62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
import torch
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import torch.nn as nn
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from torchvision.datasets import MNIST
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import torchvision
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from torch.utils.data import DataLoader, Dataset
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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from ddpm import DDPM
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from unet import Unet
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BATCH_SIZE = 256
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ITERATION = 500
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TIME_EMB_DIM = 128
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DEVICE = torch.device('cuda')
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EPOCH_NUM = 500
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LEARNING_RATE = 1e-4
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def getMnistLoader():
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transform = torchvision.transforms.Compose([
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torchvision.transforms.ToTensor()
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])
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data = MNIST("./data", train=True, download=True, transform=transform)
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loader = DataLoader(data, batch_size=BATCH_SIZE, shuffle=True)
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return loader
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def train(loader, device, epoch_num, lr):
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model = Unet(TIME_EMB_DIM, DEVICE).to(device)
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ddpm = DDPM(BATCH_SIZE, ITERATION, 1e-4, 2e-2, device)
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criterion = nn.MSELoss()
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optimzer = torch.optim.Adam(model.parameters(), lr=lr)
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min_loss = 99
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for epoch in range(epoch_num):
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loss_sum = 0
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# progress = tqdm(total=len(loader))
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for x, y in loader:
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optimzer.zero_grad()
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x = x.to(DEVICE)
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time_seq = ddpm.get_time_seq(x.shape[0])
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x_t, noise = ddpm.get_x_t(x, time_seq)
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predict_noise = model(x_t, time_seq)
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loss = criterion(predict_noise, noise)
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loss_sum += loss.item()
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loss.backward()
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optimzer.step()
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# progress.update(1)
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print("Epoch {}/{}: With lr={}, batch_size={}, iteration={}. The best loss: {} - loss: {}".format(epoch, EPOCH_NUM, LEARNING_RATE, BATCH_SIZE, ITERATION, min_loss, loss_sum/len(loader)))
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if loss_sum/len(loader) < min_loss:
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min_loss = loss_sum/len(loader)
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print("save model: the best loss is {}".format(min_loss))
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torch.save(model.state_dict(), 'unet.pth')
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loader = getMnistLoader()
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train(loader, DEVICE, EPOCH_NUM, LEARNING_RATE) |