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import logging | |
import higher | |
import pytorch_lightning as pl | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from pytorch_lightning import Trainer | |
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint | |
from pytorch_lightning.loggers import WandbLogger | |
from pytorch_lightning.metrics.functional.classification import accuracy | |
from torchmeta.datasets.helpers import omniglot | |
from torchmeta.utils.data import BatchMetaDataLoader | |
from config import parser | |
logger = logging.getLogger(__name__) | |
class ConvolutionalNeuralNetwork(nn.Module): | |
def __init__(self, in_channels, out_features, hidden_size=64): | |
super(ConvolutionalNeuralNetwork, self).__init__() | |
self.in_channels = in_channels | |
self.out_features = out_features | |
self.hidden_size = hidden_size | |
self.features = nn.Sequential( | |
self.conv3x3(in_channels, hidden_size), | |
self.conv3x3(hidden_size, hidden_size), | |
self.conv3x3(hidden_size, hidden_size), | |
self.conv3x3(hidden_size, hidden_size), | |
) | |
self.classifier = nn.Linear(hidden_size, out_features) | |
def forward(self, inputs, params=None): | |
features = self.features(inputs) | |
features = features.view((features.size(0), -1)) | |
logits = self.classifier(features) | |
return logits | |
def conv3x3(self, in_channels, out_channels, **kwargs): | |
return nn.Sequential( | |
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, **kwargs), | |
nn.BatchNorm2d(out_channels, momentum=1.0, track_running_stats=False), | |
nn.ReLU(), | |
nn.MaxPool2d(2), | |
) | |
class MAML(pl.LightningModule): | |
def __init__(self, model): | |
super().__init__() | |
self.model = model | |
self.accuracy = accuracy