!10924 deeplabv3 bugfix: 1. add CPU training usage in readme file; 2. add dataset list file genrate script.

From: @caojian05
Reviewed-by: @wuxuejian,@oacjiewen
Signed-off-by: @wuxuejian

model_zoo/official/cv/deeplabv3/README.md

@@ -50,6 +50,8 @@ JPEGImages/00004.jpg SegmentationClassGray/00004.png
 ......
 ```
 
+You can also generate the list file automatically by run script: `python get_dataset_lst.py --data_root=/PATH/TO/DATA`
+
 - Configure and run build_data.sh to convert dataset to mindrecords. Arguments in scripts/build_data.sh:
 
  ```shell
@@ -164,10 +166,12 @@ run_eval_s8_multiscale_flip.sh
     ├── run_eval_s8_multiscale.sh                 # launch ascend evaluation with multiscale in s8 structure
     ├── run_eval_s8_multiscale_filp.sh            # launch ascend evaluation with multiscale and filp in s8 structure
     ├── run_standalone_train.sh                   # launch ascend standalone training(1 pc)
+    ├── run_standalone_train_cpu.sh               # launch CPU standalone training
   ├── src
     ├── data
         ├── dataset.py                            # mindrecord data generator
         ├── build_seg_data.py                     # data preprocessing
+        ├── get_dataset_lst.py                    # dataset list file generator
     ├── loss
        ├── loss.py                                # loss definition for deeplabv3
     ├── nets
@@ -189,6 +193,7 @@ Default configuration
 
 ```shell
 "data_file":"/PATH/TO/MINDRECORD_NAME"            # dataset path
+"device_target":Ascend                            # device target
 "train_epochs":300                                # total epochs
 "batch_size":32                                   # batch size of input tensor
 "crop_size":513                                   # crop size
@@ -238,7 +243,7 @@ For 8 devices training, training steps are as follows:
 
 1. Train s16 with vocaug dataset, finetuning from resnet101 pretrained model, script is as follows:
 
-```python
+```shell
 # run_distribute_train_s16_r1.sh
 for((i=0;i<=$RANK_SIZE-1;i++));
 do
@@ -328,8 +333,34 @@ do
 done
 ```
 
+#### Running on CPU
+
+For CPU training, please config parameters, training script is as follows:
+
+```shell
+# run_standalone_train_cpu.sh
+python ${train_code_path}/train.py --data_file=/PATH/TO/MINDRECORD_NAME  \
+                    --device_target=CPU  \
+                    --train_dir=${train_path}/ckpt  \
+                    --train_epochs=200  \
+                    --batch_size=32  \
+                    --crop_size=513  \
+                    --base_lr=0.015  \
+                    --lr_type=cos  \
+                    --min_scale=0.5  \
+                    --max_scale=2.0  \
+                    --ignore_label=255  \
+                    --num_classes=21  \
+                    --model=deeplab_v3_s16  \
+                    --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL  \
+                    --save_steps=1500  \
+                    --keep_checkpoint_max=200 >log 2>&1 &
+```
+
 ### Result
 
+#### Running on Ascend
+
 - Training vocaug in s16 structure
 
 ```shell
@@ -386,6 +417,17 @@ epoch time: 5962.164 ms, per step time: 542.015 ms
 ...
 ```
 
+#### Running on CPU
+
+- Training voctrain in s16 structure
+
+```bash
+epoch: 1 step: 1, loss is 3.655448
+epoch: 2 step: 1, loss is 1.5531876
+epoch: 3 step: 1, loss is 1.5099041
+...
+```
+
 ## [Evaluation Process](#contents)
 
 ### Usage
@@ -438,7 +480,7 @@ Note: There OS is output stride, and MS is multiscale.
 
 # [Model Description](#contents)
 
-## [Performance](#contents
+## [Performance](#contents)
 
 ### Evaluation Performance
 

model_zoo/official/cv/deeplabv3/README_CN.md

@@ -63,6 +63,8 @@ Pascal VOC数据集和语义边界数据集（Semantic Boundaries Dataset，SBD
      ......
      ```
 
+你也可以通过运行脚本：`python get_dataset_lst.py --data_root=/PATH/TO/DATA` 来自动生成数据清单文件。
+
 - 配置并运行build_data.sh，将数据集转换为MindRecords。scripts/build_data.sh中的参数：
 
