# Display the captured frame:
testIm(frame)
# Press q on keyboard to exit the program
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Break the loop
else:
break
if __name__ == '__main__':
net = FaceBox()
net.load_state_dict(torch.load('weight/faceboxes.pt', map_location=lambda storage, loc:storage), strict=False)
net.eval()
data_encoder = DataEncoder()
# given image path, predict and show
root_path = "picture/"
#picture = 'img_36.jpg' timg.jpg
picture = 'timg.jpg'
#testIm(root_path + picture)
#im = cv2.imread(root_path + picture)
cap = cv2.VideoCapture(0)
cvDemo()
cap.release()
cv2.destroyAllWindows()
def train():
use_gpu = torch.cuda.is_available()
file_root = os.path.dirname(os.path.abspath(__file__))
learning_rate = 0.00000001
num_epochs = 300
batch_size = 32
net = FaceBox()
if use_gpu:
net.cuda()
print('load model...')
net.load_state_dict(torch.load('weight/faceboxes.pt'))
criterion = MultiBoxLoss()
optimizer = torch.optim.SGD(net.parameters(),
lr=learning_rate,
momentum=0.99,
weight_decay=0.00055)
#optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[248, 300],
gamma=0.1)
train_dataset = ListDataset(root=file_root,
list_file='./traintest.txt',
train=True,
transform=[transforms.ToTensor()])
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
drop_last=True)
val_dataset = ListDataset(root=file_root,
list_file='./new_val.txt',
train=False,
transform=[transforms.ToTensor()])
val_loader = DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=1)
print('the dataset has %d images' % (len(train_dataset)))
print('the batch_size is %d' % (batch_size))
num_iter = 0
vis = visdom.Visdom()
win = vis.line(Y=np.array([0]), X=np.array([0]))
net.train()
for epoch in range(num_epochs):
scheduler.step()
print('\n\nStarting epoch %d / %d' % (epoch + 1, num_epochs))
print('Learning Rate for this epoch: {}'.format(learning_rate))
total_loss = 0.
net.train()
for i, (images, loc_targets, conf_targets) in enumerate(train_loader):
if use_gpu:
images = images.cuda()
loc_targets = loc_targets.cuda()
conf_targets = conf_targets.cuda()
loc_preds, conf_preds = net(images)
loss = criterion(loc_preds, loc_targets, conf_preds, conf_targets)
total_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 10 == 0:
print(
'Epoch [{}/{}], Iter [{}/{}] Loss: {:.4f}, average_loss: {:.4f}'
.format(epoch + 1, num_epochs, i + 1, len(train_loader),
loss.item(), total_loss / (i + 1)))
vis.line(Y=np.array([total_loss / (i + 1)]),
X=np.array([num_iter]),
win=win,
name='train',
update='append')
num_iter += 1
val_loss = 0.0
net.eval()
for idx, (images, loc_targets, conf_targets) in enumerate(val_loader):
with torch.no_grad():
if use_gpu:
images = images.cuda()
loc_targets = loc_targets.cuda()
conf_targets = conf_targets.cuda()
loc_preds, conf_preds = net(images)
loss = criterion(loc_preds, loc_targets, conf_preds,
conf_targets)
val_loss += loss.item()
val_loss /= len(val_dataset) / batch_size
vis.line(Y=np.array([val_loss]),
X=np.array([epoch * 30 + 30]),
win=win,
name='val',
update='append')
print('loss of val is {}'.format(val_loss))
if not os.path.exists('weight/'):
os.mkdir('weight')
print('saving model ...')
torch.save(net.state_dict(), 'weight/faceboxes.pt')
