Pytorch构建模型的技巧小结（1）

1、保存模型参数和加载模型参数

（A）保存参数

# 2 ways to save the net
torch.save(net1, 'net.pkl')  # save entire net
torch.save(net1.state_dict(), 'net_params.pkl')   # save only the parameters

（B）加载参数

# copy net1's parameters into net3
net3.load_state_dict(torch.load('net_params.pkl'))
prediction = net3(x)

上面出现的net1和net3都是nn.Module的实例。

2、模型参数的钳位

# Clip weights of discriminator
for p in discriminator.parameters():
      p.data.clamp_(-opt.clip_value, opt.clip_value)

p是Module（nn.Module）—— discriminator的参数。这段代码是实现WGAN时用到的。钳位不仅可以实现WGAN，而且它可以消除在训练中出现的nan情况，但钳位的大小很关键。

3、模型的CUDA化

在配有CUDA的训练过程中，模型和数据都需要加载到CUDA中，pytorch的张量有两种类型，以Float为例：用于CPU——torch.FloatTensor、用于CUDA——torch.cuda.FloatTensor，以下是完整列表：

$$\begin{array}{c|lc|r} n & \text{CPU} & \text{CUDA} & \text{Desc.}\\ \hline 1 & \text{torch.FloatTensor} & \text{torch.cuda.FloatTensor} & \text{32-bit floating point} \\ 2 & \text{torch.DoubleTensor} & \text{torch.cuda.DoubleTensor} & \text{64-bit floating point} \\ 3 & \text{N/A} & \text{torch.cuda.HalfTensor} & \text{16-bit floating point} \\ 4 & \text{torch.ByteTensor} & \text{torch.cuda.ByteTensor} & \text{8-bit integer (unsigned)} \\ 5 & \text{torch.CharTensor} & \text{torch.cuda.CharTensor} & \text{8-bit integer (signed)} \\ 6 & \text{torch.ShortTensor} & \text{torch.cuda.ShortTensor} & \text{16-bit integer (signed)} \\ 7 & \text{torch.IntTensor} & \text{torch.cuda.IntTensor} & \text{32-bit integer (signed)} \\ 8 & \text{torch.LongTensor} & \text{torch.cuda.LongTensor} & \text{64-bit integer (signed)} \\ \end{array}$$
CPU中Tensor经常需要与CUDA中Tensor交换，交换的方法如下：
方法一：

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

...2.
def to_cuda(x):
    return x.to(DEVICE)

'''3...模型的CUDA化'''
D = to_cuda(Discriminator())
G = to_cuda(Generator())

'''4...输入数据的CUDA化'''
x = to_cuda(images)

'''5...两个CUDA化的模型结合'''
x_outputs = D(x)

"""6...从CUDA回到CPU"""
def get_sample_image(G, n_noise=100):
    """
        save sample 100 images
    """
    for num in range(10):
        for i in range(10):
            z = to_cuda(torch.randn(1, n_noise))
            y_hat = G(z)
            line_img = torch.cat((line_img, y_hat.view(28, 28)), dim=1) if i > 0 else y_hat.view(28, 28)
        all_img = torch.cat((all_img, line_img), dim=0) if num > 0 else line_img
    img = all_img.cpu().data.numpy()
    return img

方法二：使用 .cuda() 和 .cpu()

# setup input tensors
x = torch.FloatTensor(opt.batch_size, nc, opt.image_size, opt.image_size)
z = torch.FloatTensor(opt.batch_size, nz, 1, 1)
noise = torch.FloatTensor(opt.batch_size, 1, 1, 1)

if opt.cuda:
    netGx.cuda(), netGz.cuda()
    netDx.cuda(), netDz.cuda(), netDxz.cuda()
    x, z, noise = x.cuda(), z.cuda(), noise.cuda()

从cuda中回来：

def test(dataloader, epoch):
    real_cpu_first, _ = iter(dataloader).next()
    real_cpu_first = real_cpu_first.mul(0.5).add(0.5)  # denormalize

    if opt.cuda:
        real_cpu_first = real_cpu_first.cuda()

    netGx.eval(), netGz.eval()  # switch to test mode
    latent = netGz(Variable(real_cpu_first, volatile=True))

