mirror of
https://github.com/zebrajr/pytorch.git
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104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
import torch
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from functools import reduce
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def maybe_view(variable, size, check_same_size=True):
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if check_same_size and variable.size() == size:
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return variable
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return variable.contiguous().view(size)
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def maybe_unexpand(variable, old_size, check_same_size=True):
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if check_same_size and variable.size() == old_size:
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return variable
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num_unsqueezed = variable.dim() - len(old_size)
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expanded_dims = [dim for dim, (expanded, original)
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in enumerate(zip(variable.size()[num_unsqueezed:], old_size))
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if expanded != original]
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for _ in range(num_unsqueezed):
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variable = variable.sum(0, keepdim=False)
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for dim in expanded_dims:
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variable = variable.sum(dim, keepdim=True)
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return variable
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_SAME_SIZE = 2
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_EXPANDABLE = 1
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_NOT_EXPANDABLE = 0
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def variable_expandable(variable, old_size):
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if variable.size() == old_size:
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return _SAME_SIZE
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try:
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torch._C._infer_size(variable.size(), old_size)
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except RuntimeError:
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return _NOT_EXPANDABLE
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return _EXPANDABLE
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def maybe_unexpand_or_view(variable, old_size):
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var_expanded = variable_expandable(variable, old_size)
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if var_expanded == _SAME_SIZE:
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return variable
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elif var_expanded == _EXPANDABLE:
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return maybe_unexpand(variable, old_size, False)
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else:
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return maybe_view(variable, old_size, False)
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# Generate paddings in ONNX order based on pad in pytorch.
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# Arguments:
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# dim: the dimension of the tensor.
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# pad: the paddings in pytorch.
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# The order is dim_n_begin, dim_n_end, dim_n-1_begin, dim_n-1_end, ...
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def prepare_onnx_paddings(dim, pad):
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assert isinstance(dim, int)
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# The order of paddings is dim_0_begin, dim_0_end, dim_1_begin, ... , dim_n_end.
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# n is the dimension of input.
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assert len(pad) <= dim * 2
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paddings = []
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# pad is guaranteed to have even elements.
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for i, j in zip(pad[0::2], pad[1::2]):
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paddings = [i, j] + paddings
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while len(paddings) < 2 * dim:
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paddings = [0, 0] + paddings
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assert len(paddings) == dim * 2
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return paddings
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# Check whether the op enable broadcasting, and whether it is supported by ONNX.
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# If dims1 and dims2 are different, then broadcast is True.
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# We always assume the combination of dims1 and dims2 is broadcastable.
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# The following types of broadcasting are supported in ONNX:
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# 1) Only one element in dims2, such as dims2 = [1, 1]
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# 2) dims2 is suffix of dims1, such as dims1 = [2, 3, 4], and dims2 = [3, 4]
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# Details can be found here: https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm
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def check_onnx_broadcast(dims1, dims2):
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broadcast = False
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supported = True
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len1 = len(dims1)
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len2 = len(dims2)
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numel1 = reduce(lambda x, y: x * y, dims1)
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numel2 = reduce(lambda x, y: x * y, dims2)
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if len1 < len2:
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broadcast = True
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if numel2 != 1:
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supported = False
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elif len1 > len2:
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broadcast = True
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if numel2 != 1 and dims1[len1 - len2:] != dims2:
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supported = False
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else:
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if dims1 != dims2:
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broadcast = True
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if numel2 != 1:
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supported = False
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if not supported:
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raise ValueError("Numpy style broadcasting is not supported in ONNX. "
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"Input dims are: {}, {}".format(dims1, dims2))
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return broadcast
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