【论文复现赛第六期】PSANet(含自定义C++外部算子调试经验)

二、网络结构

PSANet网络结构于大部分网络相同，如下图所示，PSANet也使用了辅助损失函数。 

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三、实验结果

官方此次复现的指标应该是参考mmsegmentation复现的结果，要求PSANet-resnet50 输入分辨率512x1024 mIOU=77.24%。
mmsegmentation PSANet参考：https://github.com/open-mmlab/mmsegmentation/tree/master/configs/psanet

四、快速体验

可以按照以下步骤快速体验PSANet，有以下几点说明：
1、PSANet包含有外部C++算子，在目录/home/aistudio/PSANet_paddle/paddleseg/models/ops；
2、运行的环境不要有使用pip安装的paddleseg，如果有需要卸载；（因为本算法未PR到paddleseg，外部C++算子未注册，运行可能会出错）
3、本次复现在单卡训练约50h，未使用多卡任务；（因为脚本任务排队时间长，第一次排上了自定义算子运行出错，所以用aistudio单卡跑完）

# 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

/home/aistudio/PSANet_paddle
Compiling user custom op, it will cost a few seconds.....
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  return (isinstance(seq, collections.Sequence) and
2022-04-25 09:04:34 [INFO]	
---------------Config Information---------------
batch_size: 8
iters: 80000
loss:
  coef:
  - 1
  - 0.4
  types:
  - type: CrossEntropyLoss
  - type: CrossEntropyLoss
lr_scheduler:
  end_lr: 1.0e-05
  learning_rate: 0.01
  power: 0.9
  type: PolynomialDecay
model:
  align_corners: false
  backbone:
    output_stride: 8
    pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz
    type: ResNet50_vd
  enable_auxiliary_loss: true
  mask_h: 59
  mask_w: 59
  normalization_factor: 1.0
  psa_softmax: true
  psa_type: 2
  shrink_factor: 2
  type: PSANet
  use_psa: true
optimizer:
  momentum: 0.9
  type: sgd
  weight_decay: 4.0e-05
train_dataset:
  dataset_root: data/cityscapes
  mode: train
  transforms:
  - max_scale_factor: 2.0
    min_scale_factor: 0.5
    scale_step_size: 0.25
    type: ResizeStepScaling
  - crop_size:
    - 1024
    - 512
    type: RandomPaddingCrop
  - type: RandomHorizontalFlip
  - brightness_range: 0.4
    contrast_range: 0.4
    saturation_range: 0.4
    type: RandomDistort
  - type: Normalize
  type: Cityscapes
val_dataset:
  dataset_root: data/cityscapes
  mode: val
  transforms:
  - type: Normalize
  type: Cityscapes
------------------------------------------------
W0425 09:04:34.999719   952 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0425 09:04:34.999774   952 device_context.cc:465] device: 0, cuDNN Version: 7.6.
2022-04-25 09:04:39 [INFO]	Loading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz
2022-04-25 09:04:40 [INFO]	There are 275/275 variables loaded into ResNet_vd.
2022-04-25 09:04:40 [INFO]	Loading pretrained model from /home/aistudio/model.pdparams
2022-04-25 09:04:40 [INFO]	There are 316/316 variables loaded into PSANet.
2022-04-25 09:04:40 [INFO]	Loaded trained params of model successfully
2022-04-25 09:04:40 [INFO]	Start evaluating (total_samples: 500, total_iters: 500)...
500/500 [==============================] - 143s 287ms/step - batch_cost: 0.2866 - reader cost: 8.4048e-04
2022-04-25 09:07:04 [INFO]	[EVAL] #Images: 500 mIoU: 0.7994 Acc: 0.9637 Kappa: 0.9528 Dice: 0.8825
2022-04-25 09:07:04 [INFO]	[EVAL] Class IoU: 
[0.9839 0.8721 0.9272 0.5406 0.6225 0.6643 0.7219 0.8053 0.9271 0.654
 0.9481 0.8321 0.6427 0.9562 0.8628 0.9078 0.863  0.6689 0.7886]
2022-04-25 09:07:04 [INFO]	[EVAL] Class Precision: 
[0.9934 0.9274 0.9562 0.8691 0.8382 0.8159 0.8432 0.9091 0.9552 0.8596
 0.9646 0.8919 0.8184 0.9741 0.9425 0.9614 0.9633 0.8229 0.8825]
2022-04-25 09:07:04 [INFO]	[EVAL] Class Recall: 
[0.9904 0.936  0.9683 0.5885 0.7075 0.7814 0.8339 0.8758 0.9693 0.7322
 0.9823 0.9255 0.7496 0.9811 0.9107 0.9421 0.8923 0.7814 0.881 ]

# 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'

五、外部C++算子踩坑调试记录

1、仿照repo给的pytorch参考代码，写了外部算子，在cpu前向推理测试算子，与torch输出一致，于是直接训练模型，结果几次迭代后网络输出全部为nan；

2、移除自定义外部算子，网络训练恢复正常，确认问题在自定义算子内；

3、cpu测试，打印反向传播梯度，与torch不一致，仔细核对，发现问题为反向传播梯度不一致；
首先说一下原因：官方给的relu算子示例，他的特征图输出维度和输入维度是相同的，所以定义梯度不需要初始化，因为每个梯度值都会被覆盖（见下方代码）。
但是PSA算子的输入是[mask_h*mask_w, h, w]，输出是[h * w, h, w]，他们维度不同！！！！ 所以如果不对梯度初始化，那么未赋值的梯度值是随机的，导致网络训练奔溃，将梯度初始化为0后解决该问题。（哭死，这里不知道掉了多少头发才发现）

std::vector 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();  auto* grad_out_data = grad_out.data();  auto* grad_x_data = grad_x.mutable_data(x.place());  for (int i = 0; i < out_numel; ++i) {
    grad_x_data[i] =
        grad_out_data[i] * (out_data[i] > static_cast(0) ? 1. : 0.);
  }  return {grad_x};
}

4、cpu算子调试好了后，cuda算子就好写多了，但是需要注意不要有小错误，不然很难发现；（梯度初始化一开始写错了，部分未初始化为0，然后一个个梯度打印出来调试，又是大把的头发）

六、复现经验

1、使用paddleseg套件复现论文，可以赢在起跑线；
2、论文提供的repo不一定没问题（切记这一点，官方的repo模型中有个compact参数，只要设定了就会报错，一开始以为自己写的有问题，后来发现原来官方提供的就有问题，只是它没用到）；
3、写自定义算子一定要仔细核对，最好能够一个个参数前向反向对齐，cpu gpu都确认无误再使用，否则出问题很难debug。