[Pytorch系列-69]：生成对抗网络GAN - 图像生成开源项目pytorch-CycleGAN-and-pix2pix - test.py代码详解

Posted 2022-01-12 文火冰糖的硅基工坊

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本文网址：[Pytorch系列-66]：生成对抗网络GAN - 图像生成开源项目pytorch-CycleGAN-and-pix2pix - pix2pix test代码详解_文火冰糖（王文兵）的博客-CSDN博客

第1章 pix2pix测试代码

1.1 代码路径

.\\pytorch-CycleGAN-and-pix2pix\\test

1.2 关键命令行参数（以pix2pix为例）

-dataroot ./datasets/facades --direction BtoA --model pix2pix --name facades_pix2pix --verbose

其中 --verbose：表示打印网络架构

第2章测试代码主要流程

（1）获取命令行参数：opt = TestOptions().parse()

（2）设置test模式下命令命令行参数

（3）创建数据集：dataset = create_dataset(opt)

（4）创建模型pix2pix模型： model = create_model(opt)

（5）加载预预训练模型参数：model.setup(opt)

预训练模型的位置有opt参数指定。

print(model.model_names)
print(model.visual_names)

[Network G] Total number of parameters : 54.414 M
-----------------------------------------------
['G']
['real_A', 'fake_B', 'real_B']

备注：

pix2pix的test，只有G网络，没有D网络。
在pix2pix网络中，有三个关键点的图片：['real_A', 'fake_B', 'real_B']，其中real_A为输入图片，fake_B为创作生成的图片，real_B为与创作对应的真实图片，用它与创作图片进行比较和验证，以确定输出图片是否精准。

（6）构建web输出结构

（7）设置在评估模式：model.eval()

（8）读取数据集：for i, data in enumerate(dataset):

（9）unpack成对数据：model.set_input(data)

（10）根据输入图片，生成创作图片：model.test() , =》调用foward() 进行前向预测

而前向预测只有G网络，没有D网络：

    def forward(self):
        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
        self.fake_B = self.netG(self.real_A)  # G(A)

（11）获取输出图片：visuals = model.get_current_visuals()，图片包括：real_A（输入图片）, fake_B（创作图片）, real_B (用于人工比较）这三部分。

（12）存储图片：save_images =》 pytorch-CycleGAN-and-pix2pix\\results\\facades_pix2pix\\test_latest\\images\\xxx

（13）存储Website：webpage.save() =》pytorch-CycleGAN-and-pix2pix\\results\\facades_pix2pix\\test_latest\\index.htlm

第3章代码详解

   # 获取命令行参数
    opt = TestOptions().parse()  # get test options
    
    # hard-code some parameters for test
    opt.num_threads = 0   # test code only supports num_threads = 1
    opt.batch_size = 1    # test code only supports batch_size = 1
    opt.serial_batches = True  # disable data shuffling; comment this line if results on randomly chosen images are needed.
    opt.no_flip = True    # no flip; comment this line if results on flipped images are needed.
    opt.display_id = -1   # no visdom display; the test code saves the results to a html file.

    # 创建数据集
    dataset = create_dataset(opt)  # create a dataset given opt.dataset_mode and other options
    
    # 创建模型
    model = create_model(opt)      # create a model given opt.model and other options

    # 加载预训练模型
    model.setup(opt)               # regular setup: load and print networks; create schedulers

    # 构建web输出
    # create a website
    web_dir = os.path.join(opt.results_dir, opt.name, '_'.format(opt.phase, opt.epoch))  # define the website directory
    if opt.load_iter > 0:  # load_iter is 0 by default
        web_dir = ':s_iter:d'.format(web_dir, opt.load_iter)
    print('creating web directory', web_dir)
    webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))

    # test with eval mode. This only affects layers like batchnorm and dropout.
    # For [pix2pix]: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode.
    # For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.

    # 设置在评估模式
    if opt.eval:
        model.eval()

    # 读取数据集数据
    for i, data in enumerate(dataset):
        if i >= opt.num_test:  # only apply our model to opt.num_test images.
            break
            
        # unpack成对数据
        model.set_input(data)  # unpack data from data loader
        
        # 生成图片
        model.test()           # run inference
        
        # 获取输出图片：real_A（输入图片）, fake_B（创作图片）, real_B (用于人工比较）
        visuals = model.get_current_visuals()  # get image results
        
        # 获取存放图片的路径
        img_path = model.get_image_paths()     # get image paths
        
        # 每运行5次，打印一次提示
        if i % 5 == 0:  # save images to an HTML file
            print('processing (%04d)-th image... %s' % (i, img_path))
            
        #存储图片
        save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
    
    # 图片信息存放到web中，用于可视化显示
    webpage.save()  # save the HTML

----------------- Options ---------------
aspect_ratio: 1.0
batch_size: 1
checkpoints_dir: ./checkpoints
crop_size: 256
dataroot: ./datasets/facades     [default: None]
dataset_mode: aligned
direction: BtoA     [default: AtoB]
display_winsize: 256
epoch: latest
eval: False
gpu_ids: 0
init_gain: 0.02
init_type: normal
input_nc: 3
isTrain: False    [default: None]
load_iter: 0    [default: 0]
load_size: 256
max_dataset_size: inf
model: pix2pix    [default: test]
n_layers_D: 3
name: facades_pix2pix    [default: experiment_name]
ndf: 64
netD: basic
netG: unet_256
ngf: 64
no_dropout: False
no_flip: False
norm: batch
ntest: inf
num_test: 50
num_threads: 4
output_nc: 3
phase: test
preprocess: resize_and_crop
results_dir: ./results/
serial_batches: False
suffix:
verbose: True     [default: False]
----------------- End -------------------
dataset [AlignedDataset] was created
initialize network with normal
model [Pix2PixModel] was created
loading the model from ./checkpoints\\facades_pix2pix\\latest_net_G.pth
---------- Networks initialized -------------
DataParallel(
(module): UnetGenerator(
(model): UnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): ReLU(inplace=True)
(3): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.5, inplace=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.5, inplace=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.5, inplace=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(1024, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(512, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(2): ReLU(inplace=True)
(3): ConvTranspose2d(128, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(4): Tanh()
)
)
)
)
[Network G] Total number of parameters : 54.414 M
-----------------------------------------------
['G']
['real_A', 'fake_B', 'real_B']
creating web directory ./results/facades_pix2pix\\test_latest
C:\\ProgramData\\Anaconda3\\envs\\pytorch-gpu-os\\lib\\site-packages\\torchvision\\transforms\\transforms.py:287: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.
warnings.warn(
processing (0000)-th image... ['./datasets/facades\\\\test\\\\1.jpg']
processing (0005)-th image... ['./datasets/facades\\\\test\\\\103.jpg']
processing (0010)-th image... ['./datasets/facades\\\\test\\\\12.jpg']
processing (0015)-th image... ['./datasets/facades\\\\test\\\\17.jpg']
processing (0020)-th image... ['./datasets/facades\\\\test\\\\21.jpg']
processing (0025)-th image... ['./datasets/facades\\\\test\\\\26.jpg']
processing (0030)-th image... ['./datasets/facades\\\\test\\\\30.jpg']
processing (0035)-th image... ['./datasets/facades\\\\test\\\\35.jpg']
processing (0040)-th image... ['./datasets/facades\\\\test\\\\4.jpg']
processing (0045)-th image... ['./datasets/facades\\\\test\\\\44.jpg']