recitify learning rate generating policy

model_zoo/official/cv/resnet/README.md

@@ -73,6 +73,7 @@ Parameters for both training and evaluation can be set in config.py.
 "momentum": 0.9,                  # momentum
 "weight_decay": 1e-4,             # weight decay 
 "epoch_size": 90,                 # only valid for taining, which is always 1 for inference 
+"pretrain_epoch_size": 0,         # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
 "save_checkpoint": True,          # whether save checkpoint or not
 "save_checkpoint_steps": 195,     # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
 "keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
@@ -93,7 +94,7 @@ Parameters for both training and evaluation can be set in config.py.
 "momentum": 0.9,                  # momentum optimizer
 "weight_decay": 1e-4,             # weight decay 
 "epoch_size": 90,                 # only valid for taining, which is always 1 for inference 
-"pretrained_epoch_size": 1,       # epoch size that model has been trained before load pretrained checkpoint
+"pretrain_epoch_size": 0,         # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
 "save_checkpoint": True,          # whether save checkpoint or not
 "save_checkpoint_epochs": 1,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
 "keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
@@ -114,8 +115,8 @@ Parameters for both training and evaluation can be set in config.py.
 "loss_scale": 1024,               # loss scale
 "momentum": 0.9,                  # momentum optimizer
 "weight_decay": 1e-4,             # weight decay
-"epoch_size": 120,                # epoch sizes for training
-"pretrain_epoch_size": 0,         # epoch size of pretrain checkpoint
+"epoch_size": 120,                # epoch size for training
+"pretrain_epoch_size": 0,         # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
 "save_checkpoint": True,          # whether save checkpoint or not
 "save_checkpoint_epochs": 1,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
 "keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint

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

@@ -25,6 +25,7 @@ config1 = ed({
     "momentum": 0.9,
     "weight_decay": 1e-4,
     "epoch_size": 90,
+    "pretrain_epoch_size": 0,
     "save_checkpoint": True,
     "save_checkpoint_epochs": 5,
     "keep_checkpoint_max": 10,
@@ -44,7 +45,7 @@ config2 = ed({
     "momentum": 0.9,
     "weight_decay": 1e-4,
     "epoch_size": 90,
-    "pretrain_epoch_size": 1,
+    "pretrain_epoch_size": 0,
     "save_checkpoint": True,
     "save_checkpoint_epochs": 5,
     "keep_checkpoint_max": 10,

model_zoo/official/cv/resnet/train.py

@@ -184,4 +184,5 @@ if __name__ == '__main__':
         cb += [ckpt_cb]
 
     # train model
-    model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=(not args_opt.parameter_server))
+    model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb,
+                dataset_sink_mode=(not args_opt.parameter_server))