I was trying to fine tune pre-trained model but I think you current code did not provide this facility. I added a few lines in train.py, have a look at the following code. If you think it should be the part of it kindly add this in next commit. Thanks for your good work.
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, models, transforms
from dataloader import TrainDataset, ValDataset, collater, RandomCroper, RandomFlip, Resizer, PadToSquare
from torch.utils.data import Dataset, DataLoader
from terminaltables import AsciiTable, DoubleTable, SingleTable
from tensorboardX import SummaryWriter
from torch.optim import lr_scheduler
import torch.distributed as dist
import eval_widerface
import torchvision
import model
import os
from torch.utils.data.distributed import DistributedSampler
import torchvision_model
def get_args():
parser = argparse.ArgumentParser(description="Train program for retinaface.")
parser.add_argument('--data_path', type=str, help='Path for dataset,default WIDERFACE')
parser.add_argument('--batch', type=int, default=16, help='Batch size')
parser.add_argument('--epochs', type=int, default=200, help='Max training epochs')
parser.add_argument('--shuffle', type=bool, default=True, help='Shuffle dataset or not')
parser.add_argument('--img_size', type=int, default=640, help='Input image size')
parser.add_argument('--verbose', type=int, default=10, help='Log verbose')
parser.add_argument('--save_step', type=int, default=10, help='Save every save_step epochs')
parser.add_argument('--eval_step', type=int, default=3, help='Evaluate every eval_step epochs')
parser.add_argument('--save_path', type=str, default='./out', help='Model save path')
parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50)
parser.add_argument('--pretrained_model_path', type=str, default='./out', help='Pre-Trained Model Path')
args = parser.parse_args()
print(args)
return args
def main():
args = get_args()
if not os.path.exists(args.save_path):
os.mkdir(args.save_path)
log_path = os.path.join(args.save_path,'log')
if not os.path.exists(log_path):
os.mkdir(log_path)
writer = SummaryWriter(log_dir=log_path)
data_path = args.data_path
train_path = os.path.join(data_path,'train/label.txt')
val_path = os.path.join(data_path,'val/label.txt')
# dataset_train = TrainDataset(train_path,transform=transforms.Compose([RandomCroper(),RandomFlip()]))
dataset_train = TrainDataset(train_path,transform=transforms.Compose([Resizer(),PadToSquare()]))
dataloader_train = DataLoader(dataset_train, num_workers=8, batch_size=args.batch, collate_fn=collater,shuffle=True)
# dataset_val = ValDataset(val_path,transform=transforms.Compose([RandomCroper()]))
dataset_val = ValDataset(val_path,transform=transforms.Compose([Resizer(),PadToSquare()]))
dataloader_val = DataLoader(dataset_val, num_workers=8, batch_size=args.batch, collate_fn=collater)
total_batch = len(dataloader_train)
# Create the model
# if args.depth == 18:
# retinaface = model.resnet18(num_classes=2, pretrained=True)
# elif args.depth == 34:
# retinaface = model.resnet34(num_classes=2, pretrained=True)
# elif args.depth == 50:
# retinaface = model.resnet50(num_classes=2, pretrained=True)
# elif args.depth == 101:
# retinaface = model.resnet101(num_classes=2, pretrained=True)
# elif args.depth == 152:
# retinaface = model.resnet152(num_classes=2, pretrained=True)
# else:
# raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
# Create torchvision model
return_layers = {'layer2':1,'layer3':2,'layer4':3}
retinaface = torchvision_model.create_retinaface(return_layers)
retinaface = retinaface.cuda()
retinaface = torch.nn.DataParallel(retinaface).cuda()
retinaface.training = True
try:
pretrained_model_path = args.pretrained_model_path
state_dict=None
with open( pretrained_model_path , "br" ) as f:
stat_dict = torch.load(f)
retinaface.load_state_dict( stat_dict )
print( "Previuos Model is Successfully Loaded :)" )
except:
print( "Error while loading previous model :(" )
optimizer = optim.Adam(retinaface.parameters(), lr=1e-3)
# optimizer = optim.SGD(retinaface.parameters(), lr=1e-2, momentum=0.9, weight_decay=0.0005)
# scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True)
# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
#scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10,30,60], gamma=0.1)
print('Start to train.')
epoch_loss = []
iteration = 0
for epoch in range(args.epochs):
retinaface.train()
# Training
for iter_num,data in enumerate(dataloader_train):
optimizer.zero_grad()
classification_loss, bbox_regression_loss,ldm_regression_loss = retinaface([data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
bbox_regression_loss = bbox_regression_loss.mean()
ldm_regression_loss = ldm_regression_loss.mean()
# loss = classification_loss + 1.0 * bbox_regression_loss + 0.5 * ldm_regression_loss
loss = classification_loss + bbox_regression_loss + ldm_regression_loss
loss.backward()
optimizer.step()
if iter_num % args.verbose == 0:
log_str = "\n---- [Epoch %d/%d, Batch %d/%d] ----\n" % (epoch, args.epochs, iter_num, total_batch)
table_data = [
['loss name','value'],
['total_loss',str(loss.item())],
['classification',str(classification_loss.item())],
['bbox',str(bbox_regression_loss.item())],
['landmarks',str(ldm_regression_loss.item())]
]
table = AsciiTable(table_data)
log_str +=table.table
print(log_str)
# write the log to tensorboard
writer.add_scalar('losses:',loss.item(),iteration*args.verbose)
writer.add_scalar('class losses:',classification_loss.item(),iteration*args.verbose)
writer.add_scalar('box losses:',bbox_regression_loss.item(),iteration*args.verbose)
writer.add_scalar('landmark losses:',ldm_regression_loss.item(),iteration*args.verbose)
iteration +=1
# Eval
if epoch % args.eval_step == 0:
print('-------- RetinaFace Pytorch --------')
print ('Evaluating epoch {}'.format(epoch))
recall, precision = eval_widerface.evaluate(dataloader_val,retinaface)
print('Recall:',recall)
print('Precision:',precision)
writer.add_scalar('Recall:', recall, epoch*args.eval_step)
writer.add_scalar('Precision:', precision, epoch*args.eval_step)
# Save model
if (epoch + 1) % args.save_step == 0 or iter_num>=100:
torch.save(retinaface.state_dict(), args.save_path + '/model_epoch_{}.pt'.format(epoch + 1))
writer.close()
if __name__=='__main__':
main()
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