非kaggle内容 一些自学的模型
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import math
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import torch
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from torch import nn, optim
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from torch.utils.data import DataLoader
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from torchvision import datasets, transforms
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from torch.nn import functional as F
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import numpy as np
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import matplotlib.pyplot as plt
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class SinusoidalEmbedding(nn.Module):
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"""DDPM 标准的正弦位置编码，把时间步 t 编码成向量"""
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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def forward(self, t):
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half_dim = self.dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, device=t.device) * -emb)
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emb = t[:, None] * emb[None, :]
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return torch.cat([emb.sin(), emb.cos()], dim=-1)
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class ResidualBlock(nn.Module):
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def __init__(self, in_ch, out_ch, stride=1, time_dim=None):
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super().__init__()
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# 第一个卷积：可能改变通道数和步长
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self.conv1 = nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1,
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padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(out_ch)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(out_ch, out_ch, kernel_size=3, stride=1,
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padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(out_ch)
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# 时间嵌入投影：把 t 的嵌入映射到 out_ch，加到特征图上
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self.time_proj = None
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if time_dim is not None:
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self.time_proj = nn.Sequential(
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nn.SiLU(),
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nn.Linear(time_dim, out_ch),
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)
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# shortcut：如果维度变化，用1x1卷积（也可加BN）
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self.shortcut = nn.Sequential()
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if in_ch != out_ch :
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_ch, out_ch, kernel_size=1, stride=1, bias=False),
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nn.BatchNorm2d(out_ch)
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)
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def forward(self, x, t_emb=None):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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# 添加时间嵌入（FiLM 式相加注入）
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if t_emb is not None and self.time_proj is not None:
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out = out + self.time_proj(t_emb)[:, :, None, None]
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# 添加 shortcut
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out += self.shortcut(residual)
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out = self.relu(out) # 标准做法：最后加一次激活
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return out
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class DownSample(nn.Module):
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def __init__(self,ch):
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super().__init__()
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self.conv1=nn.Conv2d(ch,ch,2,2)
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def forward(self,x):
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return self.conv1(x)
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class UpSample(nn.Module):
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def __init__(self, ch):
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super().__init__()
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# 使用 ch//2 确保整数
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self.conv1 = nn.ConvTranspose2d(ch, ch//2, 4, 2, 1)
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def forward(self, x):
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return self.conv1(x)
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class UNet(nn.Module):
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def __init__(self, time_dim=64):
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super().__init__()
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self.time_emb = SinusoidalEmbedding(time_dim)
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self.time_mlp = nn.Sequential(
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nn.Linear(time_dim, time_dim * 4),
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nn.SiLU(),
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nn.Linear(time_dim * 4, time_dim * 4),
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)
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t_dim = time_dim * 4
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self.conv1 = nn.Conv2d(1, 32, 3, 1, 1)
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self.res1 = ResidualBlock(32, 64, time_dim=t_dim)
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self.down1 = DownSample(64)
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self.res2 = ResidualBlock(64, 128, time_dim=t_dim)
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self.down2 = DownSample(128)
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self.res3 = ResidualBlock(128, 256, time_dim=t_dim)
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self.down3 = DownSample(256)
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self.res4 = ResidualBlock(256, 512, time_dim=t_dim)
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# --- 上采样部分（重新设计通道匹配）---
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self.up1 = UpSample(512) # 512 -> 256
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self.res5 = ResidualBlock(512, 256, time_dim=t_dim) # 拼接后 512 -> 256
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self.up2 = UpSample(256) # 256 -> 128
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self.res6 = ResidualBlock(256, 128, time_dim=t_dim) # 拼接后 256 -> 128
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self.up3 = UpSample(128) # 128 -> 64
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self.res7 = ResidualBlock(128, 64, time_dim=t_dim) # 拼接后 128 -> 64
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self.res8 = ResidualBlock(64, 32, time_dim=t_dim)
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self.conv2 = nn.Conv2d(32, 1, 3, 1, 1)
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def forward(self, x, t):
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t_emb = self.time_mlp(self.time_emb(t)) # [b, time_dim*4]
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# ----- 下采样（保存跳跃连接）-----
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x1 = self.res1(self.conv1(x), t_emb) # [b, 64, 28, 28]
