set evaluation interval
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@ -22,7 +22,7 @@
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| print_batch_step | 设置打印log间隔 | 10 | \ |
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| print_batch_step | 设置打印log间隔 | 10 | \ |
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| save_model_dir | 设置模型保存路径 | output/{算法名称} | \ |
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| save_model_dir | 设置模型保存路径 | output/{算法名称} | \ |
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| save_epoch_step | 设置模型保存间隔 | 3 | \ |
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| save_epoch_step | 设置模型保存间隔 | 3 | \ |
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| eval_batch_step | 设置模型评估间隔 | 2000 | \ |
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| eval_batch_step | 设置模型评估间隔 | 2000 或 [1000, 2000] | 2000 表示每2000次迭代评估一次,[1000, 2000]表示从1000次迭代开始,每2000次评估一次 |
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|train_batch_size_per_card | 设置训练时单卡batch size | 256 | \ |
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|train_batch_size_per_card | 设置训练时单卡batch size | 256 | \ |
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| test_batch_size_per_card | 设置评估时单卡batch size | 256 | \ |
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| test_batch_size_per_card | 设置评估时单卡batch size | 256 | \ |
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| image_shape | 设置输入图片尺寸 | [3, 32, 100] | \ |
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| image_shape | 设置输入图片尺寸 | [3, 32, 100] | \ |
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@ -22,7 +22,7 @@ Take `rec_chinese_lite_train.yml` as an example
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| print_batch_step | Set print log interval | 10 | \ |
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| print_batch_step | Set print log interval | 10 | \ |
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| save_model_dir | Set model save path | output/{model_name} | \ |
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| save_model_dir | Set model save path | output/{model_name} | \ |
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| save_epoch_step | Set model save interval | 3 | \ |
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| save_epoch_step | Set model save interval | 3 | \ |
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| eval_batch_step | Set the model evaluation interval | 2000 | \ |
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| eval_batch_step | Set the model evaluation interval |2000 or [1000, 2000] |runing evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration |
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|train_batch_size_per_card | Set the batch size during training | 256 | \ |
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|train_batch_size_per_card | Set the batch size during training | 256 | \ |
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| test_batch_size_per_card | Set the batch size during testing | 256 | \ |
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| test_batch_size_per_card | Set the batch size during testing | 256 | \ |
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| image_shape | Set input image size | [3, 32, 100] | \ |
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| image_shape | Set input image size | [3, 32, 100] | \ |
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@ -219,6 +219,13 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
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epoch_num = config['Global']['epoch_num']
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epoch_num = config['Global']['epoch_num']
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print_batch_step = config['Global']['print_batch_step']
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print_batch_step = config['Global']['print_batch_step']
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eval_batch_step = config['Global']['eval_batch_step']
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eval_batch_step = config['Global']['eval_batch_step']
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start_eval_step = 0
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if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
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start_eval_step = eval_batch_step[0]
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eval_batch_step = eval_batch_step[1]
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logger.info(
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"During the training process, after the {}th iteration, an evaluation is run every {} iterations".
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format(start_eval_step, eval_batch_step))
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save_epoch_step = config['Global']['save_epoch_step']
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save_epoch_step = config['Global']['save_epoch_step']
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save_model_dir = config['Global']['save_model_dir']
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save_model_dir = config['Global']['save_model_dir']
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if not os.path.exists(save_model_dir):
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if not os.path.exists(save_model_dir):
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@ -246,7 +253,7 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
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t2 = time.time()
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t2 = time.time()
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train_batch_elapse = t2 - t1
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train_batch_elapse = t2 - t1
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train_stats.update(stats)
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train_stats.update(stats)
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if train_batch_id > 0 and train_batch_id \
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if train_batch_id > start_eval_step and train_batch_id \
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% print_batch_step == 0:
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% print_batch_step == 0:
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logs = train_stats.log()
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logs = train_stats.log()
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strs = 'epoch: {}, iter: {}, {}, time: {:.3f}'.format(
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strs = 'epoch: {}, iter: {}, {}, time: {:.3f}'.format(
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@ -286,6 +293,13 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
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epoch_num = config['Global']['epoch_num']
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epoch_num = config['Global']['epoch_num']
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print_batch_step = config['Global']['print_batch_step']
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print_batch_step = config['Global']['print_batch_step']
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eval_batch_step = config['Global']['eval_batch_step']
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eval_batch_step = config['Global']['eval_batch_step']
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start_eval_step = 0
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if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
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start_eval_step = eval_batch_step[0]
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eval_batch_step = eval_batch_step[1]
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logger.info(
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"During the training process, after the {}th iteration, an evaluation is run every {} iterations".
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format(start_eval_step, eval_batch_step))
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save_epoch_step = config['Global']['save_epoch_step']
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save_epoch_step = config['Global']['save_epoch_step']
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save_model_dir = config['Global']['save_model_dir']
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save_model_dir = config['Global']['save_model_dir']
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if not os.path.exists(save_model_dir):
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if not os.path.exists(save_model_dir):
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@ -324,7 +338,7 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
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train_batch_elapse = t2 - t1
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train_batch_elapse = t2 - t1
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stats = {'loss': loss, 'acc': acc}
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stats = {'loss': loss, 'acc': acc}
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train_stats.update(stats)
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train_stats.update(stats)
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if train_batch_id > 0 and train_batch_id \
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if train_batch_id > start_eval_step and train_batch_id \
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% print_batch_step == 0:
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% print_batch_step == 0:
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logs = train_stats.log()
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logs = train_stats.log()
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strs = 'epoch: {}, iter: {}, lr: {:.6f}, {}, time: {:.3f}'.format(
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strs = 'epoch: {}, iter: {}, lr: {:.6f}, {}, time: {:.3f}'.format(
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