!3178 fix pspnet bug

Merge pull request !3178 from 周莉莉/issue

research/cv/PSPNet/README.md

@@ -40,6 +40,15 @@ The pyramid pooling module fuses features under four different pyramid scales.Fo
 - [ADE20K Dataset Website](http://groups.csail.mit.edu/vision/datasets/ADE20K/)
  - It contains 22,210 finely annotated images split into training and testing sets with 20,210 and 2,000 images respectively.
 
+Datasets: attributes (names and colors) are needed, and please download as follows:
+
+- [PASCAL VOC 2012 names.txt and colors.txt Website](https://github.com/hszhao/semseg/tree/master/data/voc2012)
+- [ADE20K names.txt and colors.txt Website](https://github.com/hszhao/semseg/tree/master/data/ade20k)
+
+# [Pretrained model](#contents)
+
+[resnet50-imagenet pretrained model](https://download.mindspore.cn/thirdparty/pspnet/resnet_deepbase.ckpt)
+
 # [Environmental requirements](#Contents)
 
 - Hardware :(Ascend)
@@ -61,10 +70,10 @@ The pyramid pooling module fuses features under four different pyramid scales.Fo
 ├── eval.py                                    # Evaluation python file for ADE20K/VOC2012
 ├── export.py                                  # export mindir
 ├── README.md                                  # descriptions about PSPnet
+├── config                                     # the training config file
+│   ├── ade20k_pspnet50.yaml
+│   └── voc2012_pspnet50.yaml
 ├── src                                        # PSPNet
-│   ├── config                           # the training config file
-│   │   ├── ade20k_pspnet50.yaml
-│   │   └── voc2012_pspnet50.yaml
 │   ├── dataset                          # data processing
 │   │   ├── dataset.py
 │   │   └── transform.py
@@ -89,7 +98,7 @@ The pyramid pooling module fuses features under four different pyramid scales.Fo
 
 ## Script Parameters
 
-Set script parameters in src/config/ade20k_pspnet50.yaml and src/config/voc2012_pspnet50.yaml
+Set script parameters in ./config/ade20k_pspnet50.yaml and ./config/voc2012_pspnet50.yaml
 
 ### Model
 

research/cv/PSPNet/config/ade20k_pspnet50.yaml

 DATA:
-  data_root: /home/HEU_535/PSPNet/data/ADE/
+  data_root: ./data/ADE/
   art_data_root: /cache/data/ADE
-  train_list: /home/HEU_535/PSPNet/data/ADE/training_list.txt
+  train_list: ./data/ADE/training_list.txt
   art_train_list: /cache/data/ADE/training_list.txt
-  val_list: /home/HEU_535/PSPNet/data/ADE/val_list.txt
+  val_list: ./data/ADE/val_list.txt
   art_val_list: /cache/data/ADE/val_list.txt
   classes: 150
   prefix: ADE
-  save_dir: /home/HEU_535/PSPNet/checkpoints/
+  save_dir: ./checkpoints/
   backbone: resnet50
-  pretrain_path: /home/HEU_535/PSPNet/data/resnet_deepbase.ckpt
+  pretrain_path: ./data/resnet_deepbase.ckpt
   art_pretrain_path: /cache/data/ADE/resnet_deepbase.ckpt
-  ckpt: /home/HEU_535/PSPNet/checkpoints/8P/ADE-100_316.ckpt
-  obs_save: obs://harbin-engineering-uni/PSPnet/save_checkpoint/ADE/
+  ckpt: ./checkpoints/8P/ADE-100_316.ckpt
+  obs_save: obs://PSPnet/save_checkpoint/ADE/
 
 TRAIN:
   arch: psp
@@ -40,7 +40,7 @@ TRAIN:
 
 
 TEST:
-  test_list: /home/HEU_535/PSPNet/data/ADE/list/validation.txt
+  test_list: ./data/ADE/list/validation.txt
   split: val  # split in [train, val and test]
   base_size: 512  # based size for scaling
   test_h: 473
@@ -48,6 +48,6 @@ TEST:
   scales: [1.0]  # evaluation scales, ms as [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
   index_start: 0  # evaluation start index in list
   index_step: 0  # evaluation step index in list, 0 means to end
-  result_path: /home/HEU_535/PSPNet/result/ade/
-  color_txt: /home/HEU_535/PSPNet/config/ade20k/ade20k_colors.txt
-  name_txt: /home/HEU_535/PSPNet/config/ade20k/ade20k_names.txt
+  result_path: ./result/ade/
+  color_txt: ./ade20k/ade20k_colors.txt
+  name_txt: ./ade20k/ade20k_names.txt

