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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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class MultiScaleTridentConv(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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strides=1,
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paddings=0,
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dilations=1,
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dilation=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(MultiScaleTridentConv, 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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self.dilation = dilation
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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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if isinstance(strides, int):
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strides = [strides] * 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.strides = [_pair(stride) for stride in strides]
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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.strides)}) == 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 self.training or self.test_branch_idx == -1:
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outputs = [
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F.conv2d(input, self.weight, self.bias, stride, padding, self.dilation, self.groups)
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for input, stride, padding in zip(inputs, self.strides, 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.strides[self.test_branch_idx] if self.test_branch_idx == -1 else self.strides[-1],
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self.paddings[self.test_branch_idx] if self.test_branch_idx == -1 else self.paddings[-1],
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self.dilation,
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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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