      ```
@@ -177,10 +179,12 @@ run_eval_s8_multiscale_flip.sh
     ├── run_eval_s8_multiscale.sh                 # 使用多尺度s8结构启动Ascend评估
     ├── run_eval_s8_multiscale_filp.sh            # 使用多尺度和翻转s8结构启动Ascend评估
     ├── run_standalone_train.sh                   # 启动Ascend单机训练（单卡）
+    ├── run_standalone_train_cpu.sh               # 启动CPU单机训练
   ├── src
     ├── data
         ├── dataset.py                            # 生成MindRecord数据
         ├── build_seg_data.py                     # 数据预处理
+        ├── get_dataset_lst.py                    # 生成数据清单文件
     ├── loss
        ├── loss.py                                # DeepLabV3的损失定义
     ├── nets
@@ -202,6 +206,7 @@ run_eval_s8_multiscale_flip.sh
 
 ```bash
 "data_file":"/PATH/TO/MINDRECORD_NAME"            # 数据集路径
+"device_target":Ascend                            # 训练后端类型
 "train_epochs":300                                # 总轮次数
 "batch_size":32                                   # 输入张量的批次大小
 "crop_size":513                                   # 裁剪大小
@@ -342,8 +347,34 @@ do
 done
 ```
 
+#### CPU环境运行
+
+按以下样例配置训练参数，运行CPU训练脚本：
+
+```shell
+# run_standalone_train_cpu.sh
+python ${train_code_path}/train.py --data_file=/PATH/TO/MINDRECORD_NAME  \
+                    --device_target=CPU  \
+                    --train_dir=${train_path}/ckpt  \
+                    --train_epochs=200  \
+                    --batch_size=32  \
+                    --crop_size=513  \
+                    --base_lr=0.015  \
+                    --lr_type=cos  \
+                    --min_scale=0.5  \
+                    --max_scale=2.0  \
+                    --ignore_label=255  \
+                    --num_classes=21  \
+                    --model=deeplab_v3_s16  \
+                    --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL  \
+                    --save_steps=1500  \
+                    --keep_checkpoint_max=200 >log 2>&1 &
+```
+
 ### 结果
 
+#### Ascend处理器环境运行
+
 - 使用s16结构训练VOCaug
 
 ```bash
@@ -400,6 +431,17 @@ Epoch time: 5962.164, per step time: 542.015
 ...
 ```
 
+#### CPU环境运行
+
+- 使用s16结构训练VOCtrain
+
+```bash
+epoch: 1 step: 1, loss is 3.655448
+epoch: 2 step: 1, loss is 1.5531876
+epoch: 3 step: 1, loss is 1.5099041
+...
+```
+
 ## 评估过程
 
 ### 用法
@@ -470,7 +512,7 @@ python ${train_code_path}/eval.py --data_root=/PATH/TO/DATA  \
 | 损失 | 0.0065883575 |
 | 速度 | 31毫秒/步（单卡，s8）<br> 234毫秒/步（8卡，s8） |  
 | 微调检查点 | 443M （.ckpt文件） |
-| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/office/cv/deeplabv3) |
+| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeplabv3) |
 