    # removes last sigmoid activation to visualize reconstruction correctly
    mu, sigma = latent[:, :opt.nz], latent[:, opt.nz:].exp()
    recon = netGx(mu + sigma)

    vutils.save_image(recon.data, '{0}/reconstruction.png'.format(opt.experiment))
    vutils.save_image(real_cpu_first, '{0}/real_samples.png'.format(opt.experiment))

4、dataloader的逐次迭代

real_cpu_first, _ = iter(dataloader).next()

该方法可以用在单元测试上，检查dataloader的输出数据。以下是显示dataloader一次批处理图像的方法：

def imshow(inp, title=None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated


# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)

imshow(out, title=[class_names[x] for x in classes])

5、构建一个模型的装配模式

class CNN(nn.Module):
    def __init__(self, nc, input_size, hparams, ngpu=1, leaky_slope=0.01, std=0.01):
        super(CNN, self).__init__()
        self.ngpu = ngpu  # num of gpu's to use
        self.leaky_slope = leaky_slope  # slope for leaky_relu activation
        self.std = std  # standard deviation for weights initialization
        self.input_size = input_size  # expected input size

        main = nn.Sequential()
        in_feat, num = nc, 0
        for op, k, s, out_feat, b, bn, dp, h in hparams:
            # add operation: conv2d or convTranspose2d
            if op == 'conv2d':
                main.add_module(
                    '{0}.pyramid.{1}-{2}.conv'.format(num, in_feat, out_feat),
                    nn.Conv2d(in_feat, out_feat, k, s, 0, bias=b))
            elif op == 'convt2d':
                main.add_module(
                    '{0}.pyramid.{1}-{2}.convt'.format(num,in_feat, out_feat),
                    nn.ConvTranspose2d(in_feat, out_feat, k, s, 0, bias=b))
            else:
                raise Exception('Not supported operation: {0}'.format(op))
            num += 1
            # add batch normalization layer
            if bn:
                main.add_module(
                    '{0}.pyramid.{1}-{2}.batchnorm'.format(num, in_feat, out_feat),
                    nn.BatchNorm2d(out_feat))
                num += 1
            # add dropout layer
            main.add_module(
                '{0}.pyramid.{1}-{2}.dropout'.format(num, in_feat, out_feat),
                nn.Dropout2d(p=dp))
            num += 1
            # add activation
            if h == 'leaky_relu':
                main.add_module(
                    '{0}.pyramid.{1}-{2}.leaky_relu'.format(num, in_feat, out_feat),
                    nn.LeakyReLU(self.leaky_slope, inplace=True))
            elif h == 'sigmoid':
                main.add_module(
                    '{0}.pyramid.{1}-{2}.sigmoid'.format(num, in_feat, out_feat),
                    nn.Sigmoid())
            elif h == 'maxout':
                # TODO: implement me
                # https://github.com/IshmaelBelghazi/ALI/blob/master/ali/bricks.py#L338-L380
                raise NotImplementedError('Maxout is not implemented.')
            elif h == 'relu':
                main.add_module(
                    '{0}.pyramid.{1}-{2}.relu'.format(num, in_feat, out_feat),
                    nn.ReLU(inplace=True))
            elif h == 'tanh':
                main.add_module(
                    '{0}.pyramid.{1}-{2}.tanh'.format(num, in_feat, out_feat),
                    nn.Tanh())
            elif h == 'linear':
                num -= 1  # 'Linear' do nothing
            else:
                raise Exception('Not supported activation: {0}'.format(h))
            num += 1
            in_feat = out_feat
        self.main = main

        # initialize weights
        for m in self.modules():
            if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
                m.weight.data.normal_(0.0, self.std)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.normal_(1.0, self.std)
                m.bias.data.zero_()

    def forward(self, input):
        assert input.size(2) == self.input_size,\
            'Wrong input size: {0}. Expected {1}'.format(input.size(2),
                                                         self.input_size)
        if self.ngpu > 1 and isinstance(input.data, torch.cuda.FloatTensor):
            gpu_ids = range(self.ngpu)
            output = nn.parallel.data_parallel(self.main, input, gpu_ids)
        else:
            output = self.main(input)
        return output

