【深度学习】python之人工智能应用篇——视频生成技术

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torch.utils.data import DataLoader
from torchvision.datasets import CelebA
from torchvision.utils import save_image
# 定义生成器
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.Linear(100, 256),
            nn.ReLU(),
            nn.Linear(256, 512),
            nn.ReLU(),
            nn.Linear(512, 1024),
            nn.ReLU(),
            nn.Linear(1024, 784),
            nn.Tanh()
        )
    def forward(self, x):
        return self.main(x)
# 定义判别器
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Linear(784, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )
    def forward(self, x):
        return self.main(x)
# 训练函数
def train(generator, discriminator, dataloader, epochs=100):
    criterion = nn.BCELoss()
    optimizer_g = optim.Adam(generator.parameters(), lr=0.0002)
    optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0002)
    for epoch in range(epochs):
        for i, (real_images, _) in enumerate(dataloader):
            real_labels = torch.ones(real_images.size(0), 1)
            fake_labels = torch.zeros(real_images.size(0), 1)
            # 训练判别器
            optimizer_d.zero_grad()
            real_outputs = discriminator(real_images.view(real_images.size(0), -1))
            real_loss = criterion(real_outputs, real_labels)
            noise = torch.randn(real_images.size(0), 100)
            fake_images = generator(noise)
            fake_outputs = discriminator(fake_images.detach())
            fake_loss = criterion(fake_outputs, fake_labels)
            d_loss = real_loss + fake_loss
            d_loss.backward()
            optimizer_d.step()
            # 训练生成器
            optimizer_g.zero_grad()
            fake_outputs = discriminator(fake_images)
            g_loss = criterion(fake_outputs, real_labels)
            g_loss.backward()
            optimizer_g.step()
            if (i+1) % 100 == 0:
                print(f'Epoch [{epoch+1}/{epochs}], Step [{i+1}/{len(dataloader)}], d_loss: {d_loss.item()}, g_loss: {g_loss.item()}')
# 加载数据集
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
celeba_data = CelebA(root='./data', split='train', transform=transform, download=True)
dataloader = DataLoader(celeba_data, batch_size=64, shuffle=True)
# 初始化生成器和判别器
generator = Generator()
discriminator = Discriminator()
# 开始训练
train(generator, discriminator, dataloader)

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
# 参数设置
frame_width = 64  # 帧宽度
frame_height = 64  # 帧高度
total_frames = 100  # 总帧数
latent_dim = 100  # 隐空间维度，用于生成帧的随机向量
# 构建一个简单的生成模型
model = Sequential()
model.add(Dense(256, input_dim=latent_dim))
model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
model.add(Dense(512))
model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
model.add(Dense(frame_width * frame_height * 3))  # 输出为RGB像素值
# 编译模型
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.001))
# 假设我们有一个简化的“训练”过程，实际上这里是为了演示如何生成序列
# 实际的视频生成任务会涉及更复杂的模型和训练过程，例如使用循环神经网络(RNN)或生成对抗网络(GANs)
# 生成序列
def generate_video_sequence():
    frames = []
    for i in range(total_frames):
        noise = np.random.normal(0, 1, (1, latent_dim))
        generated_frame = model(noise)
        generated_frame = generated_frame.numpy().reshape(frame_height, frame_width, 3)
        frames.append(generated_frame)
    
    return frames
# 生成并显示视频帧序列
frames = generate_video_sequence()
# 为了可视化，我们只显示前几个帧
for i, frame in enumerate(frames[:10]):
    plt.figure(figsize=(5,5))
    plt.imshow(frame)
    plt.title(f'Frame {i}')
    plt.axis('off')
    plt.show()
# 注意：此代码未包含实际训练过程，仅为展示如何使用模型生成序列
# 在实际应用中，模型需要基于大量真实视频数据进行训练以学习生成连贯视频帧的能力