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import operator | |
from functools import reduce | |
def maybe_view(tensor, size, check_same_size=True): | |
if check_same_size and tensor.size() == size: | |
return tensor | |
return tensor.contiguous().view(size) | |
def maybe_unexpand(tensor, old_size, check_same_size=True): | |
if check_same_size and tensor.size() == old_size: | |
return tensor | |
num_unsqueezed = tensor.dim() - len(old_size) | |
expanded_dims = [ | |
dim | |
for dim, (expanded, original) in enumerate( | |
zip(tensor.size()[num_unsqueezed:], old_size) | |
) | |
if expanded != original | |
] | |
for _ in range(num_unsqueezed): | |
tensor = tensor.sum(0, keepdim=False) | |
for dim in expanded_dims: | |
tensor = tensor.sum(dim, keepdim=True) | |
return tensor | |
# Check whether the op enable broadcasting, and whether it is supported by ONNX. | |
# If dims1 and dims2 are different, then broadcast is True. | |
# We always assume the combination of dims1 and dims2 is broadcastable. | |
# The following types of broadcasting are supported in ONNX: | |
# 1) Only one element in dims2, such as dims2 = [1, 1] | |
# 2) dims2 is suffix of dims1, such as dims1 = [2, 3, 4], and dims2 = [3, 4] | |
# Details can be found here: https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm | |
def check_onnx_broadcast(dims1, dims2): | |
broadcast = False | |
supported = True | |
len1 = len(dims1) | |
len2 = len(dims2) | |
numel1 = reduce(operator.mul, dims1) | |
numel2 = reduce(operator.mul, dims2) | |
if len1 < len2: | |
broadcast = True | |
if numel2 != 1: | |
supported = False | |
elif len1 > len2: | |
broadcast = True | |
if numel2 != 1 and dims1[len1 - len2 :] != dims2: | |
supported = False | |
else: | |
if dims1 != dims2: | |
broadcast = True | |
if numel2 != 1: | |
supported = False | |
if not supported: | |
raise ValueError( | |
f"Numpy style broadcasting is not supported in ONNX. Input dims are: {dims1}, {dims2}" | |
) | |
return broadcast | |