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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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