使用photo2cartoon项目生成真人数据对应的人像卡通化.zip

# Day 3 作业--Pixel2Pixel：人像卡通化 经过今天的学习，相信大家对图像翻译、风格迁移有了一定的了解啦，是不是也想自己动手来实现下呢？ 那么，为了满足大家动手实践的愿望，同时为了巩固大家学到的知识，我们Day 3的作业便是带大家完成一遍课程讲解过的应用--**Pixel2Pixel：人像卡通化** 在本次作业中，大家需要做的是：**补齐代码，跑通训练，提交一张卡通化的成品图，动手完成自己的第一个人像卡通化的应用~**  ## 准备工作：引入依赖 & 数据准备 ```python import paddle import paddle.nn as nn from paddle.io import Dataset, DataLoader import os import cv2 import numpy as np from tqdm import tqdm import matplotlib.pyplot as plt %matplotlib inline ``` ```python /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import MutableMapping /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Iterable, Mapping /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Sized ``` ### 数据准备： - 真人数据来自[seeprettyface](http://www.seeprettyface.com/mydataset.html)。 - 数据预处理（详情见[photo2cartoon](https://github.com/minivision-ai/photo2cartoon)项目）。  - 使用[photo2cartoon](https://github.com/minivision-ai/photo2cartoon)项目生成真人数据对应的卡通数据。 ```python # 解压数据 !unzip -qao data/data79149/cartoon_A2B.zip -d data/ ``` ### 数据可视化 ```python # 训练数据统计 train_names = os.listdir('data/cartoon_A2B/train') print(f'训练集数据量: {len(train_names)}') # 测试数据统计 test_names = os.listdir('data/cartoon_A2B/test') print(f'测试集数据量: {len(test_names)}') # 训练数据可视化 imgs = [] for img_name in np.random.choice(train_names, 3, replace=False): imgs.append(cv2.imread('data/cartoon_A2B/train/'+img_name)) img_show = np.vstack(imgs)[:,:,::-1] plt.figure(figsize=(10, 10)) plt.imshow(img_show) plt.show() ``` 训练集数据量: 1361 测试集数据量: 100 /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working if isinstance(obj, collections.Iterator): /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working return list(data) if isinstance(data, collections.MappingView) else data  ```python class PairedData(Dataset): def __init__(self, phase): super(PairedData, self).__init__() self.img_path_list = self.load_A2B_data(phase) # 获取数据列表 self.num_samples = len(self.img_path_list) # 数据量 def __getitem__(self, idx): img_A2B = cv2.imread(self.img_path_list[idx]) # 读取一组数据 img_A2B = img_A2B.astype('float32') / 127.5 - 1. # 从0~255归一化至-1~1 img_A2B = img_A2B.transpose(2, 0, 1) # 维度变换HWC -> CHW img_A = img_A2B[..., :256] # 真人照 img_B = img_A2B[..., 256:] # 卡通图 return img_A, img_B def __len__(self): return self.num_samples @staticmethod def load_A2B_data(phase): assert phase in ['train', 'test'], "phase should be set within ['train', 'test']" # 读取数据集，数据中每张图像包含照片和对应的卡通画。 data_path = 'data/cartoon_A2B/'+phase return [os.path.join(data_path, x) for x in os.listdir(data_path)] ``` ```python paired_dataset_train = PairedData('train') paired_dataset_test = PairedData('test') ``` ## 第一步：搭建生成器 ### 请大家补齐空白处的代码，‘#’ 后是提示。 ```python class UnetGenerator(nn.Layer): def __init__(self, input_nc=3, output_nc=3, ngf=64): super(UnetGenerator, self).__init__() self.down1 = nn.Conv2D(input_nc, ngf, kernel_size=4, stride=2, padding=1) self.down2 = Downsample(ngf, ngf*2) self.down3 = Downsample(ngf*2, ngf*4) self.down4 = Downsample(ngf*4, ngf*8) self.down5 = Downsample(ngf*8, ngf*8) self.down6 = Downsample(ngf*8, ngf*8) self.down7 = Downsample(ngf*8, ngf*8) self.center = Downsample(ngf*8, ngf*8) self.up7 = Upsample(ngf*8, ngf*8, use_dropout=True) self.up6 = Upsample(ngf*8*2, ngf*8, use_dropout=True) self.up5 = Upsample(ngf*8*2, ngf*8, use_dropout=True) self.up4 = Upsample(ngf*8*2, ngf*8) self.up3 = Upsample(ngf*8*2, ngf*4) self.up2 = Upsample(ngf*4*2, ngf*2) self.up1 = Upsample(ngf*2*2, ngf) self.output_block = nn.Sequential( nn.ReLU(), nn.Conv2DTranspose(ngf*2, output_nc, kernel_size=4, stride=2, padding=1), nn.Tanh() ) def forward(self, x): d1 = self.down1(x) d2 = self.down2(d1) d3 = self.down3(d2) d4 = self.down4(d3) d5 = self.down5(d4) d6 = self.down6(d5) d7 = self.down7(d6) c = self.center(d7) x = self.up7(c, d7) x = self.up6(x, d6) x = self.up5(x, d5) x = self.up4(x, d4) x = self.up3(x, d3) x = self.up2(x, d2) x = self.up1(x, d1) x = self.output_block(x) return x class Downsample(nn.Layer): # LeakyReLU => conv => batch norm def __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1): super(Downsample, self).__init__() self.layers = nn.Sequential( nn.LeakyReLU(0.2), # LeakyReLU, leaky=0.2 nn.Conv2D(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False), # Conv2D nn.BatchNorm2D(out_dim) ) def forward(self, x): x = self.layers(x) return x class Upsample(nn.Layer): # ReLU => deconv => batch norm => dropout def __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1, use_dropout=False): super(Upsample, self).__init__() sequence = [ nn.ReLU(), # ReLU nn.Conv2DTranspose(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False), # Conv2DTranspose nn.BatchNorm2D(out_dim) ] if use_dropout: sequence.append(nn.Dropout(p=0.5)) self.layers = nn.Sequential(*sequence) def forward(self, x, skip): x = self.layers(x) x = paddle.concat([x, skip], axis=1) return x ``` ## 第二步：鉴别器的搭建 ### 请大家补齐空白处的代码，‘#’ 后是提示。 ```python class NLayerDiscriminator(nn.Layer): def __init__(self, input_nc=6, ndf=64):