fix some errors and add resnext50 Chinese README

model_zoo/official/cv/mobilenetv2/README.md

@@ -10,7 +10,7 @@
     - [Script and Sample Code](#script-and-sample-code)
         - [Training Process](#training-process)
         - [Evaluation Process](#eval-process)
-        - [Export MindIR](#export-mindir)
+        - [Model Export](#model-export)
 - [Model Description](#model-description)
     - [Performance](#performance)  
         - [Training Performance](#training-performance)
@@ -182,14 +182,14 @@ Inference result will be stored in the example path, you can find result like th
 result: {'acc': 0.71976314102564111} ckpt=./ckpt_0/mobilenet-200_625.ckpt
 ```
 
-## [Export MindIR](#contents)
-
-Change the export mode and export file in `src/config.py`, and run `export.py`.
+## [Model Export](#contents)
 
 ```shell
-python export.py --platform [PLATFORM] --pretrain_ckpt [CKPT_PATH]
+python export.py --platform [PLATFORM] --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT]
 ```
 
+`EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"]
+
 # [Model description](#contents)
 
 ## [Performance](#contents)

model_zoo/official/cv/mobilenetv2/README_CN.md

@@ -17,7 +17,7 @@
         - [用法](#用法-1)
         - [启动](#启动-1)
         - [结果](#结果-1)
-    - [导出MINDIR](#导出MINDIR)
+    - [模型导出](#模型导出)
 - [模型描述](#模型描述)
     - [性能](#性能)
         - [训练性能](#训练性能)
@@ -188,14 +188,14 @@ epoch time:138331.250, per step time:221.330, avg loss:3.917
 result:{'acc':0.71976314102564111} ckpt=./ckpt_0/mobilenet-200_625.ckpt
 ```
 
-## 导出MINDIR
-
-修改`src/config.py`文件中的`export_mode`和`export_file`, 运行`export.py`。
+## 模型导出
 
 ```shell
-python export.py --platform [PLATFORM] --pretrain_ckpt [CKPT_PATH]
+python export.py --platform [PLATFORM] --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT]
 ```
 
+`EXPORT_FORMAT` 可选 ["AIR", "ONNX", "MINDIR"].
+
 # 模型描述
 
 ## 性能

model_zoo/official/cv/mobilenetv2/src/config.py

@@ -41,9 +41,7 @@ def set_config(args):
         "save_checkpoint_path": "./",
         "platform": args.platform,
         "run_distribute": args.run_distribute,
-        "activation": "Softmax",
-        "export_format": "MINDIR",
-        "export_file": "mobilenetv2"
+        "activation": "Softmax"
     })
     config_gpu = ed({
         "num_classes": 1000,
@@ -65,9 +63,7 @@ def set_config(args):
         "save_checkpoint_path": "./",
         "platform": args.platform,
         "run_distribute": args.run_distribute,
-        "activation": "Softmax",
-        "export_format": "MINDIR",
-        "export_file": "mobilenetv2"
+        "activation": "Softmax"
     })
     config_ascend = ed({
         "num_classes": 1000,
@@ -92,9 +88,7 @@ def set_config(args):
         "rank_id": int(os.getenv('RANK_ID', '0')),
         "rank_size": int(os.getenv('RANK_SIZE', '1')),
         "run_distribute": int(os.getenv('RANK_SIZE', '1')) > 1.,
-        "activation": "Softmax",
-        "export_format": "MINDIR",
-        "export_file": "mobilenetv2"
+        "activation": "Softmax"
     })
     config = ed({"CPU": config_cpu,
                  "GPU": config_gpu,

model_zoo/official/cv/resnet/eval.py

@@ -56,7 +56,7 @@ if __name__ == '__main__':
 
     # init context
     context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
-    if target != "GPU":
+    if target == "Ascend":
         device_id = int(os.getenv('DEVICE_ID'))
         context.set_context(device_id=device_id)
 

model_zoo/official/cv/resnext50/README.md

@@ -12,7 +12,7 @@
     - [Script Parameters](#script-parameters)
     - [Training Process](#training-process)
     - [Evaluation Process](#evaluation-process)
-    - [Export MindIR](#export-mindir)
+    - [Model Export](#model-export)
 - [Model Description](#model-description)
     - [Performance](#performance)  
         - [Training Performance](#evaluation-performance)
@@ -206,14 +206,14 @@ acc=78.16%(TOP1)
 acc=93.88%(TOP5)
 ```
 
-## [Export MindIR](#contents)
+## [Model Export](#contents)
 
-Change the export mode and export file in `src/config.py`, and run `export.py`.
-
-```script
-python export.py --platform PLATFORM --pretrained CKPT_PATH
+```shell
+python export.py --device_target [PLATFORM] --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT]
 ```
 
+`EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"]
+
 # [Model description](#contents)
 
 ## [Performance](#contents)

model_zoo/official/cv/resnext50/src/config.py

@@ -41,7 +41,5 @@ config = ed({
     "is_save_on_master": 1,
 
     "rank": 0,
-    "group_size": 1,
-    "export_format": "MINDIR",
-    "export_file": "resnext50"
+    "group_size": 1
 })

model_zoo/official/cv/ssd/src/dataset.py

@@ -35,13 +35,12 @@ def _rand(a=0., b=1.):
     return np.random.rand() * (b - a) + a
 
 
-def get_imageId_from_fileName(filename):
-    """Get imageID from fileName"""
-    try:
-        filename = os.path.splitext(filename)[0]
+def get_imageId_from_fileName(filename, id_iter):
+    """Get imageID from fileName if fileName is int, else return id_iter."""
+    filename = os.path.splitext(filename)[0]
+    if filename.isdigit():
         return int(filename)
-    except:
-        raise NotImplementedError('Filename %s is supposed to be an integer.' % (filename))
+    return id_iter
 
 
 def random_sample_crop(image, boxes):
@@ -185,6 +184,7 @@ def create_voc_label(is_training):
     image_files_dict = {}
     image_anno_dict = {}
     images = []
+    id_iter = 0
     for anno_file in os.listdir(anno_dir):
         print(anno_file)
         if not anno_file.endswith('xml'):
@@ -192,7 +192,8 @@ def create_voc_label(is_training):
         tree = et.parse(os.path.join(anno_dir, anno_file))
         root_node = tree.getroot()
         file_name = root_node.find('filename').text
-        img_id = get_imageId_from_fileName(file_name)
+        img_id = get_imageId_from_fileName(file_name, id_iter)
+        id_iter += 1
         image_path = os.path.join(image_dir, file_name)
         print(image_path)
         if not os.path.isfile(image_path):

model_zoo/research/cv/cycle_gan/src/dataset/datasets.py

@@ -18,6 +18,7 @@ import random
 import numpy as np
 from PIL import Image
 
+random.seed(1)
 IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.tif', '.tiff']
 
 def is_image_file(filename):

model_zoo/research/cv/cycle_gan/src/utils/cityscapes_utils.py

@@ -82,7 +82,7 @@ class CityScapes:
                         'bus', 'train', 'motorcycle', 'bicycle', 'unlabeled']
         self.color_list = []
         for name in self.classes:
-            self.color_list.append(label2color[name].color)
+            self.color_list.append(label2color[name])
         self.class_num = len(self.classes)
 
     def get_id(self, img_path):