research/cv/PSPNet/config/voc2012_pspnet50.yaml

 DATA:
-  data_root: /home/HEU_535/PSPNet/data/voc/voc/
+  data_root: ./data/voc/voc/
   art_data_root: /cache/data
-  train_list: /home/HEU_535/PSPNet/data/voc/voc/train_list.txt
+  train_list: ./data/voc/voc/train_list.txt
   art_train_list: /cache/data/train_list.txt
-  val_list: /home/HEU_535/PSPNet/data/voc/voc/val_list.txt
+  val_list: ./data/voc/voc/val_list.txt
   art_val_list: /cache/data/val_list.txt
   classes: 21
   prefix: voc
-  save_dir: /home/HEU_535/PSPNet/checkpoints/
+  save_dir: ./checkpoints/
   backbone: resnet50
-  pretrain_path: /home/HEU_535/PSPNet/data/resnet_deepbase.ckpt
+  pretrain_path: ./data/resnet_deepbase.ckpt
   art_pretrain_path: /cache/data/resnet_deepbase.ckpt
-  ckpt: /home/HEU_535/PSPNet/checkpoints/8P/voc-50_133.ckpt
-  obs_save: obs://harbin-engineering-uni/PSPnet/save_checkpoint/voc/
+  ckpt: ./checkpoints/8P/voc-50_133.ckpt
+  obs_save: obs://PSPnet/save_checkpoint/voc/
 
 TRAIN:
   arch: psp
@@ -40,7 +40,7 @@ TRAIN:
 
 
 TEST:
-  test_list: /home/HEU_535/PSPNet/dataset/voc2012/list/val.txt
+  test_list: ./dataset/voc2012/list/val.txt
   split: val  # split in [train, val and test]
   base_size: 512  # based size for scaling
   test_h: 473
@@ -48,6 +48,6 @@ TEST:
   scales: [1.0]  # evaluation scales, ms as [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
   index_start: 0  # evaluation start index in list
   index_step: 0  # evaluation step index in list, 0 means to end
-  result_path: /home/HEU_535/PSPNet/result/voc/
-  color_txt: /home/HEU_535/PSPNet/config/voc2012/voc2012_colors.txt
-  name_txt: /home/HEU_535/PSPNet/config/voc2012/voc2012_names.txt
+  result_path: ./result/voc/
+  color_txt: ./voc2012/voc2012_colors.txt
+  name_txt: ./voc2012/voc2012_names.txt

research/cv/PSPNet/eval.py

@@ -39,13 +39,13 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
 def get_parser():
     """
     Read parameter file
-        -> for ADE20k: ./src/config/voc2012_pspnet50.yaml
-        -> for voc2012: ./src/config/voc2012_pspnet50.yaml
+        -> for ADE20k: ./config/ade20k_pspnet50.yaml
+        -> for voc2012: ./config/voc2012_pspnet50.yaml
     """
     parser = argparse.ArgumentParser(description='MindSpore Semantic Segmentation')
-    parser.add_argument('--config', type=str, required=True, default='./src/config/voc2012_pspnet50.yaml',
+    parser.add_argument('--config', type=str, required=True, default='./config/voc2012_pspnet50.yaml',
                         help='config file')
-    parser.add_argument('opts', help='see ./src/config/voc2012_pspnet50.yaml for all options', default=None,
+    parser.add_argument('opts', help='see ./config/voc2012_pspnet50.yaml for all options', default=None,
                         nargs=argparse.REMAINDER)
     args_ = parser.parse_args()
     assert args_.config is not None

research/cv/PSPNet/export.py

@@ -23,7 +23,7 @@ from mindspore import Tensor, context, load_checkpoint, load_param_into_net, exp
 parser = argparse.ArgumentParser(description='maskrcnn export')
 parser.add_argument("--device_id", type=int, default=0, help="Device id")
 parser.add_argument("--batch_size", type=int, default=1, help="batch size")
-parser.add_argument("--yaml_path", type=str, required=True, default='./src/config/voc2012_pspnet50.yaml',
+parser.add_argument("--yaml_path", type=str, required=True, default='./config/voc2012_pspnet50.yaml',
                     help='yaml file path')
 parser.add_argument("--ckpt_file", type=str, required=True, default='./checkpoints/voc/ADE-50_1063.ckpt',
                     help="Checkpoint file path.")

research/cv/PSPNet/src/config/ade20k_pspnet50.yaml

-DATA:
-  data_root: ./data/ADE/
-  train_list: ./data/ADE/training_list.txt
-  val_list: ./data/ADE/val_list.txt  #   test_list: dataset/ade20k/list/validation.txt
-  classes: 150
-  prefix: ADE
-  save_dir: ./checkpoints/
-  backbone: resnet50
-  pretrain_path: ./data/resnet_deepbase.ckpt
-  ckpt: ./checkpoints/ade/ADE_1-100_2527.ckpt
-
-TRAIN:
-  arch: psp
-  feature_size: 60
-  train_h: 473
-  train_w: 473
-  scale_min: 0.5  # minimum random scale
-  scale_max: 2.0  # maximum random scale
-  rotate_min: -10  # minimum random rotate
-  rotate_max: 10  # maximum random rotate
-  zoom_factor: 8  # zoom factor for final prediction during training, be in [1, 2, 4, 8]
-  ignore_label: 255
-  aux_weight: 0.4
-  data_name: ade
-  batch_size: 8  # batch size for training
-  batch_size_val: 8  # batch size for validation during training
-  base_lr: 0.005
-  epochs: 100
-  start_epoch: 0
-  power: 0.9
-  momentum: 0.9
-  weight_decay: 0.0001
-
-
-TEST:
-  test_list: ./dataset/ade20k/list/validation.txt
-  split: val  # split in [train, val and test]
-  base_size: 512  # based size for scaling
-  test_h: 473
-  test_w: 473
-  scales: [1.0]  # evaluation scales, ms as [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
-  index_start: 0  # evaluation start index in list
-  index_step: 0  # evaluation step index in list, 0 means to end
-  result_path: ./result/ade/
-  color_txt: ./src/config/ade20k/ade20k_colors.txt
-  name_txt: ./src/config/ade20k/ade20k_names.txt
-

