| """ PyTorch Mixed Convolution |
| |
| Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595) |
| |
| Hacked together by / Copyright 2020 Ross Wightman |
| """ |
|
|
| import torch |
| from torch import nn as nn |
|
|
| from .conv2d_same import create_conv2d_pad |
|
|
|
|
| def _split_channels(num_chan, num_groups): |
| split = [num_chan // num_groups for _ in range(num_groups)] |
| split[0] += num_chan - sum(split) |
| return split |
|
|
|
|
| class MixedConv2d(nn.ModuleDict): |
| """ Mixed Grouped Convolution |
| |
| Based on MDConv and GroupedConv in MixNet impl: |
| https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py |
| """ |
| def __init__(self, in_channels, out_channels, kernel_size=3, |
| stride=1, padding='', dilation=1, depthwise=False, **kwargs): |
| super(MixedConv2d, self).__init__() |
|
|
| kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size] |
| num_groups = len(kernel_size) |
| in_splits = _split_channels(in_channels, num_groups) |
| out_splits = _split_channels(out_channels, num_groups) |
| self.in_channels = sum(in_splits) |
| self.out_channels = sum(out_splits) |
| for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)): |
| conv_groups = in_ch if depthwise else 1 |
| |
| self.add_module( |
| str(idx), |
| create_conv2d_pad( |
| in_ch, out_ch, k, stride=stride, |
| padding=padding, dilation=dilation, groups=conv_groups, **kwargs) |
| ) |
| self.splits = in_splits |
|
|
| def forward(self, x): |
| x_split = torch.split(x, self.splits, 1) |
| x_out = [c(x_split[i]) for i, c in enumerate(self.values())] |
| x = torch.cat(x_out, 1) |
| return x |
|
|
