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52a9452 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | import torch
from torch.autograd import Function
from torch.nn.modules.utils import _pair
from .. import deform_conv_cuda
class DeformConvFunction(Function):
@staticmethod
def forward(ctx,
input,
offset,
weight,
stride=1,
padding=0,
dilation=1,
groups=1,
deformable_groups=1,
im2col_step=64):
if input is not None and input.dim() != 4:
raise ValueError(
"Expected 4D tensor as input, got {}D tensor instead.".format(
input.dim()))
ctx.stride = _pair(stride)
ctx.padding = _pair(padding)
ctx.dilation = _pair(dilation)
ctx.groups = groups
ctx.deformable_groups = deformable_groups
ctx.im2col_step = im2col_step
ctx.save_for_backward(input, offset, weight)
output = input.new_empty(
DeformConvFunction._output_size(input, weight, ctx.padding,
ctx.dilation, ctx.stride))
ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones
if not input.is_cuda:
raise NotImplementedError
else:
cur_im2col_step = min(ctx.im2col_step, input.shape[0])
assert (input.shape[0] %
cur_im2col_step) == 0, 'im2col step must divide batchsize'
deform_conv_cuda.deform_conv_forward_cuda(
input, weight, offset, output, ctx.bufs_[0], ctx.bufs_[1],
weight.size(3), weight.size(2), ctx.stride[1], ctx.stride[0],
ctx.padding[1], ctx.padding[0], ctx.dilation[1],
ctx.dilation[0], ctx.groups, ctx.deformable_groups,
cur_im2col_step)
return output
@staticmethod
def backward(ctx, grad_output):
input, offset, weight = ctx.saved_tensors
grad_input = grad_offset = grad_weight = None
if not grad_output.is_cuda:
raise NotImplementedError
else:
cur_im2col_step = min(ctx.im2col_step, input.shape[0])
assert (input.shape[0] %
cur_im2col_step) == 0, 'im2col step must divide batchsize'
if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
grad_input = torch.zeros_like(input)
grad_offset = torch.zeros_like(offset)
deform_conv_cuda.deform_conv_backward_input_cuda(
input, offset, grad_output, grad_input,
grad_offset, weight, ctx.bufs_[0], weight.size(3),
weight.size(2), ctx.stride[1], ctx.stride[0],
ctx.padding[1], ctx.padding[0], ctx.dilation[1],
ctx.dilation[0], ctx.groups, ctx.deformable_groups,
cur_im2col_step)
if ctx.needs_input_grad[2]:
grad_weight = torch.zeros_like(weight)
deform_conv_cuda.deform_conv_backward_parameters_cuda(
input, offset, grad_output,
grad_weight, ctx.bufs_[0], ctx.bufs_[1], weight.size(3),
weight.size(2), ctx.stride[1], ctx.stride[0],
ctx.padding[1], ctx.padding[0], ctx.dilation[1],
ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1,
cur_im2col_step)
return (grad_input, grad_offset, grad_weight, None, None, None, None,
None)
@staticmethod
def _output_size(input, weight, padding, dilation, stride):
channels = weight.size(0)
output_size = (input.size(0), channels)
for d in range(input.dim() - 2):
in_size = input.size(d + 2)
pad = padding[d]
kernel = dilation[d] * (weight.size(d + 2) - 1) + 1
stride_ = stride[d]
output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
if not all(map(lambda s: s > 0, output_size)):
raise ValueError(
"convolution input is too small (output would be {})".format(
'x'.join(map(str, output_size))))
return output_size
class ModulatedDeformConvFunction(Function):
@staticmethod
def forward(ctx,
input,
offset,
mask,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1,
deformable_groups=1):
ctx.stride = stride
ctx.padding = padding
ctx.dilation = dilation
ctx.groups = groups
ctx.deformable_groups = deformable_groups
ctx.with_bias = bias is not None
if not ctx.with_bias:
bias = input.new_empty(1) # fake tensor
if not input.is_cuda:
raise NotImplementedError
if weight.requires_grad or mask.requires_grad or offset.requires_grad \
or input.requires_grad:
ctx.save_for_backward(input, offset, mask, weight, bias)
output = input.new_empty(
ModulatedDeformConvFunction._infer_shape(ctx, input, weight))
ctx._bufs = [input.new_empty(0), input.new_empty(0)]
deform_conv_cuda.modulated_deform_conv_cuda_forward(
input, weight, bias, ctx._bufs[0], offset, mask, output,
ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride,
ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation,
ctx.groups, ctx.deformable_groups, ctx.with_bias)
return output
@staticmethod
def backward(ctx, grad_output):
if not grad_output.is_cuda:
raise NotImplementedError
input, offset, mask, weight, bias = ctx.saved_tensors
grad_input = torch.zeros_like(input)
grad_offset = torch.zeros_like(offset)
grad_mask = torch.zeros_like(mask)
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(bias)
deform_conv_cuda.modulated_deform_conv_cuda_backward(
input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1],
grad_input, grad_weight, grad_bias, grad_offset, grad_mask,
grad_output, weight.shape[2], weight.shape[3], ctx.stride,
ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation,
ctx.groups, ctx.deformable_groups, ctx.with_bias)
if not ctx.with_bias:
grad_bias = None
return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias,
None, None, None, None, None)
@staticmethod
def _infer_shape(ctx, input, weight):
n = input.size(0)
channels_out = weight.size(0)
height, width = input.shape[2:4]
kernel_h, kernel_w = weight.shape[2:4]
height_out = (height + 2 * ctx.padding -
(ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1
width_out = (width + 2 * ctx.padding -
(ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1
return n, channels_out, height_out, width_out
deform_conv = DeformConvFunction.apply
modulated_deform_conv = ModulatedDeformConvFunction.apply
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