torch.argmax(probs, dim=1) + self.metric_observer.update(preds, label) + + metric_dict = self.metric_observer.compute_metrics() + return metric_dict + + + +class MscEvalCrop(object): + + def __init__( + self, + n_classes, + cropsize=1024, + cropstride=2./3, + flip=True, + scales=[0.5, 0.75, 1, 1.25, 1.5, 1.75], + lb_ignore=255, + size_processor=None + ): + self.n_classes = n_classes + self.scales = scales + self.ignore_label = lb_ignore + self.flip = flip + self.sp = size_processor + self.metric_observer = Metrics(n_classes, lb_ignore) + + self.cropsize = cropsize if isinstance(cropsize, (list, tuple)) else (cropsize, cropsize) + self.cropstride = cropstride + + + def pad_tensor(self, inten): + N, C, H, W = inten.size() + cropH, cropW = self.cropsize + if cropH < H and cropW < W: return inten, [0, H, 0, W] + padH, padW = max(cropH, H), max(cropW, W) + outten = torch.zeros(N, C, padH, padW).cuda() + outten.requires_grad_(False) + marginH, marginW = padH - H, padW - W + hst, hed = marginH // 2, marginH // 2 + H + wst, wed = marginW // 2, marginW // 2 + W + outten[:, :, hst:hed, wst:wed] = inten + return outten, [hst, hed, wst, wed] + + + def eval_chip(self, net, crop): + prob = net(crop)[0].softmax(dim=1) + if self.flip: + crop = torch.flip(crop, dims=(3,)) + prob += net(crop)[0].flip(dims=(3,)).softmax(dim=1) + prob = torch.exp(prob) + return prob + + + def crop_eval(self, net, im, n_classes): + cropH, cropW = self.cropsize + stride_rate = self.cropstride + im, indices = self.pad_tensor(im) + N, C, H, W = im.size() + + strdH = math.ceil(cropH * stride_rate) + strdW = math.ceil(cropW * stride_rate) + n_h = math.ceil((H - cropH) / strdH) + 1 + n_w = math.ceil((W - cropW) / strdW) + 1 + prob = torch.zeros(N, n_classes, H, W).cuda() + prob.requires_grad_(False) + for i in range(n_h): + for j in range(n_w): + stH, stW = strdH * i, strdW * j + endH, endW = min(H, stH + cropH), min(W, stW + cropW) + stH, stW = endH - cropH, endW - cropW + chip = im[:, :, stH:endH, stW:endW] + prob[:, :, stH:endH, stW:endW] += self.eval_chip(net, chip) + hst, hed, wst, wed = indices + prob = prob[:, :, hst:hed, wst:wed] + return prob + + + def scale_crop_eval(self, net, im, scale, size, n_classes): + N, C, H, W = im.size() + new_hw = [int(H * scale), int(W * scale)] + im = F.interpolate(im, new_hw, mode='bilinear', align_corners=True) + prob = self.crop_eval(net, im, n_classes) + prob = F.interpolate(prob, size, mode='bilinear', align_corners=True) + return prob + + + @torch.no_grad() + def __call__(self, net, dl): + + n_classes = self.n_classes + hist = torch.zeros(n_classes, n_classes).cuda().detach() + hist.requires_grad_(False) + if dist.is_initialized() and dist.get_rank() != 0: + diter = enumerate(dl) + else: + diter = enumerate(tqdm(dl)) + + for i, (imgs, label) in diter: + imgs = imgs.cuda() + imgs = self.sp(imgs) + label = label.squeeze(1).cuda() + N, *size = label.size() + + probs = torch.zeros((N, n_classes, *size)).cuda() + probs.requires_grad_(False) + for sc in self.scales: + probs += self.scale_crop_eval(net, imgs, sc, size, n_classes) + torch.cuda.empty_cache() + preds = torch.argmax(probs, dim=1) + self.metric_observer.update(preds, label) + + metric_dict = self.metric_observer.compute_metrics() + return metric_dict + + +def print_res_table(tname, heads, weights, metric, cat_metric): + heads = [tname, 'ratio'] + heads + lines = [] + for k, v in metric.items(): + line = [k, '-'] + [f'{el:.6f}' for el in v] + lines.append(line) + cat_res = [weights,] + cat_metric + cat_res = [ + [f'cat {idx}',] + [f'{el:.6f}' for el in group] + for idx,group in enumerate(zip(*cat_res))] + content = cat_res + lines + return heads, content + + +@torch.no_grad() +def eval_model(cfg, net): + org_aux = net.aux_mode + net.aux_mode = 'eval' + net.eval() + + is_dist = dist.is_initialized() + dl = get_data_loader(cfg, mode='val') + lb_ignore = dl.dataset.lb_ignore + + heads, mious, fw_mious, cat_ious = [], [], [], [] + f1_scores, macro_f1, micro_f1 = [], [], [] + logger = logging.getLogger() + + size_processor = SizePreprocessor( + cfg.get('eval_start_shape'), + cfg.get('eval_start_shortside'), + cfg.get('eval_start_longside'), + ) + + single_scale = MscEvalV0( + n_classes=cfg.n_cats, + scales=(1., ), + flip=False, + lb_ignore=lb_ignore, + size_processor=size_processor + ) + logger.info('compute single scale metrics') + metrics = single_scale(net, dl) + heads.append('ss') + mious.append(metrics['miou']) + fw_mious.append(metrics['fw_miou']) + cat_ious.append(metrics['ious']) + f1_scores.append(metrics['f1_scores']) + macro_f1.append(metrics['macro_f1']) + micro_f1.append(metrics['micro_f1']) + + single_crop = MscEvalCrop( + n_classes=cfg.n_cats, + cropsize=cfg.eval_crop, + cropstride=2. / 3, + flip=False, + scales=(1., ), + lb_ignore=lb_ignore, + size_processor=size_processor + ) + logger.info('compute single scale crop metrics') + metrics = single_crop(net, dl) + heads.append('ssc') + mious.append(metrics['miou']) + fw_mious.append(metrics['fw_miou']) + cat_ious.append(metrics['ious']) + f1_scores.append(metrics['f1_scores']) + macro_f1.append(metrics['macro_f1']) + micro_f1.append(metrics['micro_f1']) + + ms_flip = MscEvalV0( + n_classes=cfg.n_cats, + scales=cfg.eval_scales, + flip=True, + lb_ignore=lb_ignore, + size_processor=size_processor + ) + logger.info('compute multi scale flip metrics') + metrics = ms_flip(net, dl) + heads.append('msf') + mious.append(metrics['miou']) + fw_mious.append(metrics['fw_miou']) + cat_ious.append(metrics['ious']) + f1_scores.append(metrics['f1_scores']) + macro_f1.append(metrics['macro_f1']) + micro_f1.append(metrics['micro_f1']) + + ms_flip_crop = MscEvalCrop( + n_classes=cfg.n_cats, + cropsize=cfg.eval_crop, + cropstride=2. / 3, + flip=True, + scales=cfg.eval_scales, + lb_ignore=lb_ignore, + size_processor=size_processor + ) + logger.info('compute multi scale flip crop metrics') + metrics = ms_flip_crop(net, dl) + heads.append('msfc') + mious.append(metrics['miou']) + fw_mious.append(metrics['fw_miou']) + cat_ious.append(metrics['ious']) + f1_scores.append(metrics['f1_scores']) + macro_f1.append(metrics['macro_f1']) + micro_f1.append(metrics['micro_f1']) + + weights = metrics['weights'] + + metric = dict(mious=mious, fw_mious=fw_mious) + iou_heads, iou_content = print_res_table('iou', heads, + weights, metric, cat_ious) + metric = dict(macro_f1=macro_f1, micro_f1=micro_f1) + f1_heads, f1_content = print_res_table('f1 score', heads, + weights, metric, f1_scores) + + net.aux_mode = org_aux + return iou_heads, iou_content, f1_heads, f1_content + + +def evaluate(cfg, weight_pth): + logger = logging.getLogger() + + ## model + logger.info('setup and restore model') + net = model_factory[cfg.model_type](cfg.n_cats) + net.load_state_dict(torch.load(weight_pth, map_location='cpu')) + net.cuda() + + # if dist.is_initialized(): + # local_rank = dist.get_rank() + # net = nn.parallel.DistributedDataParallel( + # net, + # device_ids=[local_rank, ], + # output_device=local_rank + # ) + + ## evaluator + iou_heads, iou_content, f1_heads, f1_content = eval_model(cfg, net) + logger.info('\neval results of f1 score metric:') + logger.info('\n' + tabulate(f1_content, headers=f1_heads, tablefmt='orgtbl')) + logger.info('\neval results of miou metric:') + logger.info('\n' + tabulate(iou_content, headers=iou_heads, tablefmt='orgtbl')) + + +def parse_args(): + parse = argparse.ArgumentParser() + parse.add_argument('--weight-path', dest='weight_pth', type=str, + default='model_final.pth',) + parse.add_argument('--config', dest='config', type=str, + default='configs/bisenetv2.py',) + return parse.parse_args() + + +def main(): + args = parse_args() + cfg = set_cfg_from_file(args.config) + if 'LOCAL_RANK' in os.environ: + local_rank = int(os.environ['LOCAL_RANK']) + torch.cuda.set_device(local_rank) + dist.init_process_group(backend='nccl') + if not osp.exists(cfg.respth): os.makedirs(cfg.respth) + setup_logger(f'{cfg.model_type}-{cfg.dataset.lower()}-eval', cfg.respth) + evaluate(cfg, args.weight_pth) + + +if __name__ == "__main__": + main() diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/export_libtorch.