| | """ 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 |
| |
|
