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