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/export_libtorch.py new file mode 100755 index 000000000..7a566e89b --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tools/export_libtorch.py @@ -0,0 +1,37 @@ +import argparse +import os.path as osp +import sys +sys.path.insert(0, '.') + +import torch + +from lib.models import model_factory +from configs import set_cfg_from_file + +torch.set_grad_enabled(False) + + +parse = argparse.ArgumentParser() +parse.add_argument('--config', dest='config', type=str, + default='configs/bisenetv2.py',) +parse.add_argument('--weight-path', dest='weight_pth', type=str, + default='model_final.pth') +parse.add_argument('--outpath', dest='out_pth', type=str, + default='model.pt') +args = parse.parse_args() + + +cfg = set_cfg_from_file(args.config) +if cfg.use_sync_bn: cfg.use_sync_bn = False + +net = model_factory[cfg.model_type](cfg.n_cats, aux_mode='pred') +net.load_state_dict(torch.load(args.weight_pth, map_location='cpu'), strict=False) +net.eval() + + +# dummy_input = torch.randn(1, 3, *cfg.crop_size) +dummy_input = torch.randn(1, 3, 1024, 2048) +script_module = torch.jit.trace(net, dummy_input) +# script_module.save(args.out_pth, _use_new_zipfile_serialization=False) +script_module.save(args.out_pth) + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/export_onnx.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/export_onnx.py new file mode 100755 index 000000000..7acd98e0f --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tools/export_onnx.py @@ -0,0 +1,42 @@ +import argparse +import os.path as osp +import sys +sys.path.insert(0, '.') + +import torch + +from lib.models import model_factory +from configs import set_cfg_from_file + +torch.set_grad_enabled(False) + + +parse = argparse.ArgumentParser() +parse.add_argument('--config', dest='config', type=str, + default='configs/bisenetv2.py',) +parse.add_argument('--weight-path', dest='weight_pth', type=str, + default='model_final.pth') +parse.add_argument('--outpath', dest='out_pth', type=str, + default='model.onnx') +parse.add_argument('--aux-mode', dest='aux_mode', type=str, + default='pred') +args = parse.parse_args() + + +cfg = set_cfg_from_file(args.config) +if cfg.use_sync_bn: cfg.use_sync_bn = False + +net = model_factory[cfg.model_type](cfg.n_cats, aux_mode=args.aux_mode) +net.load_state_dict(torch.load(args.weight_pth, map_location='cpu'), strict=False) +net.eval() + + +# dummy_input = torch.randn(1, 3, *cfg.crop_size) +dummy_input = torch.randn(1, 3, 1024, 2048) +input_names = ['input_image'] +output_names = ['preds',] + +torch.onnx.export(net, dummy_input, args.out_pth, + input_names=input_names, output_names=output_names, + verbose=False, opset_version=11, ) + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/gen_dataset_annos.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/gen_dataset_annos.py new file mode 100755 index 000000000..bcba9697d --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tools/gen_dataset_annos.py @@ -0,0 +1,88 @@ + +import os +import os.path as osp +import argparse + + +def gen_coco(): + ''' + root_path: + |- images + |- train2017 + |- val2017 + |- labels + |- train2017 + |- val2017 + ''' + root_path = './datasets/coco' + save_path = './datasets/coco/' + for mode in ('train', 'val'): + im_root = osp.join(root_path, f'images/{mode}2017') + lb_root = osp.join(root_path, f'labels/{mode}2017') + + ims = os.listdir(im_root) + lbs = os.listdir(lb_root) + + print(len(ims)) + print(len(lbs)) + + im_names = [el.replace('.jpg', '') for el in ims] + lb_names = [el.replace('.png', '') for el in lbs] + common_names = list(set(im_names) & set(lb_names)) + + lines = [ + f'images/{mode}2017/{name}.jpg,labels/{mode}2017/{name}.png' + for name in common_names + ] + + with open(f'{save_path}/{mode}.txt', 'w') as fw: + fw.write('\n'.join(lines)) + + +def gen_ade20k(): + ''' + root_path: + |- images + |- training + |- validation + |- annotations + |- training + |- validation + ''' + root_path = './datasets/ade20k/' + save_path = './datasets/ade20k/' + folder_map = {'train': 'training', 'val': 'validation'} + for mode in ('train', 'val'): + folder = folder_map[mode] + im_root = osp.join(root_path, f'images/{folder}') + lb_root = osp.join(root_path, f'annotations/{folder}') + + ims = os.listdir(im_root) + lbs = os.listdir(lb_root) + + print(len(ims)) + print(len(lbs)) + + im_names = [el.replace('.jpg', '') for el in ims] + lb_names = [el.replace('.png', '') for el in lbs] + common_names = list(set(im_names) & set(lb_names)) + + lines = [ + f'images/{folder}/{name}.jpg,annotations/{folder}/{name}.png' + for name in common_names + ] + + with open(f'{save_path}/{mode}.txt', 'w') as fw: + fw.write('\n'.join(lines)) + + + +if __name__ == '__main__': + parse = argparse.ArgumentParser() + parse.add_argument('--dataset', dest='dataset', type=str, default='coco') + args = parse.parse_args() + + if args.dataset == 'coco': + gen_coco() + elif args.dataset == 'ade20k': + gen_ade20k() diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/tools/train_amp.py b/cv/semantic_segmentation/bisenetv2/pytorch/tools/train_amp.py new file mode 100755 index 000000000..08207d03a --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/tools/train_amp.py @@ -0,0 +1,210 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import sys +sys.path.insert(0, '.') +import os +import os.path as osp +import random +import logging +import time +import json +import argparse +import numpy as np +from tabulate import tabulate + +import torch +import torch.nn as nn +import torch.distributed as dist +from torch.utils.data import DataLoader +import torch.cuda.amp as amp + +from lib.models import model_factory +from configs import set_cfg_from_file +from lib.data import get_data_loader +from evaluate import eval_model +from lib.ohem_ce_loss import OhemCELoss +from lib.lr_scheduler import WarmupPolyLrScheduler +from lib.meters import TimeMeter, AvgMeter +from lib.logger import setup_logger, print_log_msg + + + +## fix all random seeds +# torch.manual_seed(123) +# torch.cuda.manual_seed(123) +# np.random.seed(123) +# random.seed(123) +# torch.backends.cudnn.deterministic = True +# torch.backends.cudnn.benchmark = True +# torch.multiprocessing.set_sharing_strategy('file_system') + + +def parse_args(): + parse = argparse.ArgumentParser() + parse.add_argument('--config', dest='config', type=str, + default='configs/bisenetv2.py',) + parse.add_argument('--finetune-from', type=str, default=None,) + return parse.parse_args() + +args = parse_args() +cfg = set_cfg_from_file(args.config) + + +def set_model(lb_ignore=255): + logger = logging.getLogger() + net = model_factory[cfg.model_type](cfg.n_cats) + if not args.finetune_from is None: + logger.info(f'load pretrained weights from {args.finetune_from}') + msg = net.load_state_dict(torch.load(args.finetune_from, + map_location='cpu'), strict=False) + logger.info('\tmissing keys: ' + json.dumps(msg.missing_keys)) + logger.info('\tunexpected keys: ' + json.dumps(msg.unexpected_keys)) + if cfg.use_sync_bn: net = nn.SyncBatchNorm.convert_sync_batchnorm(net) + net.cuda() + net.train() + criteria_pre = OhemCELoss(0.7, lb_ignore) + criteria_aux = [OhemCELoss(0.7, lb_ignore) + for _ in range(cfg.num_aux_heads)] + return net, criteria_pre, criteria_aux + + +def set_optimizer(model): + if hasattr(model, 'get_params'): + wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = model.get_params() + # wd_val = cfg.weight_decay + wd_val = 0 + params_list = [ + {'params': wd_params, }, + {'params': nowd_params, 'weight_decay': wd_val}, + {'params': lr_mul_wd_params, 'lr': cfg.lr_start * 10}, + {'params': lr_mul_nowd_params, 'weight_decay': wd_val, 'lr': cfg.lr_start * 10}, + ] + else: + wd_params, non_wd_params = [], [] + for name, param in model.named_parameters(): + if param.dim() == 1: + non_wd_params.append(param) + elif param.dim() == 2 or param.dim() == 4: + wd_params.append(param) + params_list = [ + {'params': wd_params, }, + {'params': non_wd_params, 'weight_decay': 0}, + ] + optim = torch.optim.SGD( + params_list, + lr=cfg.lr_start, + momentum=0.9, + weight_decay=cfg.weight_decay, + ) + return optim + + +def set_model_dist(net): + local_rank = int(os.environ['LOCAL_RANK']) + net = nn.parallel.DistributedDataParallel( + net, + device_ids=[local_rank, ], + # find_unused_parameters=True, + output_device=local_rank + ) + return net + + +def set_meters(): + time_meter = TimeMeter(cfg.max_iter) + loss_meter = AvgMeter('loss') + loss_pre_meter = AvgMeter('loss_prem') + loss_aux_meters = [AvgMeter('loss_aux{}'.format(i)) + for i in range(cfg.num_aux_heads)] + return time_meter, loss_meter, loss_pre_meter, loss_aux_meters + + + +def train(): + logger = logging.getLogger() + + ## dataset + dl = get_data_loader(cfg, mode='train') + + ## model + net, criteria_pre, criteria_aux = set_model(dl.dataset.lb_ignore) + + ## optimizer + optim = set_optimizer(net) + + ## mixed precision training + scaler = amp.GradScaler() + + ## ddp training + net = set_model_dist(net) + + ## meters + time_meter, loss_meter, loss_pre_meter, loss_aux_meters = set_meters() + + ## lr scheduler + lr_schdr = WarmupPolyLrScheduler(optim, power=0.9, + max_iter=cfg.max_iter, warmup_iter=cfg.warmup_iters, + warmup_ratio=0.1, warmup='exp', last_epoch=-1,) + + ## train loop + for it, (im, lb) in enumerate(dl): + im = im.cuda() + lb = lb.cuda() + + lb = torch.squeeze(lb, 1) + + optim.zero_grad() + with amp.autocast(enabled=cfg.use_fp16): + logits, *logits_aux = net(im) + loss_pre = criteria_pre(logits, lb) + loss_aux = [crit(lgt, lb) for crit, lgt in zip(criteria_aux, logits_aux)] + loss = loss_pre + sum(loss_aux) + scaler.scale(loss).backward() + scaler.step(optim) + scaler.update() + torch.cuda.synchronize() + + time_meter.update() + loss_meter.update(loss.item()) + loss_pre_meter.update(loss_pre.item()) + _ = [mter.update(lss.item()) for mter, lss in zip(loss_aux_meters, loss_aux)] + + ## print training log message + if (it + 1) % 100 == 0: + lr = lr_schdr.get_lr() + lr = sum(lr) / len(lr) + print_log_msg( + it, cfg.max_iter, lr, time_meter, loss_meter, + loss_pre_meter, loss_aux_meters) + lr_schdr.step() + + ## dump the final model and evaluate the result + save_pth = osp.join(cfg.respth, 'model_final.pth') + logger.info('\nsave models to {}'.format(save_pth)) + state = net.module.state_dict() + if dist.get_rank() == 0: torch.save(state, save_pth) + + logger.info('\nevaluating the final model') + torch.cuda.empty_cache() + iou_heads, iou_content, f1_heads, f1_content = eval_model(cfg, net.module) + logger.info('\neval results of f1 score metric:') + logger.info('\n' + tabulate(f1_content, headers=f1_heads, tablefmt='orgtbl')) + logger.info('\neval results of miou metric:') + logger.info('\n' + tabulate(iou_content, headers=iou_heads, tablefmt='orgtbl')) + + return + + +def main(): + local_rank = int(os.environ['LOCAL_RANK']) + torch.cuda.set_device(local_rank) + dist.init_process_group(backend='nccl') + + if not osp.exists(cfg.respth): os.makedirs(cfg.respth) + setup_logger(f'{cfg.model_type}-{cfg.dataset.lower()}-train', cfg.respth) + train() + + +if __name__ == "__main__": + main() diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/train.sh b/cv/semantic_segmentation/bisenetv2/pytorch/train.sh new file mode 100755 index 000000000..6a15938c1 --- /dev/null +++ b/cv/semantic_segmentation/bisenetv2/pytorch/train.sh @@ -0,0 +1,7 @@ +# Copyright (c) 2023, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. + +NUM_GPUS=$1 +CONFIG=$2 + +python3 -m torch.distributed.run --nproc_per_node=$NUM_GPUS tools/train_amp.py --config $CONFIG diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/video.mp4 b/cv/semantic_segmentation/bisenetv2/pytorch/video.mp4 new file mode 100755 index 0000000000000000000000000000000000000000..2d838a8deccad15e6ea4465476b5e046e151fcbe GIT binary patch literal 1270170 zcmdSAc|6tK`agW_$;>u{L=mNonUZ9vsLX|uka^CODH)1LWJ-n#4M>qnG|{A@2t}GH z6)8g{O^V{V)_roH{q1+o>Gyrk>-po^=e+N=)_Yy+n%4RZ+qDA#DbLW&K|UdY{s2@U zEkeyD+)dR#NJAB1(L(>gz;FQY^AGoS$Cl+o>jN-h0tLwb|M~eJ1EhZ&FaD?Gzv|Hd z#D_wIT{hsPL+FOTY$Esf=a)9L`}^lNJO9cqB?zO8{cGVY5BJaz>`8d|g@pc6g&K%n z`Y)fwz5LyMTyRdx-~IR4RRCz|u%`a6l}g_3e!;(VK)8>)$7G+hHaHG6atUzv^B^+f zg8n`Mp4gcZ?