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run_finetune.py
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import os
import argparse
import numpy as np
import timm
import wandb
from tqdm import tqdm
import torch
#from torch.optim.lr_scheduler import StepLR, CosineAnnealingLR, CosineAnnealingWarmRestarts
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
from src.perspective_data import PerspectiveDatasetImageFolder, PerspectiveDataset
from src.utils import binary_accuracy, accuracy, CosineAnnealingWithWarmup, get_args_parser, get_transform_wo_crop
import json
import csv
from pathlib import Path
def train(model, train_loader, test_loader, val_loader, human_loader, criterion, optimizer, lr_scheduler, device, args):
best_acc_test = 0
best_acc_val = 0
best_acc_train = 0
best_acc_human = 0
for epoch in tqdm(range(args.epochs)):
model.train()
epoch_acc = []
epoch_loss = []
for i, batch in enumerate(train_loader):
imgs, labels = batch
imgs = imgs.to(device)
labels = labels.float().to(device)
labels = torch.unsqueeze(labels, 1)
optimizer.zero_grad()
preds = model(imgs)
loss = criterion(preds, labels)
loss.backward()
optimizer.step()
lr_scheduler.step()
#acc = accuracy(preds,labels)[0]
acc = binary_accuracy(preds, labels)
epoch_acc.append(acc)
epoch_loss.append(loss.item())
with torch.no_grad():
test_acc, test_loss, record = evaluate(model, test_loader, criterion, device, True, args)
val_acc, val_loss, _ = evaluate(model, val_loader, criterion, device, False, args)
human_acc, human_loss, human_record = evaluate(model, human_loader, criterion, device, True, args)
train_acc = sum(epoch_acc)/float(len(epoch_acc))
if val_acc > best_acc_val:
best_acc_val = val_acc
best_acc_test = test_acc
best_acc_human = human_acc
best_acc_train = train_acc
if args.task == 'depth':
filename = "preds_depth"
else:
filename = "preds"
if not args.not_pretrained:
file_names = [f'{args.model_name}_{filename}_ft.csv', f'{args.model_name}_{filename}_human_ft.csv']
else:
file_names = [f'{args.model_name}_{filename}_sc.csv', f'{args.model_name}_{filename}_human_sc.csv']
with open(f'./logs/fine_tune_preds/{file_names[0]}', 'w') as f:
header = ['path', 'pred', 'label']
writer = csv.writer(f)
writer.writerow(header)
writer.writerows(record.tolist())
with open(f'./logs/fine_tune_preds/{file_names[1]}', 'w') as f:
header = ['path', 'pred', 'label']
writer = csv.writer(f)
writer.writerow(header)
writer.writerows(human_record.tolist())
torch.save(model, f'./logs/fine_tune_ckpts/{args.model_name}_{args.task}.ckpt')
if args.wandb:
wandb.log({'train_acc':train_acc, 'train_loss':sum(epoch_loss)/float(len(epoch_loss)),
'val_acc':val_acc, 'val_loss':val_loss, 'human_acc':human_acc, 'human_loss':human_loss,
'test_acc':test_acc, 'test_loss':test_loss, "lr": lr_scheduler.get_last_lr()[0]})
return best_acc_train, best_acc_test, best_acc_val, best_acc_human
def evaluate(model, data_loader, criterion, device, return_record, args):
model.eval()
epoch_loss = []
preds_list = []
preds_list_logits = []
labels_list = []
img_path_list = []
for i, batch in enumerate(data_loader):
if return_record:
imgs, labels, img_path = batch
else:
imgs, labels = batch
img_path = None
imgs = imgs.to(device)
labels = labels.float().to(device)
labels = torch.unsqueeze(labels, 1)
preds = model(imgs)
loss = criterion(preds, labels)
epoch_loss.append(loss.item())
if return_record:
img_path_list+=img_path
#preds_list.append(torch.sigmoid(preds))
preds_list.append(preds)
preds_list_logits.append(preds)
labels_list.append(labels)
if return_record:
img_path_record = np.array(img_path_list).squeeze().T
preds_record = torch.cat(preds_list).cpu().numpy().squeeze().T
labels_record = torch.cat(labels_list).cpu().numpy().squeeze().T
records = np.vstack([img_path_record, preds_record, labels_record]).T
else:
records = None
preds = torch.cat(preds_list_logits).squeeze().cpu()
labels = torch.cat(labels_list).squeeze().cpu()
epoch_acc = binary_accuracy(preds, labels)
return epoch_acc, sum(epoch_loss)/float(len(epoch_loss)), records
def run(args):
#torch.set_float32_matmul_precision('high')
if args.wandb:
wandb.init(project='gs-perception-finetune',
config={
"learning_rate": args.learning_rate,
"dropout_rate": args.dropout_rate,
"weight_decay": args.weight_decay,
"architecture": args.model_name,
"epochs": args.epochs,
} )
device = torch.device(f'cuda:{args.gpu_id}')
model = timm.create_model(args.model_name, pretrained=(not args.not_pretrained), num_classes=args.num_classes, drop_rate=args.dropout_rate)
data_config = timm.data.resolve_model_data_config(model)
transform = get_transform_wo_crop(data_config)
# transforms_train = timm.data.create_transform(**data_config, is_training=True)
# transforms = timm.data.create_transform(**data_config, is_training=False)
if not args.flip:
train_dataset = datasets.ImageFolder(os.path.join(args.data_dir, 'train'), transform=transform)
else:
train_dataset = datasets.ImageFolder(os.path.join(args.data_dir, 'train_flip'), transform=transform)
test_dataset = PerspectiveDataset(Path(args.data_dir).parent, transform, split='test', task=args.task, return_path=True)
val_dataset = datasets.ImageFolder(os.path.join(args.data_dir, 'val'), transform=transform)
human_dataset = PerspectiveDataset(Path(args.data_dir).parent, transform, split='human', task=args.task, return_path=True)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True, drop_last=True, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True)
human_loader = DataLoader(human_dataset, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True)
#model = torch.compile(model)
model = model.to(device)
criterion = torch.nn.BCEWithLogitsLoss()
steps_per_epoch = len(train_loader)
optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate,
weight_decay=args.weight_decay, amsgrad=False)
lr_scheduler = CosineAnnealingWithWarmup(optimizer = optimizer, min_lrs = [args.min_lr], first_cycle_steps = args.epochs*steps_per_epoch, warmup_steps = args.warmup*steps_per_epoch, gamma = 0.9)
#lr_scheduler = None
best_acc_train, best_acc_test, best_acc_val, best_acc_human = train(model, train_loader, test_loader, val_loader, human_loader, criterion, optimizer, lr_scheduler, device, args)
print("Best acc train", best_acc_train)
print("Best acc test", best_acc_test)
print("Best acc val", best_acc_val)
print("Best acc human", best_acc_human)
if args.task == 'depth':
filename = 'depth_results_ft.json'
else:
filename = 'perspective_results_ft.json'
with open(f'logs/{filename}', 'r') as f:
results = json.load(f)
results[args.model_name] = [best_acc_train, best_acc_test, best_acc_val, best_acc_human]
with open(f'logs/{filename}', 'w') as f:
results_json = json.dumps(results, indent=4)
f.write(results_json)
if __name__ == "__main__":
args = get_args_parser().parse_args()
run(args)