class QuasiSiameseNetwork(object):
def __init__(self, args):
input_size = (args.inputSize, args.inputSize)
self.run_name = args.runName
self.input_size = input_size
self.lr = args.learningRate
self.criterion = nnloss.MSELoss()
self.transforms = {}
self.model = SiameseNetwork()
if torch.cuda.device_count() > 1:
logger.info('Using {} GPUs'.format(torch.cuda.device_count()))
self.model = nn.DataParallel(self.model)
for s in ('train', 'validation', 'test'):
self.transforms[s] = get_pretrained_iv3_transforms(s)
logger.debug('Num params: {}'.format(
len([_ for _ in self.model.parameters()])))
self.optimizer = Adam(self.model.parameters(), lr=self.lr)
self.lr_scheduler = ReduceLROnPlateau(self.optimizer,
factor=0.1,
patience=10,
min_lr=1e-5,
verbose=True)
def run_epoch(self,
epoch,
loader,
device,
phase='train',
accuracy_threshold=0.1):
assert phase in ('train', 'validation', 'test')
self.model = self.model.to(device)
self.model.eval()
if phase == 'train':
self.model.train() # Set model to training mode
running_loss = 0.0
running_corrects = 0
running_n = 0.0
if not (phase == 'train'):
prediction_file = open(
os.path.join(
loader.dataset.directory,
'{}_epoch_{:03d}_predictions.txt'.format(
self.run_name, epoch)), 'w+')
prediction_file.write('filename label prediction\n')
for idx, (filename, image1, image2, labels) in enumerate(loader, 1):
image1 = image1.to(device)
image2 = image2.to(device)
labels = labels.float().to(device)
if phase == 'train':
# zero the parameter gradients
self.optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = self.model(image1, image2).squeeze()
loss = self.criterion(outputs, labels)
if not (phase == 'train'):
prediction_file.writelines([
'{} {} {}\n'.format(*line)
for line in zip(filename, labels.tolist(),
outputs.clamp(0, 1).tolist())
])
if phase == 'train':
loss.backward()
self.optimizer.step()
running_loss += loss.item() * image1.size(0)
running_corrects += (
outputs - labels.data).abs().le(accuracy_threshold).sum()
running_n += image1.size(0)
if idx % 1 == 0:
logger.debug(
'Epoch: {:03d} Phase: {:10s} Batch {:04d}/{:04d}: Loss: {:.4f} Accuracy: {:.4f}'
.format(epoch, phase, idx, len(loader),
running_loss / running_n,
running_corrects.double() / running_n))
epoch_loss = running_loss / running_n
epoch_accuracy = running_corrects.double() / running_n
if not (phase == 'train'):
prediction_file.write('Epoch {:03d} Accuracy: {:.4f}\n'.format(
epoch, epoch_accuracy))
prediction_file.close()
logger.info(
'Epoch {:03d} Phase: {:10s} Loss: {:.4f} Accuracy: {:.4f}'.format(
epoch, phase, epoch_loss, epoch_accuracy))
return epoch_loss, epoch_accuracy
def train(self, n_epochs, datasets, device, save_path):
train_set, train_loader = datasets.load('train')
validation_set, validation_loader = datasets.load('validation')
best_accuracy, best_model_wts = 0.0, copy.deepcopy(
self.model.state_dict())
start_time = time.time()
for epoch in range(1, n_epochs + 1):
# train network
train_loss, train_accuracy = self.run_epoch(epoch,
train_loader,
device,
phase='train')
# eval on validation
validation_loss, validation_accuracy = self.run_epoch(
epoch, validation_loader, device, phase='validation')
self.lr_scheduler.step(validation_loss)
if validation_accuracy > best_accuracy:
best_accuracy = validation_accuracy
best_model_wts = copy.deepcopy(self.model.state_dict())
logger.info('Epoch {:03d} Checkpoint: Saving to {}'.format(
epoch, save_path))
torch.save(best_model_wts, save_path)
time_elapsed = time.time() - start_time
logger.info('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
logger.info('Best validation Accuracy: {:4f}.'.format(best_accuracy))
def test(self, datasets, device, load_path):
self.model.load_state_dict(torch.load(load_path))
test_set, test_loader = datasets.load('test')
