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import math
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import torch
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import torch.nn as nn
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from torch.autograd import Function
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from torch.autograd.function import once_differentiable
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from torch.nn.modules.utils import _pair
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from ..utils import ext_loader
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ext_module = ext_loader.load_ext(
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'_ext', ['masked_im2col_forward', 'masked_col2im_forward'])
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class MaskedConv2dFunction(Function):
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@staticmethod
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def symbolic(g, features, mask, weight, bias, padding, stride):
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return g.op(
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'mmcv::MMCVMaskedConv2d',
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features,
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mask,
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weight,
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bias,
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padding_i=padding,
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stride_i=stride)
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@staticmethod
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def forward(ctx, features, mask, weight, bias, padding=0, stride=1):
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assert mask.dim() == 3 and mask.size(0) == 1
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assert features.dim() == 4 and features.size(0) == 1
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assert features.size()[2:] == mask.size()[1:]
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pad_h, pad_w = _pair(padding)
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stride_h, stride_w = _pair(stride)
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if stride_h != 1 or stride_w != 1:
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raise ValueError(
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'Stride could not only be 1 in masked_conv2d currently.')
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out_channel, in_channel, kernel_h, kernel_w = weight.size()
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batch_size = features.size(0)
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out_h = int(
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math.floor((features.size(2) + 2 * pad_h -
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(kernel_h - 1) - 1) / stride_h + 1))
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out_w = int(
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math.floor((features.size(3) + 2 * pad_w -
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(kernel_h - 1) - 1) / stride_w + 1))
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mask_inds = torch.nonzero(mask[0] > 0, as_tuple=False)
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output = features.new_zeros(batch_size, out_channel, out_h, out_w)
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if mask_inds.numel() > 0:
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mask_h_idx = mask_inds[:, 0].contiguous()
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mask_w_idx = mask_inds[:, 1].contiguous()
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data_col = features.new_zeros(in_channel * kernel_h * kernel_w,
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mask_inds.size(0))
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ext_module.masked_im2col_forward(
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features,
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mask_h_idx,
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mask_w_idx,
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data_col,
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kernel_h=kernel_h,
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kernel_w=kernel_w,
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pad_h=pad_h,
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pad_w=pad_w)
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masked_output = torch.addmm(1, bias[:, None], 1,
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weight.view(out_channel, -1), data_col)
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ext_module.masked_col2im_forward(
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masked_output,
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mask_h_idx,
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mask_w_idx,
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output,
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height=out_h,
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width=out_w,
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channels=out_channel)
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return output
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@staticmethod
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@once_differentiable
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def backward(ctx, grad_output):
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return (None, ) * 5
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masked_conv2d = MaskedConv2dFunction.apply
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class MaskedConv2d(nn.Conv2d):
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"""A MaskedConv2d which inherits the official Conv2d.
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The masked forward doesn't implement the backward function and only
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supports the stride parameter to be 1 currently.
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"""
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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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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bias=True):
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super(MaskedConv2d,
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self).__init__(in_channels, out_channels, kernel_size, stride,
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padding, dilation, groups, bias)
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def forward(self, input, mask=None):
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if mask is None:
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return super(MaskedConv2d, self).forward(input)
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else:
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return masked_conv2d(input, mask, self.weight, self.bias,
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self.padding)
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