- class mmpretrain.datasets.transforms.RandomCrop(crop_size, padding=None, pad_if_needed=False, pad_val=0, padding_mode='constant')¶
crop_size (int | Sequence) – Desired output size of the crop. If crop_size is an int instead of sequence like (h, w), a square crop (crop_size, crop_size) is made.
padding (int | Sequence, optional) – Optional padding on each border of the image. If a sequence of length 4 is provided, it is used to pad left, top, right, bottom borders respectively. If a sequence of length 2 is provided, it is used to pad left/right, top/bottom borders, respectively. Default: None, which means no padding.
pad_if_needed (bool) – It will pad the image if smaller than the desired size to avoid raising an exception. Since cropping is done after padding, the padding seems to be done at a random offset. Default: False.
pad_val (Number | Sequence[Number]) – Pixel pad_val value for constant fill. If a tuple of length 3, it is used to pad_val R, G, B channels respectively. Default: 0.
padding_mode (str) –
Type of padding. Defaults to “constant”. Should be one of the following:
constant: Pads with a constant value, this value is specified with pad_val.
edge: pads with the last value at the edge of the image.
reflect: Pads with reflection of image without repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2].
symmetric: Pads with reflection of image repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3].