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@ -40,37 +40,35 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
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rank_id = int(os.getenv("RANK_ID"))
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if rank_size == 1:
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=16, shuffle=True)
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
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else:
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=16, shuffle=True,
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
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num_shards=rank_size, shard_id=rank_id)
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resize_height = config.image_height
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resize_width = config.image_width
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rescale = 1.0 / 255.0
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shift = 0.0
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buffer_size = 1000
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# define map operations
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decode_op = C.Decode()
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resize_crop_op = C.RandomResizedCrop(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
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resize_crop_op = C.RandomCropDecodeResize(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
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horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)
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resize_op = C.Resize((256, 256))
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center_crop = C.CenterCrop(resize_width)
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rescale_op = C.Rescale(rescale, shift)
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normalize_op = C.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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rescale_op = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
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normalize_op = C.Normalize(mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])
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change_swap_op = C.HWC2CHW()
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if do_train:
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trans = [decode_op, resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]
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trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]
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else:
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trans = [decode_op, resize_op, center_crop, rescale_op, normalize_op, change_swap_op]
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type_cast_op = C2.TypeCast(mstype.int32)
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ds = ds.map(input_columns="image", operations=trans)
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ds = ds.map(input_columns="label", operations=type_cast_op)
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ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8)
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ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
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# apply shuffle operations
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ds = ds.shuffle(buffer_size=buffer_size)
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