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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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from torch.nn.modules.utils import _pair |
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from detectron2.layers.wrappers import _NewEmptyTensorOp |
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class TridentConv(nn.Module): |
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def __init__( |
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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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paddings=0, |
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dilations=1, |
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groups=1, |
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num_branch=1, |
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test_branch_idx=-1, |
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bias=False, |
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norm=None, |
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activation=None, |
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): |
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super(TridentConv, self).__init__() |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.kernel_size = _pair(kernel_size) |
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self.num_branch = num_branch |
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self.stride = _pair(stride) |
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self.groups = groups |
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self.with_bias = bias |
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if isinstance(paddings, int): |
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paddings = [paddings] * self.num_branch |
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if isinstance(dilations, int): |
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dilations = [dilations] * self.num_branch |
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self.paddings = [_pair(padding) for padding in paddings] |
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self.dilations = [_pair(dilation) for dilation in dilations] |
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self.test_branch_idx = test_branch_idx |
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self.norm = norm |
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self.activation = activation |
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assert len({self.num_branch, len(self.paddings), len(self.dilations)}) == 1 |
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self.weight = nn.Parameter( |
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torch.Tensor(out_channels, in_channels // groups, *self.kernel_size) |
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) |
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if bias: |
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self.bias = nn.Parameter(torch.Tensor(out_channels)) |
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else: |
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self.bias = None |
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nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") |
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if self.bias is not None: |
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nn.init.constant_(self.bias, 0) |
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def forward(self, inputs): |
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num_branch = self.num_branch if self.training or self.test_branch_idx == -1 else 1 |
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assert len(inputs) == num_branch |
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if inputs[0].numel() == 0: |
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output_shape = [ |
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(i + 2 * p - (di * (k - 1) + 1)) // s + 1 |
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for i, p, di, k, s in zip( |
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inputs[0].shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride |
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) |
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] |
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output_shape = [input[0].shape[0], self.weight.shape[0]] + output_shape |
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return [_NewEmptyTensorOp.apply(input, output_shape) for input in inputs] |
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if self.training or self.test_branch_idx == -1: |
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outputs = [ |
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F.conv2d(input, self.weight, self.bias, self.stride, padding, dilation, self.groups) |
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for input, dilation, padding in zip(inputs, self.dilations, self.paddings) |
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] |
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else: |
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outputs = [ |
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F.conv2d( |
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inputs[0], |
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self.weight, |
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self.bias, |
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self.stride, |
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self.paddings[self.test_branch_idx], |
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self.dilations[self.test_branch_idx], |
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self.groups, |
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) |
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] |
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if self.norm is not None: |
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outputs = [self.norm(x) for x in outputs] |
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if self.activation is not None: |
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outputs = [self.activation(x) for x in outputs] |
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return outputs |
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def extra_repr(self): |
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tmpstr = "in_channels=" + str(self.in_channels) |
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tmpstr += ", out_channels=" + str(self.out_channels) |
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tmpstr += ", kernel_size=" + str(self.kernel_size) |
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tmpstr += ", num_branch=" + str(self.num_branch) |
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tmpstr += ", test_branch_idx=" + str(self.test_branch_idx) |
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tmpstr += ", stride=" + str(self.stride) |
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tmpstr += ", paddings=" + str(self.paddings) |
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tmpstr += ", dilations=" + str(self.dilations) |
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tmpstr += ", groups=" + str(self.groups) |
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tmpstr += ", bias=" + str(self.with_bias) |
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return tmpstr |
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