它的调用模式：

def create_svhn_gz(nz=256, ngpu=1):
    hparams = [
        # op // kernel // strides // fmaps // conv. bias // batch_norm // dropout // nonlinearity
        ['conv2d', 5, 1,   32, False, True, 0.0, 'leaky_relu'],
        ['conv2d', 4, 2,   64, False, True, 0.0, 'leaky_relu'],
        ['conv2d', 4, 1,  128, False, True, 0.0, 'leaky_relu'],
        ['conv2d', 4, 2,  256, False, True, 0.0, 'leaky_relu'],
        ['conv2d', 4, 1,  512, False, True, 0.0, 'leaky_relu'],
        ['conv2d', 1, 1,  512, False, True, 0.0, 'leaky_relu'],
        ['conv2d', 1, 1, 2*nz, True, False, 0.0, 'linear'],
    ]
    return CNN(3, 32, hparams, ngpu)

装配模式完成模型构建、参数设置和和参数的初始化。

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6、对模型中不同参数进行不同的优化

在GAN中，生成器与判别器的参数不是同时优化的，需要交替进行，我们使用torch.optim实现了类似spring的横切功能：
A）定义两个optim，分别掌管不同的模型参数

# setup optimizer
dis_params = chain(netDx.parameters(), netDz.parameters(), netDxz.parameters())
gen_params = chain(netGx.parameters(), netGz.parameters())

kwargs_adam = {'lr': opt.lr, 'betas': (opt.beta1, opt.beta2)}
optimizerD = optim.Adam(dis_params, **kwargs_adam)
optimizerG = optim.Adam(gen_params, **kwargs_adam)

B）交替调用优化器

D_loss = compute_loss(batch_size, d_loss=True)
G_loss = compute_loss(batch_size, d_loss=False)

for p in netGx.parameters():
    p.requires_grad = False  # to avoid computation
for p in netGz.parameters():
    p.requires_grad = False  # to avoid computation
for p in netDx.parameters():
    p.requires_grad = True  # to avoid computation
for p in netDz.parameters():
    p.requires_grad = True  # to avoid computation
for p in netDxz.parameters():
    p.requires_grad = True  # to avoid computation

optimizerD.zero_grad()
D_loss.backward()
optimizerD.step()  # Apply optimization step

for p in netGx.parameters():
    p.requires_grad = True  # to avoid computation
for p in netGz.parameters():
    p.requires_grad = True  # to avoid computation
for p in netDx.parameters():
    p.requires_grad = False  # to avoid computation
for p in netDz.parameters():
    p.requires_grad = False  # to avoid computation
for p in netDxz.parameters():
    p.requires_grad = False  # to avoid computation

optimizerG.zero_grad()
G_loss.backward()
optimizerG.step()  # Apply optimization step

7、DataLoader的构建

1. 继承自torch.utils.data.Dataset
2. 实现三个方法：
   1）init
   2）getitem(index)
   3）len

class CustomDataset(data.Dataset):#需要继承data.Dataset
    def __init__(self):
        # TODO
        # 1. Initialize file path or list of file names.
        pass
    def __getitem__(self, index):
        # TODO
        # 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
        # 2. Preprocess the data (e.g. torchvision.Transform).
        # 3. Return a data pair (e.g. image and label).
        #这里需要注意的是，第一步：read one data，是一个data
        pass
    def __len__(self):
        # You should change 0 to the total size of your dataset.
        return 0

然后Dataset作为Dataloader的输入参数。我们用枚举来调用Dataloader，或用Next()调用Dataloader。

dataloader = torch.utils.data.DataLoader(
    ListDataset(train_path),
    batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
....
for batch_i, (_, imgs, targets) in enumerate(data loader):
    ....

8、一些函数的应用

Pytorch中view()、squeeze()、unsqueeze()、torch.max()函数经常用：
1、view()函数作用是将一个多行的Tensor,拼接成一行
2、squeeze(0)表示如果第一维度值为1，则去掉，否则不变。
3、unsqueeze()与squeeze()作用相反，即增加一维
4、torch.max()返回两个结果，第一个是最大值，第二个是对应的索引值；第二个参数 0 代表按列取最大值并返回对应的行索引值，1 代表按行取最大值并返回对应的列索引值。
参考： https://blog.csdn.net/lanse_zhicheng/article/details/79148678
5、contiguous：view只能用在contiguous的variable上。如果在view之前用了transpose, permute等，需要用contiguous()来返回一个contiguous copy