提交 9126cb6f 编写于 作者: L LDOUBLEV

modify rec saved model dir

上级 8007a955
......@@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output/rec
save_model_dir: output/rec_CRNN
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
......
......@@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output/rec
save_model_dir: output/rec_Rosetta
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
......
......@@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output/rec
save_model_dir: output/rec_RARE
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
......
......@@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output/rec
save_model_dir: output/rec_STARNet
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
......
......@@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output/rec
save_model_dir: output/rec_CRNN
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
......
......@@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output/rec
save_model_dir: output/rec_Rosetta
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
......
......@@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output/rec
save_model_dir: output/rec_RARE
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
......
......@@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output/rec
save_model_dir: output/rec_STARNet
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
......
......@@ -284,9 +284,7 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
eval_batch_step = config['Global']['eval_batch_step']
save_epoch_step = config['Global']['save_epoch_step']
save_model_dir = config['Global']['save_model_dir']
if save_model_dir[-1] == "/":
save_model_dir = save_model_dir[:-1]
if not os.path.exists(save_model_dir + config['Global']['algorithm']):
if not os.path.exists(save_model_dir):
os.makedirs(save_model_dir)
train_stats = TrainingStats(log_smooth_window, ['loss', 'acc'])
best_eval_acc = -1
......
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