def test_testing(self):
_ = import_ham_dataset(dataset_root=self.DATA_DIR,
model_path=self.DATA_DIR,
training=True)
dataset = import_ham_dataset(dataset_root=self.DATA_DIR,
model_path=self.DATA_DIR,
training=False)
assert len(dataset) == dataset.num_test_imgs
def main(opt):
# Check you can write to output path directory
if not os.access(os.path.split(opt.model_path)[0], os.W_OK):
raise OSError("--model_path is not a writeable path: %s" % opt.model_path)
# Import dataset
dataset = import_ham_dataset(dataset_root=opt.dataroot, training=opt.training,
model_path=os.path.split(opt.model_path)[0])
dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=True,
num_workers=opt.workers)
n_class = dataset.NUM_CLASS
# Load InceptionV3 network
model = models.inception_v3(pretrained=True)
# Freeze all layers
for params in model.parameters():
params.requires_grad = False
# Stage-2 , Freeze all the layers till "Conv2d_4a_3*3"
ct = []
for name, child in model.named_children():
if "Conv2d_4a_3x3" in ct:
for params in child.parameters():
params.requires_grad = True
ct.append(name)
# Replace final layer
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, n_class)
# Print network layer architecture
for name, child in model.named_children():
for name2, params in child.named_parameters():
print(name, name2, 'trainable=%r' % params.requires_grad)
if opt.cuda and torch.cuda.is_available():
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
print("Using", device)
model.to(device) # Move model to device
# Model training parameters
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(list(filter(lambda p: p.requires_grad, model.parameters())), lr=0.001, momentum=0.9)
scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
# Initiate TensorBoard logger
logger_tensorboard = TensorboardLogger(log_dir=os.path.split(opt.model_path)[0])
# # Training
if opt.training:
train_model(model, dataloader, len(dataset), criterion, optimizer, scheduler, device, opt.model_path,
logger_tensorboard, num_epochs=opt.epochs)
# # Testing
else:
model.load_state_dict(torch.load(opt.model_path))
test_model(model, dataloader, len(dataset), criterion, device)
def test_testing_wo_yaml(self):
with pytest.raises(HAMDatasetException):
_ = import_ham_dataset(dataset_root=self.DATA_DIR,
model_path=self.DATA_DIR,
training=False)
def test_training(self):
dataset = import_ham_dataset(dataset_root=self.DATA_DIR,
model_path=self.DATA_DIR,
training=True)
assert len(dataset) == (self.HAM_SIZE - dataset.num_test_imgs)
assert os.path.exists(dataset.path_yaml)