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# Copyright 2022 Dakewe Biotech Corporation. All Rights Reserved.
# 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
#
# http://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 math
from typing import Any
import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F_torch
from torchvision import models
from torchvision import transforms
from torchvision.models.feature_extraction import create_feature_extractor
__all__ = [
"SRResNet", "Discriminator",
"srresnet_x4", "discriminator", "content_loss",
]
class SRResNet(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
channels: int,
num_rcb: int,
upscale_factor: int
) -> None:
super(SRResNet, self).__init__()
# Low frequency information extraction layer
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, channels, (9, 9), (1, 1), (4, 4)),
nn.PReLU(),
)
# High frequency information extraction block
trunk = []
for _ in range(num_rcb):
trunk.append(_ResidualConvBlock(channels))
self.trunk = nn.Sequential(*trunk)
# High-frequency information linear fusion layer
self.conv2 = nn.Sequential(
nn.Conv2d(channels, channels, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(channels),
)
# zoom block
upsampling = []
if upscale_factor == 2 or upscale_factor == 4 or upscale_factor == 8:
for _ in range(int(math.log(upscale_factor, 2))):
upsampling.append(_UpsampleBlock(channels, 2))
elif upscale_factor == 3:
upsampling.append(_UpsampleBlock(channels, 3))
self.upsampling = nn.Sequential(*upsampling)
# reconstruction block
self.conv3 = nn.Conv2d(channels, out_channels, (9, 9), (1, 1), (4, 4))
# Initialize neural network weights
self._initialize_weights()
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
# Support torch.script function
def _forward_impl(self, x: Tensor) -> Tensor:
out1 = self.conv1(x)
out = self.trunk(out1)
out2 = self.conv2(out)
out = torch.add(out1, out2)
out = self.upsampling(out)
out = self.conv3(out)
out = torch.clamp_(out, 0.0, 1.0)
return out
def _initialize_weights(self) -> None:
for module in self.modules():
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.BatchNorm2d):
nn.init.constant_(module.weight, 1)
class Discriminator(nn.Module):
def __init__(self) -> None:
super(Discriminator, self).__init__()
self.features = nn.Sequential(
# input size. (3) x 96 x 96
nn.Conv2d(3, 64, (3, 3), (1, 1), (1, 1), bias=True),
nn.LeakyReLU(0.2, True),
# state size. (64) x 48 x 48
nn.Conv2d(64, 64, (3, 3), (2, 2), (1, 1), bias=False),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2, True),
nn.Conv2d(64, 128, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, True),
# state size. (128) x 24 x 24
nn.Conv2d(128, 128, (3, 3), (2, 2), (1, 1), bias=False),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, True),
nn.Conv2d(128, 256, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, True),
# state size. (256) x 12 x 12
nn.Conv2d(256, 256, (3, 3), (2, 2), (1, 1), bias=False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, True),
nn.Conv2d(256, 512, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, True),
# state size. (512) x 6 x 6
nn.Conv2d(512, 512, (3, 3), (2, 2), (1, 1), bias=False),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, True),
)
self.classifier = nn.Sequential(
nn.Linear(512 * 6 * 6, 1024),
nn.LeakyReLU(0.2, True),
nn.Linear(1024, 1),
)
def forward(self, x: Tensor) -> Tensor:
# Input image size must equal 96
assert x.shape[2] == 96 and x.shape[3] == 96, "Image shape must equal 96x96"
out = self.features(x)
out = torch.flatten(out, 1)
out = self.classifier(out)
return out
class _ResidualConvBlock(nn.Module):
def __init__(self, channels: int) -> None:
super(_ResidualConvBlock, self).__init__()
self.rcb = nn.Sequential(
nn.Conv2d(channels, channels, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(channels),
nn.PReLU(),
nn.Conv2d(channels, channels, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(channels),
)
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.rcb(x)
out = torch.add(out, identity)
return out
class _UpsampleBlock(nn.Module):
def __init__(self, channels: int, upscale_factor: int) -> None:
super(_UpsampleBlock, self).__init__()
self.upsample_block = nn.Sequential(
nn.Conv2d(channels, channels * upscale_factor * upscale_factor, (3, 3), (1, 1), (1, 1)),
nn.PixelShuffle(2),
nn.PReLU(),
)
def forward(self, x: Tensor) -> Tensor:
out = self.upsample_block(x)
return out
class _ContentLoss(nn.Module):
"""Constructs a content loss function based on the VGG19 network.
Using high-level feature mapping layers from the latter layers will focus more on the texture content of the image.
Paper reference list:
-`Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network <https://arxiv.org/pdf/1609.04802.pdf>` paper.
-`ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks <https://arxiv.org/pdf/1809.00219.pdf>` paper.
-`Perceptual Extreme Super Resolution Network with Receptive Field Block <https://arxiv.org/pdf/2005.12597.pdf>` paper.
"""
def __init__(
self,
feature_model_extractor_node: str,
feature_model_normalize_mean: list,
feature_model_normalize_std: list
) -> None:
super(_ContentLoss, self).__init__()
# Get the name of the specified feature extraction node
self.feature_model_extractor_node = feature_model_extractor_node
# Load the VGG19 model trained on the ImageNet dataset.
model = models.vgg19(weights=models.VGG19_Weights.IMAGENET1K_V1)
# Extract the thirty-sixth layer output in the VGG19 model as the content loss.
self.feature_extractor = create_feature_extractor(model, [feature_model_extractor_node])
# set to validation mode
self.feature_extractor.eval()
# The preprocessing method of the input data.
# This is the VGG model preprocessing method of the ImageNet dataset.
self.normalize = transforms.Normalize(feature_model_normalize_mean, feature_model_normalize_std)
# Freeze model parameters.
for model_parameters in self.feature_extractor.parameters():
model_parameters.requires_grad = False
def forward(self, sr_tensor: Tensor, gt_tensor: Tensor) -> Tensor:
# Standardized operations
sr_tensor = self.normalize(sr_tensor)
gt_tensor = self.normalize(gt_tensor)
sr_feature = self.feature_extractor(sr_tensor)[self.feature_model_extractor_node]
gt_feature = self.feature_extractor(gt_tensor)[self.feature_model_extractor_node]
# Find the feature map difference between the two images
loss = F_torch.mse_loss(sr_feature, gt_feature)
return loss
def srresnet_x4(**kwargs: Any) -> SRResNet:
model = SRResNet(upscale_factor=4, **kwargs)
return model
def discriminator() -> Discriminator:
model = Discriminator()
return model
def content_loss(**kwargs: Any) -> _ContentLoss:
content_loss = _ContentLoss(**kwargs)
return content_loss
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