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~
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/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
2021-04-19 10:08:06,925 - INFO - font search path ['/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/afm', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts']
2021-04-19 10:08:07,363 - INFO - generated new fontManager
- 真人数据来自seeprettyface。
- 数据预处理（详情见photo2cartoon项目）。
- 使用photo2cartoon项目生成真人数据对应的卡通数据。
# 解压数据
!unzip -q data/data79149/cartoon_A2B.zip -d data/# 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
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)]paired_dataset_train = PairedData('train')
paired_dataset_test = PairedData('test')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) # BatchNorm2D
)
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) # nn.BatchNorm2D
]
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 xclass NLayerDiscriminator(nn.Layer):
def __init__(self, input_nc=6, ndf=64):
super(NLayerDiscriminator, self).__init__()
self.layers = nn.Sequential(
nn.Conv2D(input_nc, ndf, kernel_size=4, stride=2, padding=1),
nn.LeakyReLU(0.2),
ConvBlock(ndf, ndf*2),
ConvBlock(ndf*2, ndf*4),
ConvBlock(ndf*4, ndf*8, stride=1),
nn.Conv2D(ndf*8, 1, kernel_size=4, stride=1, padding=1),
nn.Sigmoid()
)
def forward(self, input):
return self.layers(input)
class ConvBlock(nn.Layer):
# conv => batch norm => LeakyReLU
def __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1):
super(ConvBlock, self).__init__()
self.layers = nn.Sequential(
nn.Conv2D(in_dim, out_dim,kernel_size, stride, padding, bias_attr=False), # Conv2D
nn.BatchNorm2D(out_dim), # BatchNorm2D
nn.LeakyReLU(0.2) # LeakyReLU, leaky=0.2
)
def forward(self, x):
x = self.layers(x)
return xgenerator = UnetGenerator()
discriminator = NLayerDiscriminator()out = generator(paddle.ones([1, 3, 256, 256]))
print('生成器输出尺寸：', out.shape) # 应为[1, 3, 256, 256]
out = discriminator(paddle.ones([1, 6, 256, 256]))
print('鉴别器输出尺寸：', out.shape) # 应为[1, 1, 30, 30]/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:648: UserWarning: When training, we now always track global mean and variance.
"When training, we now always track global mean and variance.")
生成器输出尺寸： [1, 3, 256, 256]
鉴别器输出尺寸： [1, 1, 30, 30]
# 超参数
LR = 1e-4
BATCH_SIZE = 8
EPOCHS = 100
# 优化器
optimizerG = paddle.optimizer.Adam(
learning_rate=LR,
parameters=generator.parameters(),
beta1=0.5,
beta2=0.999)
optimizerD = paddle.optimizer.Adam(
learning_rate=LR,
parameters=discriminator.parameters(),
beta1=0.5,
beta2=0.999)
# 损失函数
bce_loss = nn.BCELoss()
l1_loss = nn.L1Loss()
# dataloader
data_loader_train = DataLoader(
paired_dataset_train,
batch_size=BATCH_SIZE,
shuffle=True,
drop_last=True
)
data_loader_test = DataLoader(
paired_dataset_test,
batch_size=BATCH_SIZE
)results_save_path = 'work/results'
os.makedirs(results_save_path, exist_ok=True) # 保存每个epoch的测试结果
weights_save_path = 'work/weights'
os.makedirs(weights_save_path, exist_ok=True) # 保存模型
for epoch in range(EPOCHS):
for data in tqdm(data_loader_train):
real_A, real_B = data
optimizerD.clear_grad()
# D([real_A, real_B])
real_AB = paddle.concat((real_A, real_B), 1)
d_real_predict = discriminator(real_AB)
d_real_loss = bce_loss(d_real_predict, paddle.ones_like(d_real_predict))
# D([real_A, fake_B])
fake_B = generator(real_A).detach()
fake_AB = paddle.concat((real_A, fake_B), 1)
d_fake_predict = discriminator(fake_AB)
d_fake_loss = bce_loss(d_fake_predict, paddle.zeros_like(d_fake_predict))
# train D
d_loss = (d_real_loss + d_fake_loss) / 2.
