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train_3D.py
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train_3D.py
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import argparse
import logging
from cgi import test
import timm
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.cuda.amp.autocast_mode import autocast
from torch.cuda.amp.grad_scaler import GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim.lr_scheduler import CosineAnnealingLR
from torchvision import models
from tqdm import tqdm
from base_vit import ViT
from lora import LoRA_ViT, LoRA_ViT_timm
from utils.dataloader_mrnet import kneeDataloader
from utils.dataloader_nih import nihDataloader
from utils.result import ResultCLS
from utils.utils import init, save
weightInfo = {
"base_dino": "vit_base_patch16_224.dino", # 21k -> 1k
"base_sam": "vit_base_patch16_224.sam", # 1k
"base_mill": "vit_base_patch16_224_miil.in21k_ft_in1k", # 1k
"base_beit": "beitv2_base_patch16_224.in1k_ft_in22k_in1k",
"base_clip": "vit_base_patch16_clip_224.laion2b_ft_in1k", # 1k
"base_deit": "deit_base_distilled_patch16_224", # 1k
"large_clip": "vit_large_patch14_clip_224.laion2b_ft_in1k", # laion-> 1k
"large_beit": "beitv2_large_patch16_224.in1k_ft_in22k_in1k",
"huge_clip": "vit_huge_patch14_clip_224.laion2b_ft_in1k", # laion-> 1k
"giant_eva": "eva_giant_patch14_224.clip_ft_in1k", # laion-> 1k
"giant_clip": "vit_giant_patch14_clip_224.laion2b",
"giga_clip": "vit_gigantic_patch14_clip_224.laion2b",
}
def train(epoch, trainset):
running_loss = 0.0
this_lr = scheduler.get_last_lr()[0]
net.train()
for image, label in tqdm(trainset, ncols=60, desc="train", unit="b", leave=None):
image, label = image.to(device), label.to(device)
optimizer.zero_grad()
with autocast(enabled=True):
pred = net.forward(image)
loss = loss_func(pred, label)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
running_loss = running_loss + loss.item()
scheduler.step()
loss = running_loss / len(trainset)
logging.info(f"\n\nEPOCH: {epoch}, LOSS : {loss:.3f}, LR: {this_lr:.2e}")
return
@torch.no_grad()
def eval(epoch, testset, datatype="val"):
result.init()
net.eval()
for image, label in tqdm(testset, ncols=60, desc=datatype, unit="b", leave=None):
image, label = image.to(device), label.to(device)
with autocast(enabled=True):
pred = net.forward(image)
result.eval(label, pred)
result.print(epoch, datatype)
return
if __name__ == "__main__":
scaler = GradScaler()
parser = argparse.ArgumentParser()
parser.add_argument("-bs", type=int, default=4)
parser.add_argument("-fold", type=int, default=0)
parser.add_argument("-data_path", type=str, default="")
parser.add_argument("-data_info", type=str, default="")
parser.add_argument("-annotation", type=str, default="")
parser.add_argument("-lr", type=float, default=3e-4)
parser.add_argument("-epochs", type=int, default=20)
parser.add_argument("-num_workers", type=int, default=4)
parser.add_argument("-num_classes", "-nc", type=int, default=2)
parser.add_argument("-backbone", type=str, default="vit_base_patch16_224")
parser.add_argument("-train_type", "-tt", type=str, default="lora", help="lora: only train lora, full: finetune on all, linear: finetune only on linear layer")
parser.add_argument("-rank", "-r", type=int, default=4)
parser.add_argument("-alpha", "-a", type=int, default=4)
cfg = parser.parse_args()
ckpt_path = init()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logging.info(cfg)
# a.根据local_rank来设定当前使用哪块GPU
# torch.cuda.set_device(local_rank)
# b.初始化DDP，使用默认backend(nccl)就行。如果是CPU模型运行，需要选择其他后端。
# dist.init_process_group(backend='nccl')
if cfg.train_type == "resnet50":
model = models.__dict__[cfg.train_type]()
model.load_state_dict(torch.load("../preTrain/resnet50-19c8e357.pth"))
infeature = model.fc.in_features
model.fc = nn.Linear(infeature, cfg.num_classes)
num_params = sum(p.numel() for p in model.parameters())
logging.info(f"trainable parameters: {num_params/2**20:.4f}M")
net = model.to(device)
else:
model = timm.create_model(cfg.backbone, pretrained=True)
# model = ViT('B_16_imagenet1k')
# model.load_state_dict(torch.load('../preTrain/B_16_imagenet1k.pth'))
if cfg.train_type == "lora":
lora_model = LoRA_ViT_timm(model, r=cfg.rank, alpha=cfg.alpha, num_classes=cfg.num_classes)
# lora_model = LoRA_ViT(model, r=cfg.rank, num_classes=cfg.num_classes)
num_params = sum(p.numel() for p in lora_model.parameters() if p.requires_grad)
logging.info(f"trainable parameters: {num_params/2**20:.4f}M")
net = lora_model.to(device)
elif cfg.train_type == "full":
model.fc = nn.Linear(768, cfg.num_classes)
num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
logging.info(f"trainable parameters: {num_params/2**20:.4f}M")
net = model.to(device)
elif cfg.train_type == "linear":
model.fc = nn.Linear(768, cfg.num_classes)
for param in model.parameters():
param.requires_grad = False
for param in model.fc.parameters():
param.requires_grad = True
num_params = sum(p.numel() for p in model.fc.parameters())
logging.info(f"trainable parameters: {num_params/2**20:.4f}M")
net = model.to(device)
else:
logging.info("Wrong training type")
exit()
net = torch.nn.DataParallel(net)
# trainset, testset = kneeDataloader(cfg)
# loss_func = nn.CrossEntropyLoss(label_smoothing=0.1).to(device)
trainset, valset = kneeDataloader(cfg)
loss_func = nn.CrossEntropyLoss(weight=torch.tensor([1,0.2]).to(device)).to(device)
optimizer = optim.Adam(net.parameters(), lr=cfg.lr)
scheduler = CosineAnnealingLR(optimizer, cfg.epochs, 1e-6)
result = ResultCLS(cfg.num_classes)
for epoch in range(1, cfg.epochs + 1):
train(epoch, trainset)
eval(epoch, valset, datatype="val")
if result.best_epoch == result.epoch:
torch.save(net.state_dict(), ckpt_path.replace(".pt", "_best.pt"))
logging.info(f"BEST VAL: {result.best_val_result:.3f}, TEST: {result.test_auc}, EPOCH: {(result.best_epoch):3}")
# logging.info(result.test_mls_auc)