dipfa/StyleTransfer-main/models.py

# -*- coding: utf-8 -*- 
""" 
@File : models.py 
@Author: csc
@Date : 2022/6/23
"""
import torch
import torch.nn as nn
import torch.nn.functional as F

import torchvision
import torchvision.transforms as transforms
import torchvision.models as models

import numpy as np
from collections import defaultdict

from utils import *

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device = 'cpu'


class VGG(nn.Module):
    def __init__(self, features):
        super(VGG, self).__init__()
        self.features = features
        self.layer_name_mapping = {
            '3': "relu1_2",
            '8': "relu2_2",
            '15': "relu3_3",
            '22': "relu4_3"
        }
        for p in self.parameters():
            p.requires_grad = False

    def forward(self, x):
        outs = []
        for name, module in self.features._modules.items():
            x = module(x)
            if name in self.layer_name_mapping:
                outs.append(x)
        return outs


class VGG19(nn.Module):
    def __init__(self, features):
        super(VGG19, self).__init__()
        self.features = features
        self.layer_name_mapping = {
            '3': "relu1_2",
            '8': "relu2_2",
            '17': "relu3_4",
            '26': "relu4_4"
        }
        for p in self.parameters():
            p.requires_grad = False

    def forward(self, x):
        outs = []
        for name, module in self.features._modules.items():
            x = module(x)
            if name in self.layer_name_mapping:
                outs.append(x)
        return outs


class MyConv2D(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, groups=1):
        super(MyConv2D, self).__init__()
        self.weight = torch.zeros((out_channels, in_channels, kernel_size, kernel_size)).to(device)
        self.bias = torch.zeros(out_channels).to(device)

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = (kernel_size, kernel_size)
        self.stride = (stride, stride)
        self.groups = groups

    def forward(self, x):
        return F.conv2d(x, self.weight, self.bias, self.stride, groups=self.groups)

    def extra_repr(self):
        s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}'
             ', stride={stride}')
        return s.format(**self.__dict__)


class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv = nn.Sequential(
            *ConvLayer(channels, channels, kernel_size=3, stride=1),
            *ConvLayer(channels, channels, kernel_size=3, stride=1, relu=False)
        )

    def forward(self, x):
        return self.conv(x) + x


class ResNeXtBlock(nn.Module):
    def __init__(self, in_channels, out_channels, identity_downsample=None, stride=1, cardinality=32, width_per_group=64):
        super(ResNeXtBlock, self).__init__()
        width = int(out_channels * (width_per_group / 64)) * cardinality  # 转换通道数
        # self.conv1 = nn.Conv2d(in_channels, width, kernel_size=1, stride=1, padding=0)  # 不改变尺寸
        self.conv1 = MyConv2D(in_channels, width, kernel_size=1, stride=1)
        self.bn1 = nn.BatchNorm2d(width)
        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=cardinality)  # stride=2，尺寸减半；stride=1，尺寸不变
        # self.conv2 = MyConv2D(width, width, kernel_size=3, stride=stride, groups=cardinality)
        self.bn2 = nn.BatchNorm2d(width)
        # self.conv3 = nn.Conv2d(width, out_channels, kernel_size=1, stride=1, padding=0)  # 不改变尺寸
        self.conv3 = MyConv2D(width, out_channels, kernel_size=1, stride=1)
        self.bn3 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()
        self.identity_downsample = identity_downsample

    def forward(self, x):
        identity = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.conv3(x)
        x = self.bn3(x)

        if self.identity_downsample is not None:
            identity = self.identity_downsample(identity)
        # 残差连接
        # print(x.shape)
        # print(identity.shape)
        x += identity
        x = self.relu(x)

        return x


def ConvLayer(in_channels, out_channels, kernel_size=3, stride=1,
              upsample=None, instance_norm=True, relu=True, trainable=False):
    layers = []
    if upsample:
        layers.append(nn.Upsample(mode='nearest', scale_factor=upsample))
    layers.append(nn.ReflectionPad2d(kernel_size // 2))
    if trainable:
        layers.append(nn.Conv2d(in_channels, out_channels, kernel_size, stride))
    else:
        layers.append(MyConv2D(in_channels, out_channels, kernel_size, stride))
    if instance_norm:
        layers.append(nn.InstanceNorm2d(out_channels))
    if relu:
        layers.append(nn.ReLU())
    return layers


class TransformNet(nn.Module):
    def __init__(self, base=8, residuals='resnet'):
        super(TransformNet, self).__init__()
        self.base = base
        self.weights = []
        self.downsampling = nn.Sequential(
            *ConvLayer(3, base, kernel_size=9, trainable=True),
            *ConvLayer(base, base * 2, kernel_size=3, stride=2),
            *ConvLayer(base * 2, base * 4, kernel_size=3, stride=2),
        )
        if residuals == 'resnet':
            self.residuals = nn.Sequential(*[ResidualBlock(base * 4) for i in range(5)])
        elif residuals == 'resnext':
            self.residuals = nn.Sequential(ResNeXtBlock(base * 4, base * 4))
        self.upsampling = nn.Sequential(
            *ConvLayer(base * 4, base * 2, kernel_size=3, upsample=2),
            *ConvLayer(base * 2, base, kernel_size=3, upsample=2),
            *ConvLayer(base, 3, kernel_size=9, instance_norm=False, relu=False, trainable=True),
        )
        self.get_param_dict()

    def forward(self, X):
        y = self.downsampling(X)
        y = self.residuals(y)
        y = self.upsampling(y)
        return y

    def get_param_dict(self):
        """找出该网络所有 MyConv2D 层，计算它们需要的权值数量"""
        param_dict = defaultdict(int)

        def dfs(module, name):
            for name2, layer in module.named_children():
                dfs(layer, '%s.%s' % (name, name2) if name != '' else name2)
            if module.__class__ == MyConv2D:
                param_dict[name] += int(np.prod(module.weight.shape))
                param_dict[name] += int(np.prod(module.bias.shape))

        dfs(self, '')
        return param_dict

    def set_my_attr(self, name, value):
        # 下面这个循环是一步步遍历类似 residuals.0.conv.1 的字符串，找到相应的权值
        target = self
        for x in name.split('.'):
            if x.isnumeric():
                target = target.__getitem__(int(x))
            else:
                target = getattr(target, x)

        # 设置对应的权值
        n_weight = np.prod(target.weight.shape)
        target.weight = value[:n_weight].view(target.weight.shape)
        target.bias = value[n_weight:].view(target.bias.shape)

    def set_weights(self, weights, i=0):
        """输入权值字典，对该网络所有的 MyConv2D 层设置权值"""
        for name, param in weights.items():
            self.set_my_attr(name, weights[name][i])


class MetaNet(nn.Module):
    def __init__(self, param_dict, backbone='vgg16'):
        super(MetaNet, self).__init__()
        self.param_num = len(param_dict)
        if backbone == 'vgg16':
            self.hidden = nn.Linear(1920, 128 * self.param_num)
        elif backbone == 'vgg19':
            self.hidden = nn.Linear(1920, 128 * self.param_num)
        self.fc_dict = {}
        for i, (name, params) in enumerate(param_dict.items()):
            self.fc_dict[name] = i
            setattr(self, 'fc{}'.format(i + 1), nn.Linear(128, params))

    def forward(self, mean_std_features):
        hidden = F.relu(self.hidden(mean_std_features))
        filters = {}
        for name, i in self.fc_dict.items():
            fc = getattr(self, 'fc{}'.format(i + 1))
            filters[name] = fc(hidden[:, i * 128:(i + 1) * 128])
        return filters