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50 lines
1.7 KiB
50 lines
1.7 KiB
5 years ago
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""" Conditional Convolution
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Hacked together by Ross Wightman
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"""
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import torch
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from torch import nn as nn
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from .conv2d_same import create_conv2d_pad
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def _split_channels(num_chan, num_groups):
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split = [num_chan // num_groups for _ in range(num_groups)]
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split[0] += num_chan - sum(split)
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return split
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class MixedConv2d(nn.ModuleDict):
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""" Mixed Grouped Convolution
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Based on MDConv and GroupedConv in MixNet impl:
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https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
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"""
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def __init__(self, in_channels, out_channels, kernel_size=3,
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stride=1, padding='', dilation=1, depthwise=False, **kwargs):
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super(MixedConv2d, self).__init__()
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kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
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num_groups = len(kernel_size)
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in_splits = _split_channels(in_channels, num_groups)
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out_splits = _split_channels(out_channels, num_groups)
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self.in_channels = sum(in_splits)
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self.out_channels = sum(out_splits)
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for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
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conv_groups = out_ch if depthwise else 1
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# use add_module to keep key space clean
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self.add_module(
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str(idx),
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create_conv2d_pad(
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in_ch, out_ch, k, stride=stride,
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padding=padding, dilation=dilation, groups=conv_groups, **kwargs)
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)
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self.splits = in_splits
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def forward(self, x):
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x_split = torch.split(x, self.splits, 1)
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x_out = [c(x_split[i]) for i, c in enumerate(self.values())]
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x = torch.cat(x_out, 1)
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return x
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