本文最后更新于：2024年1月14日 晚上

前文介绍了 标准化流 ,本文做简单尝试加深理解。

标准化流

Flow 通过多层可逆映射的精巧变换实现分布之间的转换，通过这种方式拟合复杂的分布；

有些和多层的卷积拟合复杂函数相似的感觉，只是处理的是分布转换。

标准化是因为转换过程必须万分小心，每一步 Flow 的输入输出均为分布，即需要满足和为1 的约束，体积不变，我是这么理解的。

实现流程

原始项目：https://github.com/abdulfatir/normalizing-flows

从中摘出了一个简单示例。

引入 normflows 包

安装包

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pip install normflows

引入包

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import torch
import numpy as np
import normflows as nf

初始分布

用生成器表示，采样得到点位置和该点概率密度

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q0 = nf.distributions.DiagGaussian(2)

展示

由于是生成器，只能随机生成点，因此生成很多很多点，统计直方图作为初始分布示例

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# Plot initial flow distribution
z, _ = nfm.sample(num_samples=2 ** 20)
z_np = z.to('cpu').data.numpy()
plt.figure(figsize=(10, 10))
plt.hist2d(z_np[:, 0].flatten(), z_np[:, 1].flatten(), (grid_size, grid_size), range=[[-3, 3], [-3, 3]])
plt.show()

目标分布

目标分布为评估器，给定点，输出该点的概率密度

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target = nf.distributions.TwoModes(2, 0.1)

展示

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# Plot target distribution
grid_size = 200
xx, yy = torch.meshgrid(torch.linspace(-3, 3, grid_size), torch.linspace(-3, 3, grid_size))
z = torch.cat([xx.unsqueeze(2), yy.unsqueeze(2)], 2).view(-1, 2)
log_prob = target.log_prob(z.to(device)).to('cpu').view(*xx.shape)
prob = torch.exp(log_prob)

plt.figure(figsize=(10, 10))
plt.pcolormesh(xx, yy, prob)
plt.show()

Flows

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K = 16
flows = []
for i in range(K):
    flows += [nf.flows.Planar((2,))]

创建最简单的 Planar 流，

模型

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nfm = nf.NormalizingFlow(q0=q0, flows=flows, p=target)

训练

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# Train model
max_iter = 20000
num_samples = 2 * 20
anneal_iter = 10000
annealing = True
show_iter = 200


loss_hist = np.array([])

optimizer = torch.optim.Adam(nfm.parameters(), lr=1e-3, weight_decay=1e-4)
for it in tqdm(range(max_iter)):
    optimizer.zero_grad()
    if annealing:
        loss = nfm.reverse_kld(num_samples, beta=np.min([1., 0.01 + it / anneal_iter]))
    else:
        loss = nfm.reverse_kld(num_samples)
    loss.backward()
    optimizer.step()
    
    loss_hist = np.append(loss_hist, loss.to('cpu').data.numpy())

整体代码

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# Import required packages
import torch
import numpy as np
import normflows as nf
import mtutils as mt
from matplotlib import pyplot as plt
from tqdm import tqdm


K = 16
#torch.manual_seed(0)

# Move model on GPU if available
enable_cuda = True
device = torch.device('cuda' if torch.cuda.is_available() and enable_cuda else 'cpu')

flows = []
for i in range(K):
    flows += [nf.flows.Planar((2,))]
target = nf.distributions.TwoModes(2, 0.1)

## 初始分布
q0 = nf.distributions.DiagGaussian(2)
nfm = nf.NormalizingFlow(q0=q0, flows=flows, p=target)
nfm.to(device)



# Plot target distribution
grid_size = 200
xx, yy = torch.meshgrid(torch.linspace(-3, 3, grid_size), torch.linspace(-3, 3, grid_size))
z = torch.cat([xx.unsqueeze(2), yy.unsqueeze(2)], 2).view(-1, 2)
log_prob = target.log_prob(z.to(device)).to('cpu').view(*xx.shape)
prob = torch.exp(log_prob)

plt.figure(figsize=(10, 10))
plt.pcolormesh(xx, yy, prob)
plt.show()

# Plot initial flow distribution
z, _ = nfm.sample(num_samples=2 ** 20)
z_np = z.to('cpu').data.numpy()
plt.figure(figsize=(10, 10))
plt.hist2d(z_np[:, 0].flatten(), z_np[:, 1].flatten(), (grid_size, grid_size), range=[[-3, 3], [-3, 3]])
plt.show()



# Train model
max_iter = 20000
num_samples = 2 * 20
anneal_iter = 10000
annealing = True
show_iter = 200


loss_hist = np.array([])

optimizer = torch.optim.Adam(nfm.parameters(), lr=1e-3, weight_decay=1e-4)
for it in tqdm(range(max_iter)):
    optimizer.zero_grad()
    if annealing:
        loss = nfm.reverse_kld(num_samples, beta=np.min([1., 0.01 + it / anneal_iter]))
    else:
        loss = nfm.reverse_kld(num_samples)
    loss.backward()
    optimizer.step()
    
    loss_hist = np.append(loss_hist, loss.to('cpu').data.numpy())
    
    # Plot learned distribution
    if (it + 1) % show_iter == 0:
        torch.cuda.manual_seed(0)
        z, _ = nfm.sample(num_samples=2 ** 20)
        z_np = z.to('cpu').data.numpy()
        
        plt.figure(1, figsize=(10, 10))
        plt.hist2d(z_np[:, 0].flatten(), z_np[:, 1].flatten(), (grid_size, grid_size), range=[[-3, 3], [-3, 3]])
        # plt.pause(0.1)
        image = mt.convert_plt_to_rgb_image(plt)
        mt.cv_rgb_imwrite(image, f"res/{it}.jpg")


# plt.figure(figsize=(10, 10))
# plt.plot(loss_hist, label='loss')
# plt.legend()
# plt.show()

# Plot learned distribution
z, _ = nfm.sample(num_samples=2 ** 20)
z_np = z.to('cpu').data.numpy()
plt.figure(figsize=(10, 10))
plt.hist2d(z_np[:, 0].flatten(), z_np[:, 1].flatten(), (grid_size, grid_size), range=[[-3, 3], [-3, 3]])
plt.show()

实现效果

200 iter / frame

参考资料



文章链接：
https://www.zywvvd.com/notes/study/deep-learning/generation/normalizing-flows/flow-demo/flow-demo/

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Deep_Learning > Generation_Model
#Deep_Learning #Generation_Model
Normalizing Flow 理解与实现
https://www.zywvvd.com/notes/study/deep-learning/generation/normalizing-flows/flow-demo/flow-demo/
作者
Yiwei Zhang
发布于
2023年5月23日
许可协议