103 lines
3.2 KiB
Python
Executable file
103 lines
3.2 KiB
Python
Executable file
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
|
"""
|
|
helper class that supports empty tensors on some nn functions.
|
|
|
|
Ideally, add support directly in PyTorch to empty tensors in
|
|
those functions.
|
|
|
|
This can be removed once https://github.com/pytorch/pytorch/issues/12013
|
|
is implemented
|
|
"""
|
|
|
|
import math
|
|
import torch
|
|
from torch.nn.modules.utils import _ntuple
|
|
|
|
|
|
class _NewEmptyTensorOp(torch.autograd.Function):
|
|
@staticmethod
|
|
def forward(ctx, x, new_shape):
|
|
ctx.shape = x.shape
|
|
return x.new_empty(new_shape)
|
|
|
|
@staticmethod
|
|
def backward(ctx, grad):
|
|
shape = ctx.shape
|
|
return _NewEmptyTensorOp.apply(grad, shape), None
|
|
|
|
|
|
|
|
class Conv2d(torch.nn.Conv2d):
|
|
def forward(self, x):
|
|
if x.numel() > 0:
|
|
return super(Conv2d, self).forward(x)
|
|
# get output shape
|
|
|
|
output_shape = [
|
|
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
|
|
for i, p, di, k, d in zip(
|
|
x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride
|
|
)
|
|
]
|
|
output_shape = [x.shape[0], self.weight.shape[0]] + output_shape
|
|
return _NewEmptyTensorOp.apply(x, output_shape)
|
|
|
|
|
|
class ConvTranspose2d(torch.nn.ConvTranspose2d):
|
|
def forward(self, x):
|
|
if x.numel() > 0:
|
|
return super(ConvTranspose2d, self).forward(x)
|
|
# get output shape
|
|
|
|
output_shape = [
|
|
(i - 1) * d - 2 * p + (di * (k - 1) + 1) + op
|
|
for i, p, di, k, d, op in zip(
|
|
x.shape[-2:],
|
|
self.padding,
|
|
self.dilation,
|
|
self.kernel_size,
|
|
self.stride,
|
|
self.output_padding,
|
|
)
|
|
]
|
|
output_shape = [x.shape[0], self.bias.shape[0]] + output_shape
|
|
return _NewEmptyTensorOp.apply(x, output_shape)
|
|
|
|
|
|
def interpolate(
|
|
input, size=None, scale_factor=None, mode="nearest", align_corners=None
|
|
):
|
|
if input.numel() > 0:
|
|
return torch.nn.functional.interpolate(
|
|
input, size, scale_factor, mode, align_corners
|
|
)
|
|
|
|
def _check_size_scale_factor(dim):
|
|
if size is None and scale_factor is None:
|
|
raise ValueError("either size or scale_factor should be defined")
|
|
if size is not None and scale_factor is not None:
|
|
raise ValueError("only one of size or scale_factor should be defined")
|
|
if (
|
|
scale_factor is not None
|
|
and isinstance(scale_factor, tuple)
|
|
and len(scale_factor) != dim
|
|
):
|
|
raise ValueError(
|
|
"scale_factor shape must match input shape. "
|
|
"Input is {}D, scale_factor size is {}".format(dim, len(scale_factor))
|
|
)
|
|
|
|
def _output_size(dim):
|
|
_check_size_scale_factor(dim)
|
|
if size is not None:
|
|
return size
|
|
scale_factors = _ntuple(dim)(scale_factor)
|
|
# math.floor might return float in py2.7
|
|
return [
|
|
int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)
|
|
]
|
|
|
|
output_shape = tuple(_output_size(2))
|
|
output_shape = input.shape[:-2] + output_shape
|
|
return _NewEmptyTensorOp.apply(input, output_shape)
|