File size: 7,664 Bytes
6107278 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | from matplotlib import pyplot as plt
from config import config
from dataset import myDataset, myDiTDataset
from transform import myTransform, myDiTTransform
from torch.utils.data import DataLoader
from model import mySiTModel
from BBDMScheduler import BBDMScheduler
from torch.optim.lr_scheduler import MultiStepLR
from tqdm import tqdm
from datetime import date
import torch.nn.functional as F
import torch
import time
from monai.utils import set_determinism
from torch_ema import ExponentialMovingAverage
set_determinism(42)
def train():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 设置运行环境
train_file_list = "SZCH-X-Rays_trainset.txt"
test_file_list = "SZCH-X-Rays_valset.txt"
cxr_path = "SZCH-X-Rays-741/CXR"
bs_path = "SZCH-X-Rays-741/BS"
myTrainSet = myDiTDataset(train_file_list, cxr_path, bs_path,
myDiTTransform['trainTransform'])
myTestSet = myDataset(test_file_list, cxr_path, bs_path,
myTransform['testTransform'])
myTrainLoader = DataLoader(myTrainSet, batch_size=config.batch_size, shuffle=True)
myTestLoader = DataLoader(myTestSet, batch_size=config.batch_size, shuffle=False)
print("Number of batches in train set:", len(myTrainLoader))
print("Train set size:", len(myTrainSet))
print("Number of batches in test set:", len(myTestLoader))
print("Test set size:", len(myTestSet))
model = mySiTModel.to(device).train()
noise_scheduler = BBDMScheduler(num_train_timesteps=config.num_train_timesteps)
noise_scheduler.set_timesteps(config.num_infer_timesteps, device="cuda",
original_inference_steps=config.num_train_timesteps)
optimizer = torch.optim.AdamW(model.parameters(), lr=config.initial_learning_rate, eps=1e-6)
milestones = [x * len(myTrainLoader) for x in config.milestones]
optimizer_scheduler = MultiStepLR(optimizer, milestones=milestones, gamma=0.1)
if config.ema:
ema = ExponentialMovingAverage(model.parameters(), decay=0.995)
train_losses = []
test_losses = []
plt_train_loss_epoch = []
plt_test_loss_epoch = []
train_epoch_list = list(range(0, config.epoch_number))
test_epoch_list = list(range(0, int(config.epoch_number / config.test_epoch_interval)))
VQGAN = torch.load("YOUR DAE MODEL PATH").to(device).eval().requires_grad_(False)
print(time.strftime("%H:%M:%S", time.localtime()), "----------Begin Training----------")
for epoch in range(config.epoch_number):
model.train()
print(time.strftime("%H:%M:%S", time.localtime()),
f"Epoch:{epoch},learning rate:{optimizer.param_groups[0]['lr']}")
for i, batch in tqdm(enumerate(myTrainLoader)):
cxr_i, bs_i = batch[0].to(device), batch[1].to(device)
with torch.no_grad():
cxr = VQGAN.encode_stage_2_inputs(cxr_i)
bs = VQGAN.encode_stage_2_inputs(bs_i)
noise = torch.randn_like(cxr).to(device)
timesteps = torch.randint(0, config.num_train_timesteps, (cxr.shape[0],), device=device).long()
noisy_images = noise_scheduler.add_noise(bs, cxr, noise, timesteps)
noisy_images = torch.cat(
(noisy_images, cxr.clone() * torch.bernoulli(torch.full((cxr.shape[0], 1, 1, 1), 0.85)).to(device)),
dim=1)
pred = model(noisy_images, timesteps)[:, :4]
if config.prediction_type == "noise":
loss = F.mse_loss(pred.float(), noise.float())
else:
loss = F.mse_loss(pred.float(), ((timesteps / config.num_train_timesteps).view(-1, 1, 1, 1) * (
cxr - bs) + noise_scheduler.sqrd_sigma(timesteps).view(-1, 1, 1, 1) * noise).float())
loss.backward()
train_losses.append(loss.item())
# 迭代模型参数
optimizer.step()
optimizer.zero_grad()
optimizer_scheduler.step()
ema.update()
train_loss_epoch = sum(train_losses[-len(myTrainLoader):]) / len(myTrainLoader)
print(time.strftime("%H:%M:%S", time.localtime()), f"Epoch:{epoch},train losses:{train_loss_epoch}")
plt_train_loss_epoch.append(train_loss_epoch)
if (epoch + 1) % config.test_epoch_interval == 0:
model.eval()
print(time.strftime("%H:%M:%S", time.localtime()), "----------Stop Training----------")
print(time.strftime("%H:%M:%S", time.localtime()), "----------Begin Testing----------")
with torch.no_grad():
if config.ema:
ema.store()
ema.copy_to()
for i, batch in tqdm(enumerate(myTestLoader)):
cxr_i, bs_i = batch[0].to(device), batch[1].to(device)
with torch.no_grad():
cxr = VQGAN.encode_stage_2_inputs(cxr_i)
bs = VQGAN.encode_stage_2_inputs(bs_i)
noise = torch.randn_like(cxr).to(device)
timesteps = torch.randint(0, config.num_train_timesteps, (cxr.shape[0],),
device=device).long()
noisy_images = noise_scheduler.add_noise(bs, cxr, noise, timesteps)
# noisy_images = torch.cat((noisy_images, cxr.clone()), dim=1)
noisy_images = torch.cat((noisy_images, cxr.clone() * torch.bernoulli(
torch.full((cxr.shape[0], 1, 1, 1), 0.85)).to(device)), dim=1)
pred = model(noisy_images, timesteps)[:, :4]
if config.prediction_type == "noise":
loss = F.mse_loss(pred.float(), noise.float())
else:
loss = F.mse_loss(pred.float(), (
(timesteps / config.num_train_timesteps).view(-1, 1, 1, 1) * (
cxr - bs) + noise_scheduler.sqrd_sigma(timesteps).view(-1, 1, 1,
1) * noise).float())
test_losses.append(loss.item())
if config.ema:
ema.restore()
test_loss_epoch = sum(test_losses[-len(myTestLoader):]) / len(myTestLoader)
print(time.strftime("%H:%M:%S", time.localtime()), f"Epoch:{epoch},test losses:{test_loss_epoch}")
plt_test_loss_epoch.append(test_loss_epoch)
print(time.strftime("%H:%M:%S", time.localtime()), "----------End Validation----------")
print(time.strftime("%H:%M:%S", time.localtime()), "----------Continue to Train----------")
print(time.strftime("%H:%M:%S", time.localtime()), "----------End Training Normally----------")
# 查看损失曲线
f, ([ax1, ax2]) = plt.subplots(1, 2)
ax1.plot(train_epoch_list, plt_train_loss_epoch, color="red")
ax1.set_title('Train loss')
ax2.plot(test_epoch_list, plt_test_loss_epoch, color="blue")
ax2.set_title('Test loss') # 添加标题
plt.savefig("./loss-S-ema-noise-2000-dl-8c-s1-dep1.png")
if not config.use_server:
plt.show()
if config.ema:
ema.copy_to()
torch.save(model,
"dit-" + str(config.epoch_number) + "-" + str(date.today()) + "-S-ema-noise-2000-dl-8c-s1-dep1.pth")
if __name__ == "__main__":
train()
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