tfp.experimental.nn.util.im2row

Rearrange image blocks into rows.

tfp.experimental.nn.util.im2row(
    x, block_shape, slice_step=(1, 1), padding='VALID', name=None
)

This function can be used to implement 2D convolution as a matmul, e.g.,

tf.nn.conv2d(x, k) = tf.matmul( tf.experimental.nn.util.im2row(x), tf.reshape(k, shape=[-1, out_size])).

Args
`x`	Rank 3 (or more) Tensor representing 2D images.
`block_shape`	Length-2 vector representing the block or "filter" shape.
`slice_step`	Length-2 vector specifying the convolution stride length. Default value: `(1, 1)`.
`padding`	One of `'VALID'` or `'SAME'` (case insensitive). Default value: `'VALID'`.
`name`	Python `str` used to describe ops created by this function. Default value: `None` (i.e., `'im2col'`).

Returns
`im2row_x`	batch of matrices representing subblock copies of `x`. Same batch shape as `x` but with rightmost shape: `batch_shape + [oh * ow, block_shape[0] * block_shape[1] * channels]`, where `oh = (h - block_shape[0] + 1) // slice_step[0]` and `ow = (w - block_shape[1] + 1) // slice_step[1]` when `padding = 'VALID'` and `oh = h` and `ow = w` when `padding = 'SAME'`.
`shape`	shape `Tensor` equivalent to: `batch_shape + [oh, ow, block_shape[0] * block_shape[1] * channels]` where `oh, ow` are defined as above.

Args