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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Custom replacement for `torch.nn.functional.grid_sample` that
supports arbitrarily high order gradients between the input and output.
Only works on 2D images and assumes
`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
import warnings
import torch
# pylint: disable=redefined-builtin
# pylint: disable=arguments-differ
# pylint: disable=protected-access
#----------------------------------------------------------------------------
enabled = False # Enable the custom op by setting this to true.
#----------------------------------------------------------------------------
def grid_sample(input, grid):
if _should_use_custom_op():
return _GridSample2dForward.apply(input, grid)
return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
#----------------------------------------------------------------------------
def _should_use_custom_op():
if not enabled:
return False
if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
return True
warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().')
return False
#----------------------------------------------------------------------------
class _GridSample2dForward(torch.autograd.Function):
@staticmethod
def forward(ctx, input, grid):
assert input.ndim == 4
assert grid.ndim == 4
output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
ctx.save_for_backward(input, grid)
return output
@staticmethod
def backward(ctx, grad_output):
input, grid = ctx.saved_tensors
grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
return grad_input, grad_grid
#----------------------------------------------------------------------------
class _GridSample2dBackward(torch.autograd.Function):
@staticmethod
def forward(ctx, grad_output, input, grid):
op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
ctx.save_for_backward(grid)
return grad_input, grad_grid
@staticmethod
def backward(ctx, grad2_grad_input, grad2_grad_grid):
_ = grad2_grad_grid # unused
grid, = ctx.saved_tensors
grad2_grad_output = None
grad2_input = None
grad2_grid = None
if ctx.needs_input_grad[0]:
grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
assert not ctx.needs_input_grad[2]
return grad2_grad_output, grad2_input, grad2_grid
#----------------------------------------------------------------------------
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