"""
Copyright 2019 Johns Hopkins University (Author: Jesus Villalba)
Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
import torch.nn as nn
from torch.nn import Conv2d, BatchNorm2d, Dropout2d
import torch.nn.functional as nnf
from ..layers import ActivationFactory as AF
[docs]def _conv3x3(in_channels, out_channels, stride=1, groups=1, dilation=1, bias=False):
"""3x3 convolution with padding"""
return nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=bias,
dilation=dilation,
)
[docs]def _conv1x1(in_channels, out_channels, stride=1, bias=False):
"""1x1 convolution"""
return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=bias)
def _make_downsample(in_channels, out_channels, stride, norm_layer, norm_before):
if norm_before:
return nn.Sequential(
_conv1x1(in_channels, out_channels, stride, bias=False),
norm_layer(out_channels),
)
return _conv1x1(in_channels, out_channels, stride, bias=True)
[docs]class ResNetBasicBlock(nn.Module):
expansion = 1
# __constants__ = ['downsample']
[docs] def __init__(
self,
in_channels,
channels,
activation={"name": "relu", "inplace": True},
stride=1,
dropout_rate=0,
groups=1,
dilation=1,
norm_layer=None,
norm_before=True,
):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self.in_channels = in_channels
self.channels = channels
bias = not norm_before
self.conv1 = _conv3x3(
in_channels, channels, stride, groups, dilation, bias=bias
)
self.bn1 = norm_layer(channels)
self.act1 = AF.create(activation)
self.conv2 = _conv3x3(channels, channels, groups=groups, bias=bias)
self.bn2 = norm_layer(channels)
self.act2 = AF.create(activation)
self.stride = stride
self.norm_before = norm_before
self.downsample = None
if stride != 1 or in_channels != channels:
self.downsample = _make_downsample(
in_channels, channels, stride, norm_layer, norm_before
)
self.dropout_rate = dropout_rate
self.dropout = None
if dropout_rate > 0:
self.dropout = Dropout2d(dropout_rate)
self.context = dilation + stride
self.downsample_factor = stride
@property
def out_channels(self):
return self.channels
[docs] def forward(self, x):
residual = x
x = self.conv1(x)
if self.norm_before:
x = self.bn1(x)
x = self.act1(x)
if not self.norm_before:
x = self.bn1(x)
x = self.conv2(x)
if self.norm_before:
x = self.bn2(x)
if self.downsample is not None:
residual = self.downsample(residual)
x += residual
x = self.act2(x)
if not self.norm_before:
x = self.bn2(x)
if self.dropout_rate > 0:
x = self.dropout(x)
return x
[docs]class ResNetBNBlock(nn.Module):
expansion = 4
# __constants__ = ['downsample']
[docs] def __init__(
self,
in_channels,
channels,
activation={"name": "relu", "inplace": True},
stride=1,
dropout_rate=0,
groups=1,
dilation=1,
norm_layer=None,
norm_before=True,
):
super().__init__()
self.in_channels = in_channels
self.channels = channels
if norm_layer is None:
norm_layer = nn.BatchNorm2d
bias = not norm_before
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = _conv1x1(in_channels, channels, bias=bias)
self.bn1 = norm_layer(channels)
self.conv2 = _conv3x3(channels, channels, stride, groups, dilation, bias=bias)
self.bn2 = norm_layer(channels)
self.conv3 = _conv1x1(channels, channels * self.expansion, bias=bias)
self.bn3 = norm_layer(channels * self.expansion)
self.act1 = AF.create(activation)
self.act2 = AF.create(activation)
self.act3 = AF.create(activation)
self.stride = stride
self.norm_before = norm_before
self.downsample = None
if stride != 1 or in_channels != channels * self.expansion:
self.downsample = _make_downsample(
in_channels, channels * self.expansion, stride, norm_layer, norm_before
)
self.dropout_rate = dropout_rate
self.dropout = None
if dropout_rate > 0:
self.dropout = Dropout2d(dropout_rate)
self.context = dilation
self.downsample_factor = stride
@property
def out_channels(self):
return self.channels * self.expansion
[docs] def forward(self, x):
residual = x
x = self.conv1(x)
if self.norm_before:
x = self.bn1(x)
x = self.act1(x)
if not self.norm_before:
x = self.bn1(x)
x = self.conv2(x)
if self.norm_before:
x = self.bn2(x)
x = self.act2(x)
if not self.norm_before:
x = self.bn2(x)
x = self.conv3(x)
if self.norm_before:
x = self.bn3(x)
if self.downsample is not None:
residual = self.downsample(residual)
x += residual
x = self.act3(x)
if not self.norm_before:
x = self.bn3(x)
if self.dropout_rate > 0:
x = self.dropout(x)
return x
[docs]class Interpolate(nn.Module):
[docs] def __init__(self, scale_factor, mode="nearest"):
super().__init__()
self.interp = nnf.interpolate
self.scale_factor = scale_factor
self.mode = mode
[docs] def forward(self, x):
x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode)
return x
[docs]class ResNetEndpointBlock(nn.Module):
[docs] def __init__(
self,
in_channels,
out_channels,
scale,
activation={"name": "relu", "inplace": True},
norm_layer=None,
norm_before=True,
):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
bias = not norm_before
self.out_channels = out_channels
self.in_channels = in_channels
self.norm_before = norm_before
if self.in_channels != self.out_channels:
self.conv = _conv1x1(in_channels, out_channels, bias=bias)
self.bn = norm_layer(out_channels)
self.act = AF.create(activation)
self.scale = scale
if self.scale > 1:
self.upsample = Interpolate(scale_factor=scale, mode="nearest")
[docs] def forward(self, x):
if self.in_channels != self.out_channels:
x = self.conv(x)
if self.norm_before:
x = self.bn(x)
x = self.act(x)
if not self.norm_before:
x = self.bn(x)
if self.scale > 1:
x = self.upsample(x)
return x