class ModelWrapperTest(unittest.TestCase):
def setUp(self):
# self.model = nn.Sequential(
# nn.Linear(10, 8), nn.ReLU(), nn.Dropout(), nn.Linear(8, 1), nn.Sigmoid()
# )
self.model = DummyModel()
self.criterion = nn.BCEWithLogitsLoss()
self.wrapper = ModelWrapper(self.model, self.criterion)
self.optim = torch.optim.SGD(self.wrapper.get_params(), 0.01)
self.dataset = DummyDataset()
def test_train_on_batch(self):
self.wrapper.train()
old_param = list(map(lambda x: x.clone(), self.model.parameters()))
input, target = torch.randn([1, 3, 10, 10]), torch.randn(1, 1)
self.wrapper.train_on_batch(input, target, self.optim)
new_param = list(map(lambda x: x.clone(), self.model.parameters()))
assert any(
[not torch.allclose(i, j) for i, j in zip(old_param, new_param)])
# test reset weights properties
linear_weights = list(
self.wrapper.model.named_children())[3][1].weight.clone()
conv_weights = list(
self.wrapper.model.named_children())[0][1].weight.clone()
self.wrapper.reset_fcs()
linear_new_weights = list(
self.wrapper.model.named_children())[3][1].weight.clone()
conv_new_weights = list(
self.wrapper.model.named_children())[0][1].weight.clone()
assert all([
not torch.allclose(i, j)
for i, j in zip(linear_new_weights, linear_weights)
])
assert all([
torch.allclose(i, j)
for i, j in zip(conv_new_weights, conv_weights)
])
self.wrapper.reset_all()
conv_next_new_weights = list(
self.wrapper.model.named_children())[0][1].weight.clone()
assert all([
not torch.allclose(i, j)
for i, j in zip(conv_new_weights, conv_next_new_weights)
])
def test_test_on_batch(self):
self.wrapper.eval()
input, target = torch.randn([1, 3, 10, 10]), torch.randn(1, 1)
preds = torch.stack([
self.wrapper.test_on_batch(input, target, cuda=False)
for _ in range(10)
]).view(10, -1)
# Same loss
assert torch.allclose(torch.mean(preds, 0), preds[0])
preds = torch.stack([
self.wrapper.test_on_batch(input,
target,
cuda=False,
average_predictions=10)
for _ in range(10)
]).view(10, -1)
assert torch.allclose(torch.mean(preds, 0), preds[0])
def test_predict_on_batch(self):
self.wrapper.eval()
input = torch.randn([2, 3, 10, 10])
# iteration == 1
pred = self.wrapper.predict_on_batch(input, 1, False)
assert pred.size() == (2, 1, 1)
# iterations > 1
pred = self.wrapper.predict_on_batch(input, 10, False)
assert pred.size() == (2, 1, 10)
# iteration == 1
self.wrapper = ModelWrapper(self.model,
self.criterion,
replicate_in_memory=False)
pred = self.wrapper.predict_on_batch(input, 1, False)
assert pred.size() == (2, 1, 1)
# iterations > 1
pred = self.wrapper.predict_on_batch(input, 10, False)
assert pred.size() == (2, 1, 10)
def test_train(self):
history = self.wrapper.train_on_dataset(self.dataset,
self.optim,
10,
2,
use_cuda=False,
workers=0)
assert len(history) == 2
def test_test(self):
l = self.wrapper.test_on_dataset(self.dataset,
10,
use_cuda=False,
workers=0)
assert np.isfinite(l)
l = self.wrapper.test_on_dataset(self.dataset,
10,
use_cuda=False,
workers=0,
average_predictions=10)
assert np.isfinite(l)
def test_predict(self):
l = self.wrapper.predict_on_dataset(self.dataset,
10,
20,
use_cuda=False,
workers=0)
self.wrapper.eval()
assert np.allclose(
self.wrapper.predict_on_batch(self.dataset[0][0].unsqueeze(0),
20)[0].detach().numpy(), l[0])
assert np.allclose(
self.wrapper.predict_on_batch(self.dataset[19][0].unsqueeze(0),
20)[0].detach().numpy(), l[19])
assert l.shape == (len(self.dataset), 1, 20)
# Test generators
l_gen = self.wrapper.predict_on_dataset_generator(self.dataset,
10,
20,
use_cuda=False,
workers=0)
assert np.allclose(next(l_gen)[0], l[0])
for last in l_gen:
pass # Get last item
assert np.allclose(last[-1], l[-1])
# Test Half
l_gen = self.wrapper.predict_on_dataset_generator(self.dataset,
10,
20,
use_cuda=False,
workers=0,
half=True)
l = self.wrapper.predict_on_dataset(self.dataset,
10,
20,
use_cuda=False,
workers=0,
half=True)
assert next(l_gen).dtype == np.float16
assert l.dtype == np.float16
def test_states(self):
input = torch.randn([1, 3, 10, 10])
def pred_with_dropout(replicate_in_memory):
self.wrapper = ModelWrapper(
self.model,
self.criterion,
