class TrainingTestCase(unittest.TestCase):
""" Test cases to see if we can build a model,
train it, evaluate, quantize it and prune it"""
def setUp(self):
dataset, classes = prepare_cifar10()
self.models_path = "./models"
self.trainer = SimpleTrainer(pruning=True,
datasets=dataset,
models_path=self.models_path)
experiment = ax.Experiment(
name="model_test",
search_space=search_space(),
)
sobol = ax.Models.SOBOL(search_space=experiment.search_space)
self.param = sobol.gen(1).arms[0].parameters
self.net = Net(self.param, classes=classes, input_shape=(3, 32, 32))
if not os.path.exists(self.models_path):
os.mkdir(self.models_path)
def tearDown(self):
shutil.rmtree(self.models_path)
shutil.rmtree("./data")
def test_training(self):
self.trainer.load_dataloaders(batch_size=4, collate_fn=None)
self.trainer.train(self.net,
self.param,
name="0",
epochs=1,
reload=False,
old_net=None)
self.trainer.evaluate(self.net.to('cpu'), quant_mode=False)
if __name__ == '__main__':
# dataiter = iter(trainloader)
# images, labels = dataiter.next()
# imshow(torchvision.utils.make_grid(images, padding=2))
# print(' '.join(classes[labels[j]] for j in range(4)))
bn = False
net = Net(bn)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
net.to(device)
# training the network
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
# forward
outputs = net(inputs)
loss = criterion(outputs, labels)
# backward
loss.backward()
#optimize
optimizer.step()