x = x.view(-1, 4 * 4 * 50)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
if __name__ == '__main__':
base_model = Net(128)
trainer = PyTorchImageClassificationTrainer(
base_model,
dataset_cls="MNIST",
dataset_kwargs={
"root": "data/mnist",
"download": True
},
dataloader_kwargs={"batch_size": 32},
optimizer_kwargs={"lr": 1e-3},
trainer_kwargs={"max_epochs": 1})
simple_startegy = RandomStrategy()
exp = RetiariiExperiment(base_model, trainer, [], simple_startegy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnist_search'
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 10
exp_config.training_service.use_active_gpu = False
exp.run(exp_config, 8081 + random.randint(0, 100))
from nni.retiarii.trainer import PyTorchImageClassificationTrainer
from darts_model import CNN
if __name__ == '__main__':
base_model = CNN(32, 3, 16, 10, 8)
trainer = PyTorchImageClassificationTrainer(
base_model,
dataset_cls="CIFAR10",
dataset_kwargs={
"root": "data/cifar10",
"download": True
},
dataloader_kwargs={"batch_size": 32},
optimizer_kwargs={"lr": 1e-3},
trainer_kwargs={"max_epochs": 1})
simple_startegy = TPEStrategy()
exp = RetiariiExperiment(base_model, trainer, [], simple_startegy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'darts_search'
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 10
exp_config.trial_gpu_number = 1
exp_config.training_service.use_active_gpu = True
exp_config.training_service.gpu_indices = [1, 2]
exp.run(exp_config, 8081, debug=True)
_DEFAULT_KERNEL_SIZES, _DEFAULT_NUM_LAYERS,
_DEFAULT_SKIPS)
trainer = PyTorchImageClassificationTrainer(
base_model,
dataset_cls="CIFAR10",
dataset_kwargs={
"root": "data/cifar10",
"download": True
},
dataloader_kwargs={"batch_size": 32},
optimizer_kwargs={"lr": 1e-3},
trainer_kwargs={"max_epochs": 1})
# new interface
applied_mutators = []
applied_mutators.append(BlockMutator('mutable_0'))
applied_mutators.append(BlockMutator('mutable_1'))
simple_startegy = TPEStrategy()
exp = RetiariiExperiment(base_model, trainer, applied_mutators,
simple_startegy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnasnet_search'
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 10
exp_config.training_service.use_active_gpu = False
exp.run(exp_config, 8081)
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = dict()
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res["acc{}".format(k)] = correct_k.mul_(1.0 / batch_size).item()
return res
if __name__ == '__main__':
base_model = CNN(32, 3, 16, 10, 8)
dataset_train, dataset_valid = get_dataset("cifar10")
criterion = nn.CrossEntropyLoss()
optim = torch.optim.SGD(base_model.parameters(), 0.025, momentum=0.9, weight_decay=3.0E-4)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, 50, eta_min=0.001)
trainer = DartsTrainer(
model=base_model,
loss=criterion,
metrics=lambda output, target: accuracy(output, target, topk=(1,)),
optimizer=optim,
num_epochs=50,
dataset=dataset_train,
batch_size=32,
log_frequency=10,
unrolled=False
)
exp = RetiariiExperiment(base_model, trainer)
exp.run()