What is the best way to convert a tensor from NHWC format to NCHW format, and vice versa?
Is there an op specifically that does this, or will I need to use some combination of the split/concat type operations?
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问题:
回答1:
All you need to do is a permutation of the dimensions from NHWC to NCHW (or the contrary).
The meaning of each letter might help understand:
- N: number of images in the batch
- H: height of the image
- W: width of the image
- C: number of channels of the image (ex: 3 for RGB, 1 for grayscale...)
From NHWC to NCHW
The image shape is (N, H, W, C) and we want the output to have shape (N, C, H, W). Therefore we need to apply tf.transpose with a well chosen permutation perm.
The returned tensor's dimension
iwill correspond to the input dimensionperm[i]
perm[0] = 0 # output dimension 0 will be 'N', which was dimension 0 in the input perm[1] = 3 # output dimension 1 will be 'C', which was dimension 3 in the input perm[2] = 1 # output dimension 2 will be 'H', which was dimension 1 in the input perm[3] = 2 # output dimension 3 will be 'W', which was dimension 2 in the input In practice:
images_nhwc = tf.placeholder(tf.float32, [None, 200, 300, 3]) # input batch out = tf.transpose(x, [0, 3, 1, 2]) print(out.get_shape()) # the shape of out is [None, 3, 200, 300] From NCHW to NHWC
The image shape is (N, C, H, W) and we want the output to have shape (N, H, W, C). Therefore we need to apply tf.transpose with a well chosen permutation perm.
The returned tensor's dimension
iwill correspond to the input dimensionperm[i]
perm[0] = 0 # output dimension 0 will be 'N', which was dimension 0 in the input perm[1] = 2 # output dimension 1 will be 'H', which was dimension 2 in the input perm[2] = 3 # output dimension 2 will be 'W', which was dimension 3 in the input perm[3] = 1 # output dimension 3 will be 'C', which was dimension 1 in the input In practice:
images_nchw = tf.placeholder(tf.float32, [None, 3, 200, 300]) # input batch out = tf.transpose(x, [0, 2, 3, 1]) print(out.get_shape()) # the shape of out is [None, 200, 300, 3]