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x2 = self.res2(self.down1(x1), t_emb) # [b, 128, 14, 14]
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x3 = self.res3(self.down2(x2), t_emb) # [b, 256, 7, 7]
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x4 = self.res4(self.down3(x3), t_emb) # [b, 512, 3, 3]
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# ----- 上采样 1（3×3 → 7×7）-----
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x4_up = self.up1(x4) # [b, 256, 6, 6]
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x4_up = F.interpolate(x4_up, size=7, mode='bilinear') # [b, 256, 7, 7]
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x4_cat = torch.cat([x4_up, x3], dim=1) # [b, 512, 7, 7]
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x3_new = self.res5(x4_cat, t_emb) # [b, 256, 7, 7]
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# ----- 上采样 2（7×7 → 14×14）-----
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x3_up = self.up2(x3_new) # [b, 128, 14, 14] 尺寸恰好为14
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x3_cat = torch.cat([x3_up, x2], dim=1) # [b, 256, 14, 14]
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x2_new = self.res6(x3_cat, t_emb) # [b, 128, 14, 14]
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# ----- 上采样 3（14×14 → 28×28）-----
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x2_up = self.up3(x2_new) # [b, 64, 28, 28]
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x2_cat = torch.cat([x2_up, x1], dim=1) # [b, 128, 28, 28]
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x1_new = self.res7(x2_cat, t_emb) # [b, 64, 28, 28]
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# ----- 最终输出-----
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x1_new = self.res8(x1_new, t_emb) # [b, 32, 28, 28]
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out = self.conv2(x1_new) # [b, 1, 28, 28]
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return out
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if __name__=='__main__':
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# ---------- 数据加载 ----------
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batch_size = 16
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transform = transforms.Compose(
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[
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transforms.ToTensor(), # 将 [0,255] 转为 [0,1] 的 Tensor
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transforms.Normalize((0.5,), (0.5,)), #归一化到[-1,1]
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]
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)
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train_dataset = datasets.MNIST(
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root="./data", train=True, download=True, transform=transform
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)
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test_dataset = datasets.MNIST(
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root="./data", train=False, download=True, transform=transform
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)
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
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# ---------- DDPM 超参数 ----------
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timesteps = 600
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betas = torch.linspace(1e-4, 0.02, timesteps) # 线性采样 beta
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alphas = 1.0 - betas
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alpha_bar = torch.cumprod(alphas, dim=0) # \bar{alpha}_t
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def q_sample(x_0, t):
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"""前向扩散：q(x_t | x_0) = sqrt(alpha_bar_t) * x_0 + sqrt(1 - alpha_bar_t) * eps"""
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alpha_bar_t = alpha_bar[t][:, None, None, None]
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eps = torch.randn_like(x_0)
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x_t = torch.sqrt(alpha_bar_t) * x_0 + torch.sqrt(1 - alpha_bar_t) * eps
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return x_t, eps
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def sample(model, n=16, device="cpu"):
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"""简易采样：从纯噪声逐步去噪"""
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model.eval()
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x = torch.randn(n, 1, 28, 28, device=device)
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with torch.no_grad():
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for t in reversed(range(timesteps)):
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t_batch = torch.full((n,), t, device=device, dtype=torch.long)
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eps_pred = model(x, t_batch)
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alpha_t = alphas[t].to(device)
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alpha_bar_t = alpha_bar[t].to(device)
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x = (x - (1 - alpha_t) / torch.sqrt(1 - alpha_bar_t) * eps_pred) / torch.sqrt(alpha_t)
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if t > 0:
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x += torch.sqrt(betas[t].to(device)) * torch.randn_like(x)
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model.train()
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return x
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# ---------- 训练 ----------
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = UNet().to(device)
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optimizer = optim.Adam(model.parameters(), lr=1e-3)
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epochs = 20
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for epoch in range(epochs):
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total_loss = 0.0
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for x_0, _ in train_loader:
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x_0 = x_0.to(device)
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t = torch.randint(0, timesteps, (x_0.size(0),), device=device) # 随机采样时间
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x_t, eps = q_sample(x_0, t)
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eps_pred = model(x_t, t)
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loss = F.mse_loss(eps_pred, eps) # 预测噪声，MSE 损失
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss/len(train_loader):.4f}")
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# ---------- 采样可视化 ----------
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samples = sample(model, n=16, device=device).cpu().clamp(-1, 1)
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samples = (samples + 1) / 2 # [-1,1] -> [0,1]
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fig, axes = plt.subplots(4, 4, figsize=(8, 8))
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for i, ax in enumerate(axes.flat):
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ax.imshow(samples[i, 0], cmap="gray")
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ax.axis("off")
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plt.tight_layout()
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plt.savefig("samples.png")
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plt.show()
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