self.run_epoch(1, test_loader, device, phase='test')
class QuasiSiameseNetwork(object):
def __init__(self, args):
train_config = args.outputType
net_config = args.networkType
n_freeze = args.numFreeze
input_size = (args.inputSize, args.inputSize)
assert train_config in ("soft-targets", "softmax")
assert net_config in ("pre-trained", "full")
self.train_config = train_config
self.input_size = input_size
self.lr = args.learningRate
if train_config == "soft-targets":
self.n_classes = 1
self.criterion = nnloss.BCEWithLogitsLoss()
else:
# TODO: weights
self.n_classes = 4
self.criterion = nnloss.CrossEntropyLoss()
self.transforms = {}
if net_config == "pre-trained":
self.model = SiameseNetwork(self.n_classes, n_freeze=n_freeze)
for s in ("train", "val", "test"):
self.transforms[s] = get_pretrained_iv3_transforms(s)
else:
self.model = build_net(input_size, self.n_classes)
assert input_size[0] == input_size[1]
for s in ("train", "val", "test"):
self.transforms[s] = get_transforms(s, input_size[0])
log.debug("Num params: {}".format(
len([_ for _ in self.model.parameters()])))
self.optimizer = Adam(self.model.parameters(), lr=self.lr)
self.lr_scheduler = ReduceLROnPlateau(self.optimizer,
factor=0.1,
patience=10,
min_lr=1e-5,
verbose=True)
def run_epoch(self, epoch, loader, device, phase="train"):
assert phase in ("train", "val", "test")
self.model = self.model.to(device)
log.info("Phase: {}, Epoch: {}".format(phase, epoch))
if phase == 'train':
self.model.train() # Set model to training mode
else:
self.model.eval()
running_loss = 0.0
running_corrects = 0
running_n = 0.0
rolling_eval = RollingEval()
for idx, (image1, image2, labels) in enumerate(loader):
image1 = image1.to(device)
image2 = image2.to(device)
labels = labels.to(device)
if phase == "train":
# zero the parameter gradients
self.optimizer.zero_grad()
with torch.set_grad_enabled(phase == "train"):
outputs = self.model(image1, image2)
_, preds = torch.max(outputs, 1)
_, labels = torch.max(labels, 1)
loss = self.criterion(outputs, labels)
if phase == 'train':
loss.backward()
self.optimizer.step()
rolling_eval.add(labels, preds)
running_loss += loss.item() * image1.size(0)
running_corrects += torch.sum(preds == labels.data)
running_n += image1.size(0)
if idx % 1 == 0:
log.info(
"\tBatch {}: Loss: {:.4f} Acc: {:.4f} F1: {:.4f} Recall: {:.4f}"
.format(idx, running_loss / running_n,
running_corrects.double() / running_n,
rolling_eval.f1_score(), rolling_eval.recall()))
epoch_loss = running_loss / running_n
epoch_acc = running_corrects.double() / \
running_n
epoch_f1 = rolling_eval.f1_score()
epoch_recall = rolling_eval.recall()
log.info('{}: Loss: {:.4f} \nReport: {}'.format(
phase, epoch_loss, rolling_eval.every_measure()))
return epoch_loss, epoch_acc, epoch_f1
def train(self, n_epochs, datasets, device, save_path):
train_set, train_loader = datasets.load("train")
val_set, val_loader = datasets.load("val")
best_f1, best_model_wts = 0.0, copy.deepcopy(self.model.state_dict())
start_time = time.time()
for epoch in range(n_epochs):
# train network
train_loss, train_acc, train_f1 = self.run_epoch(epoch,
train_loader,
device,
phase="train")
# eval on validation
val_loss, val_acc, val_f1 = self.run_epoch(epoch,
val_loader,
device,
phase="val")
self.lr_scheduler.step(val_loss)
if val_f1 > best_f1:
best_f1 = val_f1
best_model_wts = copy.deepcopy(self.model.state_dict())
log.info("Checkpoint: Saving to {}".format(save_path))
torch.save(best_model_wts, save_path)
time_elapsed = time.time() - start_time
log.info('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
log.info('Best val F1: {:4f}.'.format(best_f1))
def test(self, datasets, device, load_path):
self.model.load_state_dict(torch.load(load_path))
test_set, test_loader = datasets.load("test")
self.run_epoch(0, test_loader, device, phase="test")