# loss_fn =
#loss_fn = torch.nn.BCELoss()
loss_fn = torch.nn.CrossEntropyLoss()
metrics = {'fps': training.BatchTimer(), 'acc': training.accuracy}
# Train
writer = SummaryWriter()
writer.iteration, writer.interval = 0, 10
print('\n\nInitial')
print('-' * 10)
net.eval()
training.pass_epoch(net,
loss_fn,
val_loader,
batch_metrics=metrics,
show_running=True,
device=device,
writer=writer)
for epoch in range(epochs):
print('\nEpoch {}/{}'.format(epoch + 1, epochs))
print('-' * 10)
net.train()
training.pass_epoch(net,
loss_fn,
train_loader,
optimizer,
scheduler,
batch_metrics=metrics,
def train(self, save_model=True):
batch_size = 32
epochs = 100
workers = 0 if os.name == 'nt' else 8
optimizer = optim.Adam(self.model.parameters(), lr=0.001)
scheduler = MultiStepLR(optimizer, [5, 10])
dataset = self.get_train_dataset()
img_inds = np.arange(len(dataset))
np.random.shuffle(img_inds)
train_inds = img_inds[:int(0.8 * len(img_inds))]
val_inds = img_inds[int(0.8 * len(img_inds)):]
train_loader = DataLoader(dataset,
num_workers=workers,
batch_size=batch_size,
sampler=SubsetRandomSampler(train_inds))
val_loader = DataLoader(dataset,
num_workers=workers,
batch_size=batch_size,
sampler=SubsetRandomSampler(val_inds))
loss_fn = torch.nn.CrossEntropyLoss()
metrics = {'fps': training.BatchTimer(), 'acc': training.accuracy}
writer = SummaryWriter()
writer.iteration, writer.interval = 0, 10
print('\n\nInitial')
print('-' * 10)
self.model.eval()
training.pass_epoch(self.model,
loss_fn,
val_loader,
batch_metrics=metrics,
show_running=True,
writer=writer)
for epoch in tqdm(range(epochs)):
print('\nEpoch {}/{}'.format(epoch + 1, epochs))
print('-' * 10)
self.model.train()
training.pass_epoch(self.model,
loss_fn,
train_loader,
optimizer,
scheduler,
batch_metrics=metrics,
show_running=True,
writer=writer)
self.model.eval()
training.pass_epoch(self.model,
loss_fn,
val_loader,
batch_metrics=metrics,
show_running=True,
writer=writer)
writer.close()
if save_model:
self.save_model()
np.random.shuffle(img_inds)
train_inds = img_inds[:int(0.9 * len(img_inds))]
val_inds = img_inds[int(0.9 * len(img_inds)):]
train_loader = DataLoader(
dataset,
num_workers=workers,
batch_size=batch_size,
sampler=SubsetRandomSampler(train_inds)
)
val_loader = DataLoader(
dataset,
num_workers=workers,
batch_size=batch_size,
sampler=SubsetRandomSampler(val_inds)
)
loss_fn = torch.nn.CrossEntropyLoss()
metrics = {
'fps': training.BatchTimer(),
'acc': training.accuracy
}
print('\n\nInitial')
print('-' * 10)
resnet.eval()
training.pass_epoch(
resnet, loss_fn, val_loader,
batch_metrics=metrics, show_running=True, device=device
)
def train_model(db_id):
start_epoch = 0
batch_size = 32
epochs = 5
workers = 2
train_transform = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(p=0.5),
np.float32,
transforms.ToTensor(),
fixed_image_standardization
])
images, num_classes = get_dataset(db_id)
dataset = MyCustomDataset(images, train_transform)
train_loader = DataLoader(
dataset,
num_workers=workers,
batch_size=batch_size
)
model = InceptionResnetV1(
classify=True,
num_classes=num_classes
).to(device)
checkpoint_path, checkpoint_file, label_dict = get_saved_model(db_id)
if checkpoint_path is not None and os.path.exists(checkpoint_path):
checkpoint = torch.load(checkpoint_file)
model.load_state_dict(checkpoint['net'])
start_epoch = checkpoint['epoch']
else:
checkpoint_path = "./checkpoint"
optimizer = optim.SGD(model.parameters(), lr=0.1)
scheduler = MultiStepLR(optimizer, [60, 120, 180])
loss_fn = torch.nn.CrossEntropyLoss()
metrics = {
'fps': training.BatchTimer(),
'acc': training.accuracy
}
writer = SummaryWriter(log_dir=None, comment='', purge_step=None, max_queue=10, flush_secs=600, filename_suffix='face_rec_log_')
writer.iteration, writer.interval = 1, 10
checkpoint_save_name = 'face_rec_test'
ckp_dir = checkpoint_path
ckp_name = ''
for epoch in range(epochs):
training.pass_epoch(
model, loss_fn, train_loader, optimizer, scheduler,
batch_metrics=metrics, show_running=False, device=device,
writer=writer
)
if (epoch+1) % 50 == 0:
print('Saving..')
state = {
'net': model.state_dict(),
'epoch': epoch,
'is_final' : 0
}
ckp_name = checkpoint_save_name+'_'+str(epoch+1)
#if not os.path.isdir('checkpoint'):
os.makedirs(ckp_dir, exist_ok=True)
torch.save(state, ckp_dir+'/'+ckp_name+'.pth')
writer.close()
state = {
'net': model.state_dict(),
'epoch': epochs,
'is_final' : 1
}
ckp_name = checkpoint_save_name+'_final'
os.makedirs(ckp_dir, exist_ok=True)
save_path = ckp_dir+'/'+ckp_name+'.pth'
torch.save(state, save_path)
update_model(db_id, save_path)