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finetune.py
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finetune.py
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"""Author: Hyung-Kwon Ko ([email protected])"""
import os
import time
import copy
import tqdm
import logging
import argparse
import collections
from datetime import datetime
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import transforms
from vggface2.utils import load_state_dict
import vggface2.senet as SENet
from datasets.vggface2_sg2 import StyleGAN2_Data
def save_image(input, outdir, pred):
plot_style = dict(marker='o', markersize=4, linestyle='-', lw=2)
pred_type = collections.namedtuple('prediction_type', ['slice', 'color'])
pred_types = {'face': pred_type(slice(0, 17), (0.682, 0.780, 0.909, 0.5)),
'eyebrow1': pred_type(slice(17, 22), (1.0, 0.498, 0.055, 0.4)),
'eyebrow2': pred_type(slice(22, 27), (1.0, 0.498, 0.055, 0.4)),
'nose': pred_type(slice(27, 31), (0.345, 0.239, 0.443, 0.4)),
'nostril': pred_type(slice(31, 36), (0.345, 0.239, 0.443, 0.4)),
'eye1': pred_type(slice(36, 42), (0.596, 0.875, 0.541, 0.3)),
'eye2': pred_type(slice(42, 48), (0.596, 0.875, 0.541, 0.3)),
'lips': pred_type(slice(48, 60), (0.596, 0.875, 0.541, 0.3)),
'teeth': pred_type(slice(60, 68), (0.596, 0.875, 0.541, 0.4))
}
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.imshow(input)
for pred_type in pred_types.values():
ax.plot(pred[pred_type.slice, 0],
pred[pred_type.slice, 1],
color=pred_type.color, **plot_style)
ax.axis('off')
plt.savefig(outdir)
def train(args):
logging.info('Initializing Datasets and Data_loader...')
data_transforms = transforms.Compose([
transforms.Resize(args.pretrained_size),
transforms.CenterCrop(args.pretrained_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
data = {
'train': StyleGAN2_Data(root=args.root, split='train', fname=args.fname, transform=data_transforms, scale_size=args.image_size),
'val': StyleGAN2_Data(root=args.root, split='val', fname=args.fname, transform=data_transforms, scale_size=args.image_size)
}
data_loader = {
'train': torch.utils.data.DataLoader(data['train'], batch_size=args.batch_size, shuffle=False, num_workers=4, drop_last=False),
'val': torch.utils.data.DataLoader(data['val'], batch_size=args.batch_size, shuffle=False, num_workers=4, drop_last=False)
}
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = initialize_model(args)
model = model.to(device)
logging.info(model)
logging.info('Params to learn:')
if args.feature_extract:
params_to_update = []
for i, (name, param) in enumerate(model.named_parameters()):
if param.requires_grad == True:
params_to_update.append(param)
logging.info(f'{i}. param.name: {name}')
# logging.info(f'{i}. param.shape: {param.shape}')
else:
params_to_update = model.parameters()
for i, (name, param) in enumerate(model.named_parameters()):
if param.requires_grad == True:
logging.info(f'{i}: {name}')
optimizer = optim.Adam(params_to_update, lr=args.lr, weight_decay=1e-5) # l2 norm
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
criterion = nn.MSELoss()
logging.info('Training starts!')
since = time.time()
val_loss_history = []
best_model_wts = copy.deepcopy(model.state_dict())
best_loss = float('inf')
for epoch in range(args.num_epochs):
logging.info('-' * 10)
logging.info(f'Epoch {epoch}/{args.num_epochs - 1}')
for phase in ['train', 'val']:
if phase == 'train':
model.train()
else:
if epoch % 1 == 0:
model.eval()
else:
continue
running_loss = 0.0
for batch in tqdm.tqdm(data_loader[phase]):
inputs = batch['image'].to(device)
labels = batch['label'].to(device)
indices = batch['meta']['index']
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
# outputs = outputs.to(float)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
epoch_loss = running_loss / len(data_loader[phase].dataset)
logging.info(f'{phase} Loss: {epoch_loss:.4f}')
if phase == 'val':
val_loss_history.append(epoch_loss)
if epoch_loss < best_loss:
best_loss = epoch_loss
best_model_wts = copy.deepcopy(model.state_dict())
torch.save(best_model_wts, os.path.join(args.ckpt_dir, f'senet_ckpt_{args.lr}_{args.weight_decay}_{args.feature_extract}_{args.out_features}.pth'))
if args.save_fig and epoch % args.save_epoch == 0:
outputs = outputs.cpu().numpy()
for i, (index, output) in enumerate(zip(indices, outputs)):
if i > 3:
break
output = output.reshape(1, -1)
pred = data[phase].inv_scale_label(output)
pred = pred.reshape(-1, 2)
input = data[phase].get_image(index, os.path.join(args.root, phase))
save_image(input, os.path.join(args.val_save_dir, f'out_{index}_{epoch}.png'), pred)
if epoch == 0:
answ = data[phase].labels_original[index]
answ = answ.reshape(-1, 2)
save_image(input, os.path.join(args.val_save_dir, f'out_{index}_ans.png'), answ)
scheduler.step()
time_elapsed = time.time() - since
logging.info(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
logging.info(f'Best val Loss: {best_loss:4f}')
model.load_state_dict(best_model_wts)
return model, val_loss_history
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters():
param.requires_grad = False
def initialize_model(args):
model = SENet.senet50(num_classes=args.n_identity, include_top=True) # forward output w/ FC layer (dim: 138=NUM_OUT_FT)
# model = SENet.senet50(num_classes=N_IDENTITY, include_top=False) # forward output w/o FC layer (dim: 2048)
if args.mode == 'train':
load_state_dict(model, args.pretrained_path)
set_parameter_requires_grad(model, args.feature_extract)
num_in_ft = model.fc.in_features # 2048
model.fc = nn.Linear(num_in_ft, args.out_features)
return model
else:
num_in_ft = model.fc.in_features # 2048
model.fc = nn.Linear(num_in_ft, args.out_features)
model.load_state_dict(torch.load(args.model_path))
return model
def test(args):
model = initialize_model(args)
data_transforms = transforms.Compose([
transforms.Resize(args.pretrained_size),
transforms.CenterCrop(args.pretrained_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
data = StyleGAN2_Data(root=args.root, split='test', fname=args.fname, transform=data_transforms, scale_size=args.image_size)
data_loader = torch.utils.data.DataLoader(data, batch_size=args.batch_size, shuffle=False, num_workers=4, drop_last=False)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
model.eval()
for i, batch in enumerate(data_loader):
inputs = batch['image'].to(device)
indices = batch['meta']['index']
outputs = model(inputs)
outputs = outputs.cpu().detach().numpy()
for (index, output) in zip(indices, outputs):
print(f"Run for {index}/{len(indices)}...")
output = output.reshape(1, -1)
pred = data.inv_scale_label(output)
pred = pred.reshape(-1, 2)
input = data.get_image(index, os.path.join(args.root, 'test'))
save_image(input, os.path.join(args.val_save_dir, f'out_{index}.png'), pred)
if i == 0:
answ = data.labels_original[index]
answ = answ.reshape(-1, 2)
save_image(input, os.path.join(args.val_save_dir, f'out_{index}_ans.png'), answ)
def main():
parser = argparse.ArgumentParser("PyTorch SE-Net Fine-tuning Code")
parser.add_argument('--mode', type=str, default='train', choices=['train', 'test'], help='train/test')
parser.add_argument('--image_size', type=int, default=256, help='training data size == (x.size[0])')
parser.add_argument('--out_features', type=int, default=136, help='number of classes == y')
parser.add_argument('--batch_size', type=int, default=128, help='batch size')
parser.add_argument('--num_epochs', type=int, default=30, help='number of epochs to run')
parser.add_argument('--lr', type=float, default=1e-5, help='learning rate')
parser.add_argument('--weight_decay', type=float, default=1e-5, help='l2 norm')
parser.add_argument('--feature_extract', type=int, choices=[0, 1], default=0, help='finetune fc layer only (True, 1) / all layers (False, 0)')
parser.add_argument('--root', type=str, default='data', help='training data dir')
parser.add_argument('--fname', type=str, default='landmarks', help='label file name')
parser.add_argument('--pretrained_path', type=str, default='./pretrained/senet50_scratch_weight.pkl', help='pretrained SENet model path')
parser.add_argument('--model_path', type=str, default='./ckpt/senet_ckpt_0.001.pth', help='pretrained SENet model path')
parser.add_argument('--save_epoch', type=int, default=1, help='epoch to save images for validation')
parser.add_argument('--val_save_dir', type=str, default='./out/finetune', help='save directory for validation file')
parser.add_argument('--log_dir', type=str, default='log', help='save directory for log file')
parser.add_argument('--ckpt_dir', type=str, default='ckpt', help='save directory for checkpoint file')
parser.add_argument('--n_identity', type=int, default=8631, help='number of classes pretrained in SENet')
parser.add_argument('--pretrained_size', type=int, default=224, help='image size pretrained in SENet')
parser.add_argument('--save_fig', type=int, default=0, help='save predicted result as figures')
args = parser.parse_args()
os.makedirs(args.val_save_dir, exist_ok=True)
os.makedirs(args.log_dir, exist_ok=True)
os.makedirs(args.ckpt_dir, exist_ok=True)
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] %(message)s',
datefmt='%Y-%m-%d,%H:%M:%S',
handlers=[
logging.FileHandler(os.path.join(args.log_dir, f'senet_finetune_{datetime.now().time()}.log')),
logging.StreamHandler()
]
)
logging.info(f'PyTorch Version: {torch.__version__}')
logging.info(f'Torchvision Version: {torchvision.__version__}')
logging.info(f'args: {args}')
if args.mode == 'train':
train(args)
else:
test(args)
if __name__ == '__main__':
main()