134 lines
3.4 KiB
Python
134 lines
3.4 KiB
Python
""" Depthwise Separable Conv Modules
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Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
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DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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from typing import Optional, Type, Union
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from torch import nn as nn
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from .create_conv2d import create_conv2d
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from .create_norm_act import get_norm_act_layer
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class SeparableConvNormAct(nn.Module):
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""" Separable Conv w/ trailing Norm and Activation
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: int = 3,
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stride: int = 1,
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dilation: int = 1,
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padding: str = '',
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bias: bool = False,
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channel_multiplier: float = 1.0,
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pw_kernel_size: int = 1,
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norm_layer: Type[nn.Module] = nn.BatchNorm2d,
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act_layer: Type[nn.Module] = nn.ReLU,
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apply_act: bool = True,
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drop_layer: Optional[Type[nn.Module]] = None,
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device=None,
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dtype=None,
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):
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dd = {'device': device, 'dtype': dtype}
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super().__init__()
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self.conv_dw = create_conv2d(
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in_channels,
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int(in_channels * channel_multiplier),
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kernel_size,
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stride=stride,
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dilation=dilation,
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padding=padding,
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depthwise=True,
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**dd,
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)
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self.conv_pw = create_conv2d(
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int(in_channels * channel_multiplier),
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out_channels,
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pw_kernel_size,
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padding=padding,
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bias=bias,
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**dd,
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)
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norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
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norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
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self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs, **dd)
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@property
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def in_channels(self):
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return self.conv_dw.in_channels
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@property
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def out_channels(self):
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return self.conv_pw.out_channels
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def forward(self, x):
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x = self.conv_dw(x)
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x = self.conv_pw(x)
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x = self.bn(x)
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return x
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SeparableConvBnAct = SeparableConvNormAct
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class SeparableConv2d(nn.Module):
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""" Separable Conv
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"""
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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=3,
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stride=1,
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dilation=1,
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padding='',
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bias=False,
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channel_multiplier=1.0,
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pw_kernel_size=1,
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device=None,
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dtype=None,
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):
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dd = {'device': device, 'dtype': dtype}
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super().__init__()
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self.conv_dw = create_conv2d(
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in_channels,
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int(in_channels * channel_multiplier),
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kernel_size,
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stride=stride,
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dilation=dilation,
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padding=padding,
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depthwise=True,
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**dd,
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)
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self.conv_pw = create_conv2d(
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int(in_channels * channel_multiplier),
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out_channels,
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pw_kernel_size,
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padding=padding,
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bias=bias,
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**dd,
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)
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@property
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def in_channels(self):
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return self.conv_dw.in_channels
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@property
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def out_channels(self):
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return self.conv_pw.out_channels
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def forward(self, x):
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x = self.conv_dw(x)
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x = self.conv_pw(x)
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return x
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