replicate_in_memory=replicate_in_memory)
self.wrapper.train()
# Dropout make the pred changes
preds = torch.stack([
self.wrapper.predict_on_batch(input, iterations=1, cuda=False)
for _ in range(10)
]).view(10, -1)
assert not torch.allclose(torch.mean(preds, 0), preds[0])
pred_with_dropout(replicate_in_memory=True)
pred_with_dropout(replicate_in_memory=False)
def pred_without_dropout(replicate_in_memory):
self.wrapper = ModelWrapper(
self.model,
self.criterion,
replicate_in_memory=replicate_in_memory)
# Dropout is not active in eval
self.wrapper.eval()
preds = torch.stack([
self.wrapper.predict_on_batch(input, iterations=1, cuda=False)
for _ in range(10)
]).view(10, -1)
assert torch.allclose(torch.mean(preds, 0), preds[0])
pred_without_dropout(replicate_in_memory=True)
pred_without_dropout(replicate_in_memory=False)
def test_add_metric(self):
self.wrapper.add_metric('cls_report', lambda: ClassificationReport(2))
assert 'test_cls_report' in self.wrapper.metrics
assert 'train_cls_report' in self.wrapper.metrics
self.wrapper.train_on_dataset(self.dataset, self.optim, 32, 2, False)
self.wrapper.test_on_dataset(self.dataset, 32, False)
assert (self.wrapper.metrics['train_cls_report'].value['accuracy'] !=
0).any()
assert (self.wrapper.metrics['test_cls_report'].value['accuracy'] !=
0).any()
def test_train_and_test(self):
res = self.wrapper.train_and_test_on_datasets(
self.dataset,
self.dataset,
self.optim,
32,
5,
False,
return_best_weights=False)
assert len(res) == 5
res = self.wrapper.train_and_test_on_datasets(self.dataset,
self.dataset,
self.optim,
32,
5,
False,
return_best_weights=True)
assert len(res) == 2
assert len(res[0]) == 5
assert isinstance(res[1], dict)
mock = Mock()
mock.side_effect = (((np.linspace(0, 50) - 10) / 10)**2).tolist()
self.wrapper.test_on_dataset = mock
res = self.wrapper.train_and_test_on_datasets(self.dataset,
self.dataset,
self.optim,
32,
50,
False,
return_best_weights=True,
patience=1)
assert len(res) == 2
assert len(res[0]) < 50
mock = Mock()
mock.side_effect = (((np.linspace(0, 50) - 10) / 10)**2).tolist()
self.wrapper.test_on_dataset = mock
res = self.wrapper.train_and_test_on_datasets(self.dataset,
self.dataset,
self.optim,
32,
50,
False,
return_best_weights=True,
patience=1,
min_epoch_for_es=20)
assert len(res) == 2
assert len(res[0]) < 50 and len(res[0]) > 20
def main():
args = parse_args()
use_cuda = torch.cuda.is_available()
torch.backends.cudnn.benchmark = True
random.seed(1337)
torch.manual_seed(1337)
if not use_cuda:
print("warning, the experiments would take ages to run on cpu")
hyperparams = vars(args)
active_set, test_set = get_datasets(hyperparams['initial_pool'])
heuristic = get_heuristic(hyperparams['heuristic'],
hyperparams['shuffle_prop'])
criterion = CrossEntropyLoss()
model = vgg16(pretrained=False, num_classes=10)
weights = load_state_dict_from_url(
'https://download.pytorch.org/models/vgg16-397923af.pth')
weights = {k: v for k, v in weights.items() if 'classifier.6' not in k}
model.load_state_dict(weights, strict=False)
# change dropout layer to MCDropout
model = patch_module(model)
if use_cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(),
lr=hyperparams["lr"],
momentum=0.9)
# Wraps the model into a usable API.
model = ModelWrapper(model, criterion)
logs = {}
logs['epoch'] = 0
# for prediction we use a smaller batchsize
# since it is slower
active_loop = ActiveLearningLoop(active_set,
model.predict_on_dataset,
heuristic,
hyperparams.get('n_data_to_label', 1),
batch_size=10,
iterations=hyperparams['iterations'],
use_cuda=use_cuda)
for epoch in tqdm(range(args.epoch)):
model.train_on_dataset(active_set, optimizer,
hyperparams["batch_size"], 1, use_cuda)
# Validation!
model.test_on_dataset(test_set, hyperparams["batch_size"], use_cuda)
metrics = model.metrics
if epoch % hyperparams['learning_epoch'] == 0:
should_continue = active_loop.step()
model.reset_fcs()
if not should_continue:
break
val_loss = metrics['test_loss'].value
logs = {
"val": val_loss,
"epoch": epoch,
"train": metrics['train_loss'].value,
"labeled_data": active_set._labelled,
"Next Training set size": len(active_set)
}
print(logs)