def __init__(self, context: PyTorchTrialContext) -> None:
self.context = context
self.data_config = context.get_data_config()
self.num_classes = {
"train": context.get_hparam("num_classes_train"),
"val": context.get_hparam("num_classes_val"),
}
self.num_support = {
"train": context.get_hparam("num_support_train"),
"val": context.get_hparam("num_support_val"),
}
self.num_query = {
"train": context.get_hparam("num_query_train"),
"val":
None, # Use all available examples for val at meta-test time
}
self.get_train_valid_splits()
x_dim = 1 # Omniglot is black and white
hid_dim = self.context.get_hparam("hidden_dim")
z_dim = self.context.get_hparam("embedding_dim")
def conv_block(in_channels, out_channels):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(),
nn.MaxPool2d(2),
)
self.model = self.context.wrap_model(
nn.Sequential(
conv_block(x_dim, hid_dim),
conv_block(hid_dim, hid_dim),
conv_block(hid_dim, hid_dim),
conv_block(hid_dim, z_dim),
Flatten(),
))
self.optimizer = self.context.wrap_optimizer(
torch.optim.Adam(
self.model.parameters(),
lr=self.context.get_hparam("learning_rate"),
weight_decay=self.context.get_hparam("weight_decay"),
))
self.lr_scheduler = self.context.wrap_lr_scheduler(
torch.optim.lr_scheduler.StepLR(
self.optimizer,
self.context.get_hparam("reduce_every"),
gamma=self.context.get_hparam("lr_gamma"),
), LRScheduler.StepMode.STEP_EVERY_EPOCH)
def __init__(self, context: PyTorchTrialContext) -> None:
data_dir = f'/tmp/data-rank{context.distributed.get_rank()}'
self.dm = gan.MNISTDataModule(context.get_data_config()['url'], data_dir,
batch_size=context.get_per_slot_batch_size())
channels, width, height = self.dm.size()
lm = gan.GAN(channels, width, height,
batch_size=context.get_per_slot_batch_size(),
lr=context.get_hparam('lr'),
b1=context.get_hparam('b1'),
b2=context.get_hparam('b2'),
)
super().__init__(context, lightning_module=lm)
self.dm.prepare_data()
def __init__(self, context: PyTorchTrialContext, *args, **kwargs) -> None:
lm = mnist.LitMNIST(
hidden_size=context.get_hparam('hidden_size'),
learning_rate=context.get_hparam('learning_rate'),
)
data_dir = f"/tmp/data-rank{context.distributed.get_rank()}"
self.dm = data.MNISTDataModule(
data_url=context.get_data_config()["url"],
data_dir=data_dir,
batch_size=context.get_per_slot_batch_size(),
)
super().__init__(context, lightning_module=lm, *args, **kwargs)
self.dm.prepare_data()
def test_get_pretrained_weights(
mmdet_config_dir: None, context: det_torch.PyTorchTrialContext
) -> None:
mh_mmdet.utils.CONFIG_TO_PRETRAINED = mh_mmdet.utils.get_config_pretrained_url_mapping()
path, ckpt = mh_mmdet.get_pretrained_ckpt_path("/tmp", context.get_hparam("config_file"))
assert path is not None
assert ckpt is not None
def __init__(self, context: PyTorchTrialContext) -> None:
lm = mnist.LightningMNISTClassifier(
lr=context.get_hparam('learning_rate'))
data_dir = f"/tmp/data-rank{context.distributed.get_rank()}"
self.dm = mnist.MNISTDataModule(context.get_data_config()["url"],
data_dir)
super().__init__(context, lightning_module=lm)
self.dm.prepare_data()
def __init__(self, context: pytorch.PyTorchTrialContext):
self.context = context
self.dataset_len = context.get_hparam("dataset_len")
model = nn.Linear(1, 1, False)
model.weight.data.fill_(0)
self.model = context.wrap_model(model)
opt = torch.optim.SGD(self.model.parameters(), 0.1)
self.opt = context.wrap_optimizer(opt)
def __init__(self, context: PyTorchTrialContext) -> None:
self.context = context
self.data_config = context.get_data_config()
self.criterion = CrossEntropyLabelSmooth(
context.get_hparam("num_classes"), # num classes
context.get_hparam("label_smoothing_rate"),
)
self.last_epoch_idx = -1
self.model = self.context.wrap_model(self.build_model_from_config())
self.optimizer = self.context.wrap_optimizer(
torch.optim.SGD(
self.model.parameters(),
lr=self.context.get_hparam("learning_rate"),
momentum=self.context.get_hparam("momentum"),
weight_decay=self.context.get_hparam("weight_decay"),
))
self.lr_scheduler = self.context.wrap_lr_scheduler(
self.build_lr_scheduler_from_config(self.optimizer),
step_mode=LRScheduler.StepMode.STEP_EVERY_EPOCH,
)