 # 随机情况说明
 

model_zoo/official/cv/deeplabv3/src/data/get_dataset_lst.py

@@ -0,0 +1,142 @@
+import argparse
+import os
+
+import numpy as np
+import scipy.io
+from PIL import Image
+
+parser = argparse.ArgumentParser('dataset list generator')
+parser.add_argument("--data_dir", type=str, default='./', help='where dataset stored.')
+
+args, _ = parser.parse_known_args()
+
+data_dir = args.data_dir
+print("Data dir is:", data_dir)
+
+#
+VOC_IMG_DIR = os.path.join(data_dir, 'VOCdevkit/VOC2012/JPEGImages')
+VOC_ANNO_DIR = os.path.join(data_dir, 'VOCdevkit/VOC2012/SegmentationClass')
+VOC_ANNO_GRAY_DIR = os.path.join(data_dir, 'VOCdevkit/VOC2012/SegmentationClassGray')
+VOC_TRAIN_TXT = os.path.join(data_dir, 'VOCdevkit/VOC2012/ImageSets/Segmentation/train.txt')
+VOC_VAL_TXT = os.path.join(data_dir, 'VOCdevkit/VOC2012/ImageSets/Segmentation/val.txt')
+
+SBD_ANNO_DIR = os.path.join(data_dir, 'benchmark_RELEASE/dataset/cls')
+SBD_IMG_DIR = os.path.join(data_dir, 'benchmark_RELEASE/dataset/img')
+SBD_ANNO_PNG_DIR = os.path.join(data_dir, 'benchmark_RELEASE/dataset/cls_png')
+SBD_ANNO_GRAY_DIR = os.path.join(data_dir, 'benchmark_RELEASE/dataset/cls_png_gray')
+SBD_TRAIN_TXT = os.path.join(data_dir, 'benchmark_RELEASE/dataset/train.txt')
+SBD_VAL_TXT = os.path.join(data_dir, 'benchmark_RELEASE/dataset/val.txt')
+
+VOC_TRAIN_LST_TXT = os.path.join(data_dir, 'voc_train_lst.txt')
+VOC_VAL_LST_TXT = os.path.join(data_dir, 'voc_val_lst.txt')
+VOC_AUG_TRAIN_LST_TXT = os.path.join(data_dir, 'vocaug_train_lst.txt')
+
+
+def __get_data_list(data_list_file):
+    with open(data_list_file, mode='r') as f:
+        return f.readlines()
+
+
+def conv_voc_colorpng_to_graypng():
+    if not os.path.exists(VOC_ANNO_GRAY_DIR):
+        os.makedirs(VOC_ANNO_GRAY_DIR)
+
+    for ann in os.listdir(VOC_ANNO_DIR):
+        ann_im = Image.open(os.path.join(VOC_ANNO_DIR, ann))
+        ann_im = Image.fromarray(np.array(ann_im))
+        ann_im.save(os.path.join(VOC_ANNO_GRAY_DIR, ann))
+
+
+def __gen_palette(cls_nums=256):
+    palette = np.zeros((cls_nums, 3), dtype=np.uint8)
+    for i in range(cls_nums):
+        lbl = i
+        j = 0
+        while lbl:
+            palette[i, 0] |= (((lbl >> 0) & 1) << (7 - j))
+            palette[i, 1] |= (((lbl >> 1) & 1) << (7 - j))
+            palette[i, 2] |= (((lbl >> 2) & 1) << (7 - j))
+            lbl >>= 3
+            j += 1
+    return palette.flatten()
+
+
+def conv_sbd_mat_to_png():
+    if not os.path.exists(SBD_ANNO_PNG_DIR):
+        os.makedirs(SBD_ANNO_PNG_DIR)
+    if not os.path.exists(SBD_ANNO_GRAY_DIR):
+        os.makedirs(SBD_ANNO_GRAY_DIR)
+
+    palette = __gen_palette()
+    for an in os.listdir(SBD_ANNO_DIR):
+        img_id = an[:-4]
+        mat = scipy.io.loadmat(os.path.join(SBD_ANNO_DIR, an))
+        anno = mat['GTcls'][0]['Segmentation'][0].astype(np.uint8)
+        anno_png = Image.fromarray(anno)
+        # save to gray png
+        anno_png.save(os.path.join(SBD_ANNO_GRAY_DIR, img_id + '.png'))
+        # save to color png use palette
+        anno_png.putpalette(palette)
+        anno_png.save(os.path.join(SBD_ANNO_PNG_DIR, img_id + '.png'))
+
+
+def create_voc_train_lst_txt():
+    voc_train_data_lst = __get_data_list(VOC_TRAIN_TXT)
+    with open(VOC_TRAIN_LST_TXT, mode='w') as f:
+        for id_ in voc_train_data_lst:
+            id_ = id_.strip()
+            img_ = os.path.join(VOC_IMG_DIR, id_ + '.jpg')
+            anno_ = os.path.join(VOC_ANNO_GRAY_DIR, id_ + '.png')
+            f.write(img_ + ' ' + anno_ + '\n')
+
+
+def create_voc_val_lst_txt():
+    voc_val_data_lst = __get_data_list(VOC_VAL_TXT)
+    with open(VOC_VAL_LST_TXT, mode='w') as f:
+        for id_ in voc_val_data_lst:
+            id_ = id_.strip()
+            img_ = os.path.join(VOC_IMG_DIR, id_ + '.jpg')
+            anno_ = os.path.join(VOC_ANNO_GRAY_DIR, id_ + '.png')
+            f.write(img_ + ' ' + anno_ + '\n')
+
+
+def create_voc_train_aug_lst_txt():
+    voc_train_data_lst = __get_data_list(VOC_TRAIN_TXT)
+    voc_val_data_lst = __get_data_list(VOC_VAL_TXT)
+
+    sbd_train_data_lst = __get_data_list(SBD_TRAIN_TXT)
+    sbd_val_data_lst = __get_data_list(SBD_VAL_TXT)
+
+    with open(VOC_AUG_TRAIN_LST_TXT, mode='w') as f:
+        for id_ in sbd_train_data_lst + sbd_val_data_lst:
+            if id_ in voc_train_data_lst + voc_val_data_lst:
+                continue
+            id_ = id_.strip()
+            img_ = os.path.join(SBD_ANNO_DIR, id_ + '.jpg')
+            anno_ = os.path.join(SBD_ANNO_GRAY_DIR, id_ + '.png')
+            f.write(img_ + ' ' + anno_ + '\n')
+
+        for id_ in voc_train_data_lst:
+            id_ = id_.strip()
+            img_ = os.path.join(VOC_IMG_DIR, id_ + '.jpg')
+            anno_ = os.path.join(VOC_ANNO_GRAY_DIR, id_ + '.png')
+            f.write(img_ + ' ' + anno_ + '\n')
+
+
+if __name__ == '__main__':
+    print('converting voc color png to gray png ...')
+    conv_voc_colorpng_to_graypng()
+    print('converting done.')
+
+    create_voc_train_lst_txt()
+    print('generating voc train list success.')
+
+    create_voc_val_lst_txt()
+    print('generating voc val list success.')
+
+    print('converting sbd annotations to png ...')
+    conv_sbd_mat_to_png()
+    print('converting done')
+
+    create_voc_train_aug_lst_txt()
+    print('generating voc train aug list success.')