d_loss.backward()
optimizerD.step()
optimizerG.clear_grad()
# D([real_A, fake_B])
fake_B = generator(real_A)
fake_AB = paddle.concat((real_A, fake_B), 1)
g_fake_predict = discriminator(fake_AB)
g_bce_loss = bce_loss(g_fake_predict, paddle.ones_like(g_fake_predict))
g_l1_loss = l1_loss(fake_B, real_B) * 100.
g_loss = g_bce_loss + g_l1_loss
# train G
g_loss.backward()
optimizerG.step()
print(f'Epoch [{epoch+1}/{EPOCHS}] Loss D: {d_loss.numpy()}, Loss G: {g_loss.numpy()}')
if (epoch+1) % 10 == 0:
paddle.save(generator.state_dict(), os.path.join(weights_save_path, 'epoch'+str(epoch+1).zfill(3)+'.pdparams'))
# test
generator.eval()
with paddle.no_grad():
for data in data_loader_test:
real_A, real_B = data
break
fake_B = generator(real_A)
result = paddle.concat([real_A[:3], real_B[:3], fake_B[:3]], 3)
result = result.detach().numpy().transpose(0, 2, 3, 1)
result = np.vstack(result)
result = (result * 127.5 + 127.5).astype(np.uint8)
cv2.imwrite(os.path.join(results_save_path, 'epoch'+str(epoch+1).zfill(3)+'.png'), result)
generator.train() 0%| | 0/170 [00:00<?, ?it/s]/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:648: UserWarning: When training, we now always track global mean and variance.
"When training, we now always track global mean and variance.")
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-13-ea8ecf5a742c> in <module>
35 g_loss = g_bce_loss + g_l1_loss
36 # train G
---> 37 g_loss.backward()
38 optimizerG.step()
39
</opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/decorator.py:decorator-gen-239> in backward(self, retain_graph)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/wrapped_decorator.py in __impl__(func, *args, **kwargs)
23 def __impl__(func, *args, **kwargs):
24 wrapped_func = decorator_func(func)
---> 25 return wrapped_func(*args, **kwargs)
26
27 return __impl__
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/framework.py in __impl__(*args, **kwargs)
223 assert in_dygraph_mode(
224 ), "We only support '%s()' in dynamic graph mode, please call 'paddle.disable_static()' to enter dynamic graph mode." % func.__name__
--> 225 return func(*args, **kwargs)
226
227 return __impl__
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/varbase_patch_methods.py in backward(self, retain_graph)
175 retain_graph)
176 else:
--> 177 self._run_backward(framework._dygraph_tracer(), retain_graph)
178 else:
179 raise ValueError(
KeyboardInterrupt:
# 为生成器加载权重
last_weights_path = os.path.join(weights_save_path, sorted(os.listdir(weights_save_path))[-1])
print('加载权重:', last_weights_path)
model_state_dict = paddle.load(last_weights_path)
generator.load_dict(model_state_dict)
generator.eval()# 读取数据
test_names = os.listdir('data/cartoon_A2B/test')
img_name = np.random.choice(test_names)
img_A2B = cv2.imread('data/cartoon_A2B/test/'+img_name)
img_A = img_A2B[:, :256] # 真人照
img_B = img_A2B[:, 256:] # 卡通图
g_input = img_A.astype('float32') / 127.5 - 1 # 归一化
g_input = g_input[np.newaxis, ...].transpose(0, 3, 1, 2) # NHWC -> NCHW
g_input = paddle.to_tensor(g_input) # numpy -> tensor
g_output = generator(g_input)
g_output = g_output.detach().numpy() # tensor -> numpy
g_output = g_output.transpose(0, 2, 3, 1)[0] # NCHW -> NHWC
g_output = g_output * 127.5 + 127.5 # 反归一化
g_output = g_output.astype(np.uint8)
img_show = np.hstack([img_A, g_output])[:,:,::-1]
plt.figure(figsize=(8, 8))
plt.imshow(img_show)
plt.show()