def test_resnet(self):
model = resnet("cifar10", depth=20)
# Test input/output sizes
self.assertEqual(model.in_size, (32, 32))
self.assertEqual(model.num_classes, 10)
# Test input/output sizes
model = resnet("imagenet", depth=12)
self.assertEqual(model.in_size, (224, 224))
self.assertEqual(model.num_classes, 1000)
def main(args):
# Model getter: specify dataset and depth of the network.
model = pytorchcv_wrapper.resnet('cifar10', depth=20, pretrained=False)
# Or get a more specific model. E.g. wide resnet, with depth 40 and growth
# factor 8 for Cifar 10.
# model = pytorchcv_wrapper.get_model("wrn40_8_cifar10", pretrained=False)
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}"
if torch.cuda.is_available() and
args.cuda >= 0 else "cpu")
device = "cpu"
# --- TRANSFORMATIONS
transform = transforms.Compose([
ToTensor(),
transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261))
])
# --- SCENARIO CREATION
cifar_train = CIFAR10(root=expanduser("~") + "/.avalanche/data/cifar10/",
train=True, download=True, transform=transform)
cifar_test = CIFAR10(root=expanduser("~") + "/.avalanche/data/cifar10/",
train=False, download=True, transform=transform)
scenario = nc_benchmark(
cifar_train, cifar_test, 5, task_labels=False, seed=1234,
fixed_class_order=[i for i in range(10)])
# choose some metrics and evaluation method
interactive_logger = InteractiveLogger()
eval_plugin = EvaluationPlugin(
accuracy_metrics(
minibatch=True, epoch=True, experience=True, stream=True),
loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
forgetting_metrics(experience=True),
loggers=[interactive_logger])
# CREATE THE STRATEGY INSTANCE (Naive, with Replay)
cl_strategy = Naive(model, torch.optim.SGD(model.parameters(), lr=0.01),
CrossEntropyLoss(),
train_mb_size=100, train_epochs=1, eval_mb_size=100,
device=device,
plugins=[ReplayPlugin(mem_size=1000)],
evaluator=eval_plugin
)
# TRAINING LOOP
print('Starting experiment...')
results = []
for experience in scenario.train_stream:
print("Start of experience ", experience.current_experience)
cl_strategy.train(experience)
print('Training completed')
print('Computing accuracy on the whole test set')
results.append(cl_strategy.eval(scenario.test_stream))