research/cv/PSPNet/src/config/voc2012_pspnet50.yaml

-DATA:
-  data_root: /home/HEU_535/PSPNet/data/voc/voc/
-  train_list: /home/HEU_535/PSPNet/data/voc/voc/train_list.txt
-  val_list: /home/HEU_535/PSPNet/data/voc/voc/val_list.txt
-  classes: 21
-  prefix: voc
-  save_dir: /home/HEU_535/PSPNet/checkpoints/
-  backbone: resnet50
-  pretrain_path: /home/HEU_535/PSPNet/data/resnet_deepbase.ckpt
-  ckpt: /home/HEU_535/PSPNet/checkpoints/8P/voc-50_133.ckpt
-
-TRAIN:
-  arch: psp
-  feature_size: 60
-  train_h: 473
-  train_w: 473
-  scale_min: 0.5  # minimum random scale
-  scale_max: 2.0  # maximum random scale
-  rotate_min: -10  # minimum random rotate
-  rotate_max: 10  # maximum random rotate
-  zoom_factor: 8  # zoom factor for final prediction during training, be in [1, 2, 4, 8]
-  ignore_label: 255
-  aux_weight: 0.4
-  data_name:
-  batch_size: 8  # batch size for training
-  batch_size_val: 8  # batch size for validation during training, memory and speed tradeoff
-  base_lr: 0.005
-  epochs: 50
-  start_epoch: 0
-  power: 0.9
-  momentum: 0.9
-  weight_decay: 0.0001
-
-
-TEST:
-  test_list: /home/HEU_535/PSPNet/dataset/voc2012/list/val.txt
-  split: val  # split in [train, val and test]
-  base_size: 512  # based size for scaling
-  test_h: 473
-  test_w: 473
-  scales: [1.0]  # evaluation scales, ms as [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
-  index_start: 0  # evaluation start index in list
-  index_step: 0  # evaluation step index in list, 0 means to end
-  result_path: /home/HEU_535/PSPNet/result/voc/
-  color_txt: /home/HEU_535/PSPNet/config/voc2012/voc2012_colors.txt
-  name_txt: /home/HEU_535/PSPNet/config/voc2012/voc2012_names.txt

research/cv/PSPNet/src/model/cell.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """ PSPNet loss function """
 from mindspore import nn
-from src.utils.metrics import SoftmaxCrossEntropyLoss
+from src.utils.aux_loss import SoftmaxCrossEntropyLoss
 
 
 class Aux_CELoss_Cell(nn.Cell):

research/cv/PSPNet/src/utils/metric_and_evalcallback.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """ eval_callback """
 from mindspore.nn.metrics.metric import Metric
-from src.utils.metrics import SoftmaxCrossEntropyLoss
+from src.utils.aux_loss import SoftmaxCrossEntropyLoss
 
 
 class pspnet_metric(Metric):

research/cv/PSPNet/train.py

@@ -48,8 +48,8 @@ Model_Art = False
 def get_parser():
     """
     Read parameter file
-        -> for ADE20k: ./src/config/voc2012_pspnet50.yaml
-        -> for voc2012: ./src/config/voc2012_pspnet50.yaml
+        -> for ADE20k: ./config/ade20k_pspnet50.yaml
+        -> for voc2012: ./config/voc2012_pspnet50.yaml
     """
     global Model_Art
     parser = argparse.ArgumentParser(description='MindSpore Semantic Segmentation')
@@ -63,7 +63,7 @@ def get_parser():
                         help='Location of training outputs.')
     parser.add_argument('--dataset_name', type=str, default='',
                         help='aux parameter for ModelArt')
-    parser.add_argument('opts', help='see ./src/config/voc2012_pspnet50.yaml for all options', default=None,
+    parser.add_argument('opts', help='see ./config/voc2012_pspnet50.yaml for all options', default=None,
                         nargs=argparse.REMAINDER)
     args_ = parser.parse_args()
     if args_.model_art:
@@ -253,4 +253,4 @@ def psp_train():
 if __name__ == "__main__":
     args = get_parser()
     print(args.obs_save)
-    # psp_train()
+    psp_train()