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# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import torch.nn.functional as F
import importlib
def class_for_name(module_name, class_name):
# load the module, will raise ImportError if module cannot be loaded
m = importlib.import_module(module_name)
return getattr(m, class_name)
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation,
padding_mode="reflect",
)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=1,
stride=stride,
bias=False,
padding_mode="reflect",
)
class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
groups=1,
base_width=64,
dilation=1,
norm_layer=None,
):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
# norm_layer = nn.InstanceNorm2d
if groups != 1 or base_width != 64:
raise ValueError("BasicBlock only supports groups=1 and base_width=64")
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes, track_running_stats=False, affine=True)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes, track_running_stats=False, affine=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
# This variant is also known as ResNet V1.5 and improves accuracy according to
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
groups=1,
base_width=64,
dilation=1,
norm_layer=None,
):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
# norm_layer = nn.InstanceNorm2d
width = int(planes * (base_width / 64.0)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width, track_running_stats=False, affine=True)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width, track_running_stats=False, affine=True)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(
planes * self.expansion, track_running_stats=False, affine=True
)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class conv(nn.Module):
def __init__(self, num_in_layers, num_out_layers, kernel_size, stride):
super(conv, self).__init__()
self.kernel_size = kernel_size
self.conv = nn.Conv2d(
num_in_layers,
num_out_layers,
kernel_size=kernel_size,
stride=stride,
padding=(self.kernel_size - 1) // 2,
padding_mode="reflect",
)
# self.bn = nn.InstanceNorm2d(
# num_out_layers, track_running_stats=False, affine=True
# )
self.bn = nn.BatchNorm2d(num_out_layers, track_running_stats=False, affine=True)
# self.bn = nn.LayerNorm(num_out_layers)
def forward(self, x):
return F.elu(self.bn(self.conv(x)), inplace=True)
class upconv(nn.Module):
def __init__(self, num_in_layers, num_out_layers, kernel_size, scale):
super(upconv, self).__init__()
self.scale = scale
self.conv = conv(num_in_layers, num_out_layers, kernel_size, 1)
def forward(self, x):
x = nn.functional.interpolate(
x, scale_factor=self.scale, align_corners=True, mode="bilinear"
)
return self.conv(x)
class ResUNet(nn.Module):
def __init__(
self,
encoder="resnet34",
coarse_out_ch=32,
fine_out_ch=32,
norm_layer=None,
coarse_only=False,
):
super(ResUNet, self).__init__()
assert encoder in [
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",
], "Incorrect encoder type"
if encoder in ["resnet18", "resnet34"]:
filters = [64, 128, 256, 512]
else:
filters = [256, 512, 1024, 2048]
self.coarse_only = coarse_only
if self.coarse_only:
fine_out_ch = 0
self.coarse_out_ch = coarse_out_ch
self.fine_out_ch = fine_out_ch
out_ch = coarse_out_ch + fine_out_ch
# original
layers = [3, 4, 6, 3]
if norm_layer is None:
norm_layer = nn.BatchNorm2d
# norm_layer = nn.InstanceNorm2d
self._norm_layer = norm_layer
self.dilation = 1
block = BasicBlock
replace_stride_with_dilation = [False, False, False]
self.inplanes = 64
self.groups = 1
self.base_width = 64
self.conv1 = nn.Conv2d(
3,
self.inplanes,
kernel_size=7,
stride=2,
padding=3,
bias=False,
padding_mode="reflect",
)
self.bn1 = norm_layer(self.inplanes, track_running_stats=False, affine=True)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
self.layer2 = self._make_layer(
block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
)
self.layer3 = self._make_layer(
block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
)
# decoder
self.upconv3 = upconv(filters[2], 128, 3, 2)
self.iconv3 = conv(filters[1] + 128, 128, 3, 1)
self.upconv2 = upconv(128, 64, 3, 2)
self.iconv2 = conv(filters[0] + 64, out_ch, 3, 1)
# fine-level conv
self.out_conv = nn.Conv2d(out_ch, out_ch, 1, 1)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(
planes * block.expansion, track_running_stats=False, affine=True
),
)
layers = []
layers.append(
block(
self.inplanes,
planes,
stride,
downsample,
self.groups,
self.base_width,
previous_dilation,
norm_layer,
)
)
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(
self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation,
norm_layer=norm_layer,
)
)
return nn.Sequential(*layers)
def skipconnect(self, x1, x2):
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, (diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2))
# for padding issues, see
# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
x = torch.cat([x2, x1], dim=1)
return x
def forward(self, x):
x = self.relu(self.bn1(self.conv1(x)))
x1 = self.layer1(x)
x2 = self.layer2(x1)
x3 = self.layer3(x2)
x = self.upconv3(x3)
x = self.skipconnect(x2, x)
x = self.iconv3(x)
x = self.upconv2(x)
x = self.skipconnect(x1, x)
x = self.iconv2(x)
x_out = self.out_conv(x)
return x_out
# if self.coarse_only:
# x_coarse = x_out
# x_fine = None
# else:
# x_coarse = x_out[:, : self.coarse_out_ch, :]
# x_fine = x_out[:, -self.fine_out_ch :, :]
# return x_coarse, x_fine
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