*ErkR<~!l|6@+VJ=lZDkPn&X2@Cd<`bU3Sdq}9OANF%YLPI7ugvcb4 zO~aOaSatq`2*R+9uuDKb|A)jsXqNv@otVIp3Tic=xWkNzmoU`}+vAO^9Hw|L#_$3E 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zTFqmJ>E@tLGyEHC*ojx#j~$QsyoW_kXn~Nf?bqSc7oFkOnXli7#(tY5wNH}yZhqv` z$m^gpLkk(&ZsS+T7=c_VW7=O+`_^?;nqHD5D$TruQ*DZ#p6q)z;Z3J0jL7)>N3Jrf UyCGVDgOsS^;vLaHMSnd14}a6*SO5S3 literal 0 HcmV?d00001 -- Gitee From 403cfe7891b5af8bac18c2107e5db88dd95e54a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=8E=E4=B8=80=E5=8A=9B?= Date: Fri, 26 Jan 2024 14:13:27 +0800 Subject: [PATCH 2/4] add README --- .../bisenetv2/pytorch/README.md | 171 ++---------------- 1 file changed, 16 insertions(+), 155 deletions(-) mode change 100755 => 100644 cv/semantic_segmentation/bisenetv2/pytorch/README.md diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/README.md b/cv/semantic_segmentation/bisenetv2/pytorch/README.md old mode 100755 new mode 100644 index 602925835..a36051d98 --- a/cv/semantic_segmentation/bisenetv2/pytorch/README.md +++ b/cv/semantic_segmentation/bisenetv2/pytorch/README.md @@ -1,172 +1,33 @@ -# BiSeNetV1 & BiSeNetV2 +# BiSeNet -My implementation of [BiSeNetV1](https://arxiv.org/abs/1808.00897) and [BiSeNetV2](https://arxiv.org/abs/2004.02147). +## Model description +BiSeNet V2 is a two-pathway architecture for real-time semantic segmentation. One pathway is designed to capture the spatial details with wide channels and shallow layers, called Detail Branch. In contrast, the other pathway is introduced to extract the categorical semantics with narrow channels and deep layers, called Semantic Branch. The Semantic Branch simply requires a large receptive field to capture semantic context, while the detail information can be supplied by the Detail Branch. Therefore, the Semantic Branch can be made very lightweight with fewer channels and a fast-downsampling strategy. Both types of feature representation are merged to construct a stronger and more comprehensive feature representation. -mIOUs and fps on cityscapes val set: -| none | ss | ssc | msf | mscf | fps(fp32/fp16/int8) | link | -|------|:--:|:---:|:---:|:----:|:---:|:----:| -| bisenetv1 | 75.44 | 76.94 | 77.45 | 78.86 | 25/78/141 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v1_city_new.pth) | -| bisenetv2 | 74.95 | 75.58 | 76.53 | 77.08 | 26/67/95 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v2_city.pth) | -mIOUs on cocostuff val2017 set: -| none | ss | ssc | msf | mscf | link | -|------|:--:|:---:|:---:|:----:|:----:| -| bisenetv1 | 31.49 | 31.42 | 32.46 | 32.55 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v1_coco_new.pth) | -| bisenetv2 | 30.49 | 30.55 | 31.81 | 31.73 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v2_coco.pth) | +## Step 1: Installing packages -mIOUs on ade20k val set: -| none | ss | ssc | msf | mscf | link | -|------|:--:|:---:|:---:|:----:|:----:| -| bisenetv1 | 36.15 | 36.04 | 37.27 | 36.58 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v1_ade20k.pth) | -| bisenetv2 | 32.53 | 32.43 | 33.23 | 31.72 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v2_ade20k.pth) | - -Tips: - -1. **ss** means single scale evaluation, **ssc** means single scale crop evaluation, **msf** means multi-scale evaluation with flip augment, and **mscf** means multi-scale crop evaluation with flip evaluation. The eval scales and crop size of multi-scales evaluation can be found in [configs](./configs/). - -2. The fps is tested in different way from the paper. For more information, please see [here](./tensorrt). - -3. The authors of bisenetv2 used cocostuff-10k, while I used cocostuff-123k(do not know how to say, just same 118k train and 5k val images as object detection). Thus the results maybe different from paper. - -4. The authors did not report results on ade20k, thus there is no official training settings, here I simply provide a "make it work" result. Maybe the results on ade20k can be boosted with better settings. - -5. The model has a big variance, which means that the results of training for many times would vary within a relatively big margin. For example, if you train bisenetv2 on cityscapes for many times, you will observe that the result of **ss** evaluation of bisenetv2 varies between 73.1-75.1. - - -## deploy trained models - -1. tensorrt -You can go to [tensorrt](./tensorrt) for details. - -2. ncnn -You can go to [ncnn](./ncnn) for details. - -3. openvino -You can go to [openvino](./openvino) for details. - -4. tis -Triton Inference Server(TIS) provides a service solution of deployment. You can go to [tis](./tis) for details. - - -## platform - -My platform is like this: - -* ubuntu 18.04 -* nvidia Tesla T4 gpu, driver 450.80.02 -* cuda 10.2/11.3 -* cudnn 8 -* miniconda python 3.8.8 -* pytorch 1.11.0 - - -## get start - -With a pretrained weight, you can run inference on an single image like this: - -``` -$ python tools/demo.py --config configs/bisenetv2_city.py --weight-path /path/to/your/weights.pth --img-path ./example.png -``` - -This would run inference on the image and save the result image to `./res.jpg`. - -Or you can run inference on a video like this: -``` -$ python tools/demo_video.py --config configs/bisenetv2_coco.py --weight-path res/model_final.pth --input ./video.mp4 --output res.mp4 -``` -This would generate segmentation file as `res.mp4`. If you want to read from camera, you can set `--input camera_id` rather than `input ./video.mp4`. - - -## prepare dataset - -1.cityscapes - -Register and download the dataset from the official [website](https://www.cityscapes-dataset.com/). Then decompress them into the `datasets/cityscapes` directory: -``` -$ mv /path/to/leftImg8bit_trainvaltest.zip datasets/cityscapes -$ mv /path/to/gtFine_trainvaltest.zip datasets/cityscapes -$ cd datasets/cityscapes -$ unzip leftImg8bit_trainvaltest.zip -$ unzip gtFine_trainvaltest.zip -``` - -2.cocostuff - -Download `train2017.zip`, `val2017.zip` and `stuffthingmaps_trainval2017.zip` split from official [website](https://cocodataset.org/#download). Then do as following: +```shell +$ pip3 install -r requirements.txt ``` -$ unzip train2017.zip -$ unzip val2017.zip -$ mv train2017/ /path/to/BiSeNet/datasets/coco/images -$ mv val2017/ /path/to/BiSeNet/datasets/coco/images -$ unzip stuffthingmaps_trainval2017.zip -$ mv train2017/ /path/to/BiSeNet/datasets/coco/labels -$ mv val2017/ /path/to/BiSeNet/datasets/coco/labels +## Step 2: Preparing datasets -$ cd /path/to/BiSeNet -$ python tools/gen_dataset_annos.py --dataset coco -``` +Download cityscapes from [website](https://www.cityscapes-dataset.com/) -3.ade20k +## Step 3: Training -Download `ADEChallengeData2016.zip` from this [website](http://sceneparsing.csail.mit.edu/) and unzip it. Then we can move the uncompressed folders to `datasets/ade20k`, and generate the txt files with the script I prepared for you: ``` -$ unzip ADEChallengeData2016.zip -$ mv ADEChallengeData2016/images /path/to/BiSeNet/datasets/ade20k/ -$ mv ADEChallengeData2016/annotations /path/to/BiSeNet/datasets/ade20k/ -$ python tools/gen_dataset_annos.py --ade20k +$ bash train.sh {num_gpus} configs/bisenetv2_city.py ``` +## Results on BI-V100 -4.custom dataset - -If you want to train on your own dataset, you should generate annotation files first with the format like this: -``` -munster_000002_000019_leftImg8bit.png,munster_000002_000019_gtFine_labelIds.png -frankfurt_000001_079206_leftImg8bit.png,frankfurt_000001_079206_gtFine_labelIds.png -... -``` -Each line is a pair of training sample and ground truth image path, which are separated by a single comma `,`. - -I recommand you to check the information of your dataset with the script: -``` -$ python tools/check_dataset_info.py --im_root /path/to/your/data_root --im_anns /path/to/your/anno_file -``` -This will print some of the information of your dataset. - -Then you need to change the field of `im_root` and `train/val_im_anns` in the config file. I prepared a demo config file for you named [`bisenet_customer.py`](./configs/bisenet_customer.py). You can start from this conig file. - - -## train - -Training commands I used to train the models can be found in [here](./dist_train.sh). - -Note: -1. though `bisenetv2` has fewer flops, it requires much more training iterations. The the training time of `bisenetv1` is shorter. -2. I used overall batch size of 16 to train all models. Since cocostuff has 171 categories, it requires more memory to train models on it. I split the 16 images into more gpus than 2, as I do with cityscapes. - - -## finetune from trained model - -You can also load the trained model weights and finetune from it, like this: -``` -$ export CUDA_VISIBLE_DEVICES=0,1 -$ torchrun --nproc_per_node=2 tools/train_amp.py --finetune-from ./res/model_final.pth --config ./configs/bisenetv2_city.py # or bisenetv1 -``` - - -## eval pretrained models -You can also evaluate a trained model like this: -``` -$ python tools/evaluate.py --config configs/bisenetv1_city.py --weight-path /path/to/your/weight.pth -``` -or you can use multi gpus: -``` -$ torchrun --nproc_per_node=2 tools/evaluate.py --config configs/bisenetv1_city.py --weight-path /path/to/your/weight.pth -``` - +| | ss | ssc | msf | mscf | +| --------- | ----- | ----- | ----- | ----- | +| bisenetv2 | 74.95 | 75.58 | 76.53 | 77.08 | -### Be aware that this is the refactored version of the original codebase. You can go to the `old` directory for original implementation if you need, though I believe you will not need it. +* Reference +https://github.com/CoinCheung/BiSeNet/tree/master -- Gitee From b5a4834762fd3cb57a2f34de35fa4de7a22e0bba Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=8E=E4=B8=80=E5=8A=9B?= Date: Fri, 26 Jan 2024 15:24:57 +0800 Subject: [PATCH 3/4] fix README --- cv/semantic_segmentation/bisenetv2/pytorch/README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/README.md b/cv/semantic_segmentation/bisenetv2/pytorch/README.md index a36051d98..a922d38fc 100644 --- a/cv/semantic_segmentation/bisenetv2/pytorch/README.md +++ b/cv/semantic_segmentation/bisenetv2/pytorch/README.md @@ -29,5 +29,4 @@ $ bash train.sh {num_gpus} configs/bisenetv2_city.py * Reference - -https://github.com/CoinCheung/BiSeNet/tree/master +[BiSeNet](https://github.com/CoinCheung/BiSeNet/tree/master) -- Gitee From a39221f8fd82702fdb6b7251613bb8d0e4ea56f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=8E=E4=B8=80=E5=8A=9B?= Date: Tue, 6 Feb 2024 18:20:40 +0800 Subject: [PATCH 4/4] fix readme and requirement --- .../bisenetv2/pytorch/README.md | 15 +++++++++++++-- .../bisenetv2/pytorch/requirements.txt | 2 +- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/README.md b/cv/semantic_segmentation/bisenetv2/pytorch/README.md index a922d38fc..7c4be8457 100644 --- a/cv/semantic_segmentation/bisenetv2/pytorch/README.md +++ b/cv/semantic_segmentation/bisenetv2/pytorch/README.md @@ -1,4 +1,4 @@ -# BiSeNet +SeNet ## Model description @@ -9,11 +9,21 @@ BiSeNet V2 is a two-pathway architecture for real-time semantic segmentation. On ```shell $ pip3 install -r requirements.txt +$ yum install mesa-libGL -y # if libGL.so.1 error ``` ## Step 2: Preparing datasets +### first Download cityscapes from [website](https://www.cityscapes-dataset.com/) +### second +```bash +$ mv /path/to/leftImg8bit_trainvaltest.zip datasets/cityscapes +$ mv /path/to/gtFine_trainvaltest.zip datasets/cityscapes +$ cd datasets/cityscapes +$ unzip leftImg8bit_trainvaltest.zip +$ unzip gtFine_trainvaltest.zip +``` ## Step 3: Training @@ -29,4 +39,5 @@ $ bash train.sh {num_gpus} configs/bisenetv2_city.py * Reference -[BiSeNet](https://github.com/CoinCheung/BiSeNet/tree/master) +[BiSeNet](https://github.com/CoinCheung/BiSeNet/tree/master) + diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/requirements.txt b/cv/semantic_segmentation/bisenetv2/pytorch/requirements.txt index 819a3d3f8..dc03d8c5b 100755 --- a/cv/semantic_segmentation/bisenetv2/pytorch/requirements.txt +++ b/cv/semantic_segmentation/bisenetv2/pytorch/requirements.txt @@ -1,2 +1,2 @@ tabulate -urllib=1.25.10 +urllib==1.25.10 -- Gitee

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