modify Resnet50-quant data_load_mode value

model_zoo/official/cv/resnet50_quant/README.md

@@ -106,7 +106,7 @@ Parameters for both training and evaluation can be set in config.py
   'weight_decay': 1e-4          # weight decay value
   'epoch_size': 120             # total training epochs
   'pretrained_epoch_size': 90   # pretraining epochs of resnet50, which is unquantative network of resnet50_quant
-  'data_load_mode': 'mindata'   # the style of loading data into device
+  'data_load_mode': 'original'  # the style of loading data into device, support 'original' or 'mindrecord'
   'save_checkpoint':True        # whether save checkpoint file after training finish
   'save_checkpoint_epochs': 1   # the step from which start to save checkpoint file.
   'keep_checkpoint_max': 50     # only keep the last keep_checkpoint_max checkpoint

model_zoo/official/cv/resnet50_quant/README_CN.md

@@ -109,7 +109,7 @@ ResNet-50总体网络架构如下：
   'weight_decay'：1e-4      # 权重衰减值
   'epoch_size'：120         # 训练轮次数
   'pretrained_epoch_size'：90   # 非量化网络预训练轮次数
-  'data_load_mode': 'mindata' # 数据加载模式
+  'data_load_mode': 'original'  # 数据加载模式，支持'original'和'mindrecord'
   'save_checkpoint'：True    # 训练结束后是否保存检查点文件
   "save_checkpoint_epochs": 1 # 开始保存检查点文件的步骤
   'keep_checkpoint_max'：50  # 只保留最后一个keep_checkpoint_max检查点

model_zoo/official/cv/resnet50_quant/src/config.py

@@ -24,7 +24,7 @@ config_quant = ed({
     "weight_decay": 1e-4,
     "epoch_size": 120,
     "pretrained_epoch_size": 90,
-    "data_load_mode": "mindata",
+    "data_load_mode": "original",
     "save_checkpoint": True,
     "save_checkpoint_epochs": 1,
     "keep_checkpoint_max": 50,

tests/st/quantization/resnet50_quant/test_resnet50_quant.py

@@ -43,7 +43,7 @@ config_quant = ed({
     "buffer_size": 1000,
     "image_height": 224,
     "image_width": 224,
-    "data_load_mode": "mindata",
+    "data_load_mode": "original",
     "save_checkpoint": True,
     "save_checkpoint_epochs": 1,
     "keep_checkpoint_max": 50,