【Pytorch】Visualization of Feature Maps（3）

学习参考来自：

• Image Style Transform–关于图像风格迁移的介绍
• github：https://github.com/wmn7/ML_Practice/tree/master/2019_06_03

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风格迁移

风格迁移出处：

《A Neural Algorithm of Artistic Style》（arXiv-2015）

风格迁移的实现

让 Random Image 在内容上可以接近 Content Image，在风格上可以接近 Style Image，当然， Random Image 可以初始化为 Content Image

导入基本库，数据读取

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from PIL import Image
import matplotlib.pyplot as plt

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

import numpy as np
import copy
import os

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


def image_loader(image_name, imsize):
    loader = transforms.Compose([
        transforms.Resize(imsize),  # scale images
        transforms.ToTensor()
    ])
    image = Image.open(image_name).convert("RGB")
    image = loader(image).unsqueeze(0)
    return image.to(device, torch.float)


def image_util(img_size=512, style_img="./1.jpg", content_img="./2.jpg"):
    "the size of style_img and contend_img should be same"
    imsize = img_size if torch.cuda.is_available() else 128  # use small size if no gpu
    style_img = image_loader(style_img, imsize)
    content_img = image_loader(content_img, imsize)

    print("Style Image Size:{}".format(style_img.size()))
    print("Content Image Size:{}".format(content_img.size()))

    assert style_img.size() == content_img.size(), "we need to import style and content images of the same size"
    return style_img, content_img

定义内容损失

"content loss"
class ContentLoss(nn.Module):
    def __init__(self, target):
        super(ContentLoss, self).__init__()
        self.target = target.detach()
        
    def forward(self, input):
        self.loss = F.mse_loss(input, self.target)
        return input

定义风格损失

def gram_matrix(input):
    a, b, c, d = input.size()  # N, C,
    features = input.view(a * b, c * d)
    G = torch.mm(features, features.t())
    return G.div(a * b * c * d)

Gram Matrix 最后输出大小只和 filter 的个数有关（channels），上面的例子输出为 3x3

Gram Matrix 可以表示出特征出现的关系（特征 f1、f2、f3 之间的关系）。

我们可以通过计算 Gram Matrix 的差，来计算两张图片风格上的差距

class StyleLoss(nn.Module):
    def __init__(self, target_feature):
        # we "detach" the target content from the tree used to dynamically
        # compute the gradient: this is stated value, not a variable .
        # Otherwise the forward method of the criterion will throw an error
        super(StyleLoss, self).__init__()
        self.target = gram_matrix(target_feature).detach()

    def forward(self, input):
        G = gram_matrix(input)
        self.loss = F.mse_loss(G, self.target)
        return input

写好前处理减均值，除方差

"based on VGG-16"
"put the normalization to the first layer"
class  Normalization(nn.Module):
    def __init__(self, mean, std):
        super(Normalization, self).__init__()
        # view the mean and std to make them [C,1,1] so that they can directly work with image Tensor of shape [B,C,H,W]
        self.mean = mean.view(-1, 1, 1)  # [3] -> [3, 1, 1]
        self.std = std.view(-1, 1, 1)

    def forward(self, img):
        return (img - self.mean) / self.std

定义网络，引入 loss

"modify to a style network"
def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
                               style_img, content_img,
                               content_layers,
                               style_layers):
    cnn = copy.deepcopy(cnn)
    # normalization module
    normalization = Normalization(normalization_mean, normalization_std).to(device)

    # just in order to have an iterable acess to or list of content / style
    # losses
    content_losses = []
    style_losses = []

    # assuming that cnn is a nn.Sequantial, so we make a new nn.Sequential to put
    # in modules that are supposed to be activated sequantially
    model = nn.Sequential(normalization)

    i = 0  # increment every time we see a conv
    for layer in cnn.children():
        if isinstance(layer, nn.Conv2d):
            i += 1
            name = "conv_{}".format(i)
        elif isinstance(layer, nn.ReLU):
            name = "relu_{}".format(i)
            layer = nn.ReLU(inplace=False)
        elif isinstance(layer, nn.MaxPool2d):
            name = "pool_{}".format(i)
        elif isinstance(layer, nn.BatchNorm2d):
            name = "bn_{}".format(i)
        else:
            raise RuntimeError("Unrecognized layer: {}".format(layer.__class__.__name__))
        model.add_module(name, layer)

        if name in content_layers:
            # add content loss
            target = model(content_img).detach()
            content_loss = ContentLoss(target)
            model.add_module("content_loss_{}".format(i), content_loss)
            content_losses.append(content_loss)

        if name in style_layers:
            # add style loss
            target_feature = model(style_img).detach()
            style_loss = StyleLoss(target_feature)
            model.add_module("style_loss_{}".format(i), style_loss)
            style_losses.append(style_loss)

    # now we trim off the layers afater the last content and style losses
    for i in range(len(model)-1, -1, -1):
        if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
            break
    model = model[:(i+1)]
    return model, style_losses, content_losses

def get_input_optimizer(input_img):
    optimizer = optim.LBFGS([input_img.requires_grad_()])
    return optimizer


def run_style_transfer(cnn, normalization_mean, normalization_std, content_img, style_img, input_img, content_layers,
                       style_layers, num_steps=50, style_weight=1000000, content_weight=1):
    print('Building the style transfer model..')
    model, style_losses, content_losses = get_style_model_and_losses(cnn, normalization_mean, normalization_std,
                                                                     style_img, content_img, content_layers,
                                                                     style_layers)
    optimizer = get_input_optimizer(input_img) # 网络不变，反向传播优化的是输入图片

    print('Optimizing..')
    run = [0]
    while run[0] <= num_steps:

        def closure():
            # correct the values of updated input image
            input_img.data.clamp_(0, 1)

            optimizer.zero_grad()
            model(input_img)  # 前向传播
            style_score = 0
            content_score = 0

            for sl in style_losses:
                style_score += sl.loss
            for cl in content_losses:
                content_score += cl.loss

            style_score *= style_weight
            content_score *= content_weight
            # loss为style loss 和 content loss的和
            loss = style_score + content_score
            loss.backward()  # 反向传播
            # 打印loss的变化情况
            run[0] += 1
            if run[0] % 50 == 0:
                print("run {}:".format(run))
                print('Style Loss : {:4f} Content Loss: {:4f}'.format(
                    style_score.item(), content_score.item()))
                print()

            return style_score + content_score

        # 进行参数优化
        optimizer.step(closure)

    # a last correction...
    # 数值范围的纠正, 使其范围在0-1之间
    input_img.data.clamp_(0, 1)

    return input_img

搭建完成，开始训练，仅优化更新 input image（get_input_optimizer），网络不更新

# 加载content image和style image
style_img,content_img = image_util(img_size=270, style_img="./style9.jpg", content_img="./content.jpg")  # [1, 3, 270, 270]
# input image使用content image
input_img = content_img.clone()
# 加载预训练好的模型
cnn = models.vgg19(pretrained=True).features.to(device).eval()
# 模型标准化的值
cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
# 定义要计算loss的层
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
# 模型进行计算
output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std,
                            content_img, style_img, input_img,
                            content_layers=content_layers_default,
                            style_layers=style_layers_default,
                            num_steps=300, style_weight=100000, content_weight=1)


image = output.cpu().clone()
image = image.squeeze(0)  # ([1, 3, 270, 270] -> [3, 270, 270])
unloader = transforms.ToPILImage()
image = unloader(image)
import cv2
image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
cv2.imwrite("t9.jpg", image)
torch.cuda.empty_cache()

"""VGG-19
Sequential(
  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (1): ReLU(inplace=True)
  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (3): ReLU(inplace=True)
  (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (6): ReLU(inplace=True)
  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (8): ReLU(inplace=True)
  (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (11): ReLU(inplace=True)
  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (13): ReLU(inplace=True)
  (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (15): ReLU(inplace=True)
  (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (17): ReLU(inplace=True)
  (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (20): ReLU(inplace=True)
  (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (22): ReLU(inplace=True)
  (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (24): ReLU(inplace=True)
  (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (26): ReLU(inplace=True)
  (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (29): ReLU(inplace=True)
  (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (31): ReLU(inplace=True)
  (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (33): ReLU(inplace=True)
  (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (35): ReLU(inplace=True)
  (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
"""


"""modify name, add loss layer
Sequential(
  (0): Normalization()
  (conv_1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (style_loss_1): StyleLoss()
  (relu_1): ReLU()
  (conv_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (style_loss_2): StyleLoss()
  (relu_2): ReLU()
  (pool_2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv_3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (style_loss_3): StyleLoss()
  (relu_3): ReLU()
  (conv_4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (content_loss_4): ContentLoss()
  (style_loss_4): StyleLoss()
  (relu_4): ReLU()
  (pool_4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv_5): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (style_loss_5): StyleLoss()
  (relu_5): ReLU()
  (conv_6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_6): ReLU()
  (conv_7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_7): ReLU()
  (conv_8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_8): ReLU()
  (pool_8): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv_9): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_9): ReLU()
  (conv_10): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_10): ReLU()
  (conv_11): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_11): ReLU()
  (conv_12): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_12): ReLU()
  (pool_12): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv_13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_13): ReLU()
  (conv_14): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_14): ReLU()
  (conv_15): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_15): ReLU()
  (conv_16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu_16): ReLU()
  (pool_16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
"""


"""after trim
Sequential(
  (0): Normalization()
  (conv_1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (style_loss_1): StyleLoss()
  (relu_1): ReLU()
  (conv_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (style_loss_2): StyleLoss()
  (relu_2): ReLU()
  (pool_2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv_3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (style_loss_3): StyleLoss()
  (relu_3): ReLU()
  (conv_4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (content_loss_4): ContentLoss()
  (style_loss_4): StyleLoss()
  (relu_4): ReLU()
  (pool_4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv_5): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (style_loss_5): StyleLoss()
)
"""

原图，花宝叽

不同风格

产生的结果

更直观的展示