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mmedit.utils.tensor2img

mmedit.utils.tensor2img(tensor, out_type=<class 'numpy.uint8'>, min_max=(0, 1))[source]

Convert torch Tensors into image numpy arrays.

After clamping to (min, max), image values will be normalized to [0, 1].

For different tensor shapes, this function will have different behaviors:

  1. 4D mini-batch Tensor of shape (N x 3/1 x H x W):

    Use make_grid to stitch images in the batch dimension, and then convert it to numpy array.

  2. 3D Tensor of shape (3/1 x H x W) and 2D Tensor of shape (H x W):

    Directly change to numpy array.

Note that the image channel in input tensors should be RGB order. This function will convert it to cv2 convention, i.e., (H x W x C) with BGR order.

Parameters
  • tensor (Tensor | list[Tensor]) – Input tensors.

  • out_type (numpy type) – Output types. If np.uint8, transform outputs to uint8 type with range [0, 255]; otherwise, float type with range [0, 1]. Default: np.uint8.

  • min_max (tuple) – min and max values for clamp.

Returns

3D ndarray of shape (H x W x C) or 2D ndarray of shape (H x W).

Return type

(Tensor | list[Tensor])

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