forked from mindspore-Ecosystem/mindspore
fix model_zoo
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25e587e483
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a45e29800c
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@ -85,4 +85,3 @@ if __name__ == "__main__":
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opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
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model = Model(net, loss, opt)
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model.train(config.epoch_size, train_dataset, callback)
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@ -46,7 +46,7 @@ args = parser.parse_args()
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if __name__ == "__main__":
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size, cfg.epoch_size)
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ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size, 1)
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step_size = ds_train.get_dataset_size()
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# define fusion network
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@ -105,7 +105,7 @@ if __name__ == '__main__':
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# define dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=True,
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repeat_num=epoch_size,
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repeat_num=1,
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batch_size=config.batch_size,
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target=args_opt.device_target)
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step_size = dataset.get_dataset_size()
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@ -191,7 +191,7 @@ def train(cloud_args=None):
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# dataloader
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de_dataset = classification_dataset(args.data_dir, args.image_size,
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args.per_batch_size, args.max_epoch,
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args.per_batch_size, 1,
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args.rank, args.group_size)
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de_dataset.map_model = 4 # !!!important
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args.steps_per_epoch = de_dataset.get_dataset_size()
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@ -59,7 +59,7 @@ if __name__ == '__main__':
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max_captcha_digits = cf.max_captcha_digits
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input_size = m.ceil(cf.captcha_height / 64) * 64 * 3
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# create dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path, repeat_num=cf.epoch_size, batch_size=cf.batch_size)
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dataset = create_dataset(dataset_path=args_opt.dataset_path, repeat_num=1, batch_size=cf.batch_size)
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step_size = dataset.get_dataset_size()
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# define lr
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lr_init = cf.learning_rate if not args_opt.run_distribute else cf.learning_rate * args_opt.device_num
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@ -290,7 +290,7 @@ def data_to_mindrecord_byte_image(image_dir, anno_path, mindrecord_dir, prefix,
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writer.commit()
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def create_yolo_dataset(mindrecord_dir, batch_size=32, repeat_num=10, device_num=1, rank=0,
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def create_yolo_dataset(mindrecord_dir, batch_size=32, repeat_num=1, device_num=1, rank=0,
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is_training=True, num_parallel_workers=8):
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"""Creatr YOLOv3 dataset with MindDataset."""
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ds = de.MindDataset(mindrecord_dir, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank,
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