【论文复现赛第六期】PSANet(含自定义C++外部算子调试经验)

# step 1: clone    # 可跳过# %cd ~/# !git clone https://gitee.com/dudulang001/PSANet_paddle.git# %cd PSANet_paddle# !git pull

# step 2: 卸载paddleseg   防止后续自定义外部算子未注册导致运行出错## 务必卸载paddleseg!pip uninstall paddleseg

# step 3: unzip data%cd ~/PSANet_paddle/
!mkdir data
!tar -xf ~/data/data64550/cityscapes.tar -C data/
%cd ~/

# step 4: 训练%cd ~/PSANet_paddle
!python train.py --config configs/psanet/psanet_resnet50_os8_cityscapes_1024x512_80k.yml \
     --use_vdl --log_iter 10 --save_interval 100 --save_dir output # --do_eval

# step 5: val%cd ~/PSANet_paddle/
!python val.py \
       --config configs/psanet/psanet_resnet50_os8_cityscapes_1024x512_80k.yml \
       --model_path ~/model.pdparams

# step 6: val flip%cd ~/PSANet_paddle/
!python val.py \
       --config configs/psanet/psanet_resnet50_os8_cityscapes_1024x512_80k.yml \
       --model_path ~/model.pdparams \
       --aug_eval \
       --flip_horizontal

# step 7: val ms flip %cd ~/PSANet_paddle/
!python val.py \
       --config configs/psanet/psanet_resnet50_os8_cityscapes_1024x512_80k.yml \
       --model_path ~/model.pdparams \
       --aug_eval \
       --scales 0.75 1.0 1.25 \
       --flip_horizontal

# step 8: 预测， 预测结果在~/PaddleSeg/output/result文件夹内%cd ~/PSANet_paddle/
!python predict.py \
       --config configs/psanet/psanet_resnet50_os8_cityscapes_1024x512_80k.yml \
       --model_path ~/model.pdparams \
       --image_path data/cityscapes/leftImg8bit/val/frankfurt/frankfurt_000000_000294_leftImg8bit.png \
       --save_dir output/result

# 查看预测结果import cv2import matplotlib.pyplot as plt
image_path = "/home/aistudio/PSANet_paddle/output/result/added_prediction/frankfurt_000000_000294_leftImg8bit.png"image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

plt.imshow(image)
plt.show()

# step 9: export   # 最好不要导出，自定义的外部算子目前在静态图推理有bug，issue:  https://github.com/PaddlePaddle/Paddle/issues/42068%cd ~/PSANet_paddle
!python export.py \
       --config configs/psanet/psanet_resnet50_os8_cityscapes_1024x512_80k.yml \
       --model_path ~/model.pdparams \
       --save_dir output --input_shape 1 3 512 1024

# step 10: infer         # 静态图推理，目前有bug，参考上一步issue%cd ~/PSANet_paddle
!python deploy/python/infer.py \
    --config output/deploy.yaml \
    --image_path ~/test.png \
    --save_dir output/infer/

## 静态图预测异常import cv2import matplotlib.pyplot as plt
image_path = "/home/aistudio/PSANet_paddle/output/infer/test.png"image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

plt.imshow(image)
plt.show()

# step 11: test tipc       准备数据# test tipc 1: prepare data%cd ~/PSANet_paddle/
!bash test_tipc/prepare.sh ./test_tipc/configs/psanet/train_infer_python.txt 'lite_train_lite_infer'

# step 12: test tipc       # test tipc 2: pip install requirements%cd ~/PSANet_paddle/test_tipc/
!pip install -r requirements.txt

# step 13: test tipc       # test tipc 3: 安装auto_log%cd ~/# !git clone https://github.com/LDOUBLEV/AutoLog           %cd AutoLog/
!pip3 install -r requirements.txt
!python3 setup.py bdist_wheel
!pip3 install ./dist/auto_log-1.2.0-py3-none-any.whl

# step 14: test tipc       这里需要注意，自定义的外部算子导出时需要给定维度，否则会导致维度丢失，参考train_infer_python.txt# test tipc 4: test train inference%cd ~/PSANet_paddle/
!bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/psanet/train_infer_python.txt 'lite_train_lite_infer'

std::vector<paddle::Tensor> ReluCPUBackward(const paddle::Tensor& x,                                            const paddle::Tensor& out,                                            const paddle::Tensor& grad_out) {  CHECK_INPUT(x);  CHECK_INPUT(out);  CHECK_INPUT(grad_out);  auto grad_x = paddle::Tensor(paddle::PlaceType::kCPU, x.shape());   # 看这里  auto out_numel = out.size();  auto* out_data = out.data<float>();  auto* grad_out_data = grad_out.data<float>();  auto* grad_x_data = grad_x.mutable_data<float>(x.place());  for (int i = 0; i < out_numel; ++i) {
    grad_x_data[i] =
        grad_out_data[i] * (out_data[i] > static_cast<float>(0) ? 1. : 0.);
  }  return {grad_x};
}