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import torch |
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from torch.autograd import Function |
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from torch.nn.modules.utils import _pair |
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from .. import deform_conv_cuda |
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class DeformConvFunction(Function): |
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@staticmethod |
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def forward(ctx, |
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input, |
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offset, |
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weight, |
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stride=1, |
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padding=0, |
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dilation=1, |
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groups=1, |
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deformable_groups=1, |
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im2col_step=64): |
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if input is not None and input.dim() != 4: |
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raise ValueError( |
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"Expected 4D tensor as input, got {}D tensor instead.".format( |
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input.dim())) |
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ctx.stride = _pair(stride) |
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ctx.padding = _pair(padding) |
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ctx.dilation = _pair(dilation) |
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ctx.groups = groups |
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ctx.deformable_groups = deformable_groups |
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ctx.im2col_step = im2col_step |
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ctx.save_for_backward(input, offset, weight) |
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output = input.new_empty( |
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DeformConvFunction._output_size(input, weight, ctx.padding, |
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ctx.dilation, ctx.stride)) |
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ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] |
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if not input.is_cuda: |
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raise NotImplementedError |
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else: |
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cur_im2col_step = min(ctx.im2col_step, input.shape[0]) |
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assert (input.shape[0] % |
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cur_im2col_step) == 0, 'im2col step must divide batchsize' |
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deform_conv_cuda.deform_conv_forward_cuda( |
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input, weight, offset, output, ctx.bufs_[0], ctx.bufs_[1], |
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weight.size(3), weight.size(2), ctx.stride[1], ctx.stride[0], |
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ctx.padding[1], ctx.padding[0], ctx.dilation[1], |
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ctx.dilation[0], ctx.groups, ctx.deformable_groups, |
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cur_im2col_step) |
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return output |
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@staticmethod |
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def backward(ctx, grad_output): |
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input, offset, weight = ctx.saved_tensors |
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grad_input = grad_offset = grad_weight = None |
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if not grad_output.is_cuda: |
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raise NotImplementedError |
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else: |
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cur_im2col_step = min(ctx.im2col_step, input.shape[0]) |
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assert (input.shape[0] % |
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cur_im2col_step) == 0, 'im2col step must divide batchsize' |
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if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: |
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grad_input = torch.zeros_like(input) |
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grad_offset = torch.zeros_like(offset) |
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deform_conv_cuda.deform_conv_backward_input_cuda( |
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input, offset, grad_output, grad_input, |
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grad_offset, weight, ctx.bufs_[0], weight.size(3), |
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weight.size(2), ctx.stride[1], ctx.stride[0], |
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ctx.padding[1], ctx.padding[0], ctx.dilation[1], |
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ctx.dilation[0], ctx.groups, ctx.deformable_groups, |
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cur_im2col_step) |
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if ctx.needs_input_grad[2]: |
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grad_weight = torch.zeros_like(weight) |
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deform_conv_cuda.deform_conv_backward_parameters_cuda( |
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input, offset, grad_output, |
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grad_weight, ctx.bufs_[0], ctx.bufs_[1], weight.size(3), |
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weight.size(2), ctx.stride[1], ctx.stride[0], |
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ctx.padding[1], ctx.padding[0], ctx.dilation[1], |
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ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1, |
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cur_im2col_step) |
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return (grad_input, grad_offset, grad_weight, None, None, None, None, |
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None) |
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@staticmethod |
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def _output_size(input, weight, padding, dilation, stride): |
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channels = weight.size(0) |
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output_size = (input.size(0), channels) |
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for d in range(input.dim() - 2): |
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in_size = input.size(d + 2) |
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pad = padding[d] |
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kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 |
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stride_ = stride[d] |
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output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) |
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if not all(map(lambda s: s > 0, output_size)): |
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raise ValueError( |
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"convolution input is too small (output would be {})".format( |
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'x'.join(map(str, output_size)))) |
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return output_size |
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class ModulatedDeformConvFunction(Function): |
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@staticmethod |
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def forward(ctx, |
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input, |
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offset, |
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mask, |
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weight, |
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bias=None, |
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stride=1, |
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padding=0, |
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dilation=1, |
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groups=1, |
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deformable_groups=1): |
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ctx.stride = stride |
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ctx.padding = padding |
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ctx.dilation = dilation |
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ctx.groups = groups |
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ctx.deformable_groups = deformable_groups |
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ctx.with_bias = bias is not None |
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if not ctx.with_bias: |
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bias = input.new_empty(1) |
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if not input.is_cuda: |
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raise NotImplementedError |
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if weight.requires_grad or mask.requires_grad or offset.requires_grad \ |
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or input.requires_grad: |
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ctx.save_for_backward(input, offset, mask, weight, bias) |
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output = input.new_empty( |
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ModulatedDeformConvFunction._infer_shape(ctx, input, weight)) |
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ctx._bufs = [input.new_empty(0), input.new_empty(0)] |
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deform_conv_cuda.modulated_deform_conv_cuda_forward( |
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input, weight, bias, ctx._bufs[0], offset, mask, output, |
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ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride, |
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ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, |
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ctx.groups, ctx.deformable_groups, ctx.with_bias) |
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return output |
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@staticmethod |
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def backward(ctx, grad_output): |
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if not grad_output.is_cuda: |
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raise NotImplementedError |
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input, offset, mask, weight, bias = ctx.saved_tensors |
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grad_input = torch.zeros_like(input) |
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grad_offset = torch.zeros_like(offset) |
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grad_mask = torch.zeros_like(mask) |
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grad_weight = torch.zeros_like(weight) |
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grad_bias = torch.zeros_like(bias) |
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deform_conv_cuda.modulated_deform_conv_cuda_backward( |
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input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1], |
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grad_input, grad_weight, grad_bias, grad_offset, grad_mask, |
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grad_output, weight.shape[2], weight.shape[3], ctx.stride, |
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ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, |
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ctx.groups, ctx.deformable_groups, ctx.with_bias) |
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if not ctx.with_bias: |
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grad_bias = None |
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return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, |
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None, None, None, None, None) |
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@staticmethod |
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def _infer_shape(ctx, input, weight): |
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n = input.size(0) |
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channels_out = weight.size(0) |
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height, width = input.shape[2:4] |
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kernel_h, kernel_w = weight.shape[2:4] |
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height_out = (height + 2 * ctx.padding - |
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(ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1 |
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width_out = (width + 2 * ctx.padding - |
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(ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1 |
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return n, channels_out, height_out, width_out |
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deform_conv = DeformConvFunction.apply |
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modulated_deform_conv = ModulatedDeformConvFunction.apply |
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