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# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from onnx.reference.ops.experimental._op_run_experimental import OpRunExperimental
from onnx.reference.ops_optimized.op_conv_optimized import im2col_fast
class Im2Col(OpRunExperimental):
def _run(self, img, kernel_shape, dilations=None, pads=None, strides=None): # type: ignore
if dilations is None:
dilations = [1 for s in img.shape[2:]]
if pads is None:
pads = [0 for s in img.shape[2:]] * 2
if strides is None:
strides = [1 for s in img.shape[2:]]
if min(dilations) == max(dilations) == 1:
return (im2col_fast(img, tuple(kernel_shape[2:]), pads, strides)[0],) # type: ignore
if dilations[0] != 1 or min(dilations) != max(dilations):
# Let's compute the dilated kernel.
nd = len(dilations)
new_kernel_shape = []
new_shape = list(kernel_shape)
for i, d in enumerate(dilations):
di = len(kernel_shape) - nd + i
new_shape.append(kernel_shape[di] + (kernel_shape[di] - 1) * (d - 1))
new_kernel_shape.append(
kernel_shape[i] + (kernel_shape[i] - 1) * (d - 1)
)
kernel_shape = new_kernel_shape
return (im2col_fast(img, tuple(kernel_shape[2:]), pads, strides),) # type: ignore
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