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train.py
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import numpy as np
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.nn.modules.distance import PairwiseDistance
import torchvision
from torchvision import transforms
from eval_metrics import evaluate, plot_roc
from utils import TripletLoss
from models import FaceNetModel
from data_loader import TripletFaceDataset, get_dataloader
parser = argparse.ArgumentParser(description = 'Face Recognition using Triplet Loss')
parser.add_argument('--start-epoch', default = 0, type = int, metavar = 'SE',
help = 'start epoch (default: 0)')
parser.add_argument('--num-epochs', default = 200, type = int, metavar = 'NE',
help = 'number of epochs to train (default: 200)')
parser.add_argument('--num-classes', default = 10000, type = int, metavar = 'NC',
help = 'number of clases (default: 10000)')
parser.add_argument('--num-train-triplets', default = 10000, type = int, metavar = 'NTT',
help = 'number of triplets for training (default: 10000)')
parser.add_argument('--num-valid-triplets', default = 10000, type = int, metavar = 'NVT',
help = 'number of triplets for vaidation (default: 10000)')
parser.add_argument('--embedding-size', default = 128, type = int, metavar = 'ES',
help = 'embedding size (default: 128)')
parser.add_argument('--batch-size', default = 64, type = int, metavar = 'BS',
help = 'batch size (default: 128)')
parser.add_argument('--num-workers', default = 8, type = int, metavar = 'NW',
help = 'number of workers (default: 8)')
parser.add_argument('--learning-rate', default = 0.001, type = float, metavar = 'LR',
help = 'learning rate (default: 0.001)')
parser.add_argument('--margin', default = 0.5, type = float, metavar = 'MG',
help = 'margin (default: 0.5)')
parser.add_argument('--train-root-dir', default = '/run/media/hoosiki/WareHouse2/home/mtb/datasets/vggface2/test_mtcnnpy_182', type = str,
help = 'path to train root dir')
parser.add_argument('--valid-root-dir', default = '/run/media/hoosiki/WareHouse2/home/mtb/datasets/lfw/lfw_mtcnnpy_182', type = str,
help = 'path to valid root dir')
parser.add_argument('--train-csv-name', default = './datasets/test_vggface2.csv', type = str,
help = 'list of training images')
parser.add_argument('--valid-csv-name', default = './datasets/lfw.csv', type = str,
help = 'list of validtion images')
args = parser.parse_args()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
l2_dist = PairwiseDistance(2)
def main():
model = FaceNetModel(embedding_size = args.embedding_size, num_classes = args.num_classes).to(device)
optimizer = optim.Adam(model.parameters(), lr = args.learning_rate)
scheduler = lr_scheduler.StepLR(optimizer, step_size = 50, gamma = 0.1)
if args.start_epoch != 0:
checkpoint = torch.load('./log/checkpoint_epoch{}.pth'.format(args.start_epoch-1))
model.load_state_dict(checkpoint['state_dict'])
for epoch in range(args.start_epoch, args.num_epochs + args.start_epoch):
print(80 * '=')
print('Epoch [{}/{}]'.format(epoch, args.num_epochs + args.start_epoch - 1))
data_loaders, data_size = get_dataloader(args.train_root_dir, args.valid_root_dir,
args.train_csv_name, args.valid_csv_name,
args.num_train_triplets, args.num_valid_triplets,
args.batch_size, args.num_workers)
train_valid(model, optimizer, scheduler, epoch, data_loaders, data_size)
print(80 * '=')
def train_valid(model, optimizer, scheduler, epoch, dataloaders, data_size):
for phase in ['train', 'valid']:
labels, distances = [], []
triplet_loss_sum = 0.0
if phase == 'train':
scheduler.step()
model.train()
else:
model.eval()
for batch_idx, batch_sample in enumerate(dataloaders[phase]):
anc_img = batch_sample['anc_img'].to(device)
pos_img = batch_sample['pos_img'].to(device)
neg_img = batch_sample['neg_img'].to(device)
pos_cls = batch_sample['pos_class'].to(device)
neg_cls = batch_sample['neg_class'].to(device)
with torch.set_grad_enabled(phase == 'train'):
# anc_embed, pos_embed and neg_embed are encoding(embedding) of image
anc_embed, pos_embed, neg_embed = model(anc_img), model(pos_img), model(neg_img)
# choose the hard negatives only for "training"
pos_dist = l2_dist.forward(anc_embed, pos_embed)
neg_dist = l2_dist.forward(anc_embed, neg_embed)
all = (neg_dist - pos_dist < args.margin).cpu().numpy().flatten()
if phase == 'train':
hard_triplets = np.where(all == 1)
if len(hard_triplets[0]) == 0:
continue
else:
hard_triplets = np.where(all >= 0)
anc_hard_embed = anc_embed[hard_triplets].to(device)
pos_hard_embed = pos_embed[hard_triplets].to(device)
neg_hard_embed = neg_embed[hard_triplets].to(device)
anc_hard_img = anc_img[hard_triplets].to(device)
pos_hard_img = pos_img[hard_triplets].to(device)
neg_hard_img = neg_img[hard_triplets].to(device)
pos_hard_cls = pos_cls[hard_triplets].to(device)
neg_hard_cls = neg_cls[hard_triplets].to(device)
anc_img_pred = model.forward_classifier(anc_hard_img).to(device)
pos_img_pred = model.forward_classifier(pos_hard_img).to(device)
neg_img_pred = model.forward_classifier(neg_hard_img).to(device)
triplet_loss = TripletLoss(args.margin).forward(anc_hard_embed, pos_hard_embed, neg_hard_embed).to(device)
if phase == 'train':
optimizer.zero_grad()
triplet_loss.backward()
optimizer.step()
dists = l2_dist.forward(anc_embed, pos_embed)
distances.append(dists.data.cpu().numpy())
labels.append(np.ones(dists.size(0)))
dists = l2_dist.forward(anc_embed, neg_embed)
distances.append(dists.data.cpu().numpy())
labels.append(np.zeros(dists.size(0)))
triplet_loss_sum += triplet_loss.item()
avg_triplet_loss = triplet_loss_sum / data_size[phase]
labels = np.array([sublabel for label in labels for sublabel in label])
distances = np.array([subdist for dist in distances for subdist in dist])
tpr, fpr, accuracy, val, val_std, far = evaluate(distances, labels)
print(' {} set - Triplet Loss = {:.8f}'.format(phase, avg_triplet_loss))
print(' {} set - Accuracy = {:.8f}'.format(phase, np.mean(accuracy)))
with open('./log/{}_log_epoch{}.txt'.format(phase, epoch), 'w') as f:
f.write(str(epoch) + '\t' +
str(np.mean(accuracy)) + '\t' +
str(avg_triplet_loss))
if phase == 'train':
torch.save({'epoch': epoch,
'state_dict': model.state_dict()},
'./log/checkpoint_epoch{}.pth'.format(epoch))
else:
plot_roc(fpr, tpr, figure_name = './log/roc_valid_epoch_{}.png'.format(epoch))
if __name__ == '__main__':
main()