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from . import transforms as T |
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def build_transforms(cfg, is_train=True): |
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if is_train: |
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if len(cfg.AUGMENT.MULT_MIN_SIZE_TRAIN)>0: |
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min_size = cfg.AUGMENT.MULT_MIN_SIZE_TRAIN |
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else: |
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min_size = cfg.INPUT.MIN_SIZE_TRAIN |
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max_size = cfg.INPUT.MAX_SIZE_TRAIN |
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flip_horizontal_prob = cfg.AUGMENT.FLIP_PROB_TRAIN |
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flip_vertical_prob = cfg.AUGMENT.VERTICAL_FLIP_PROB_TRAIN |
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brightness = cfg.AUGMENT.BRIGHTNESS |
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contrast = cfg.AUGMENT.CONTRAST |
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saturation = cfg.AUGMENT.SATURATION |
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hue = cfg.AUGMENT.HUE |
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crop_prob = cfg.AUGMENT.CROP_PROB |
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min_ious = cfg.AUGMENT.CROP_MIN_IOUS |
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min_crop_size = cfg.AUGMENT.CROP_MIN_SIZE |
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else: |
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min_size = cfg.INPUT.MIN_SIZE_TEST |
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max_size = cfg.INPUT.MAX_SIZE_TEST |
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flip_horizontal_prob = 0.0 |
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fix_res = cfg.INPUT.FIX_RES |
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if cfg.INPUT.FORMAT is not '': |
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input_format = cfg.INPUT.FORMAT |
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elif cfg.INPUT.TO_BGR255: |
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input_format = 'bgr255' |
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normalize_transform = T.Normalize( |
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mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD, format=input_format |
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) |
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transform = T.Compose( |
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[ |
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T.Resize(min_size, max_size, restrict=fix_res), |
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T.RandomHorizontalFlip(flip_horizontal_prob), |
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T.ToTensor(), |
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normalize_transform, |
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] |
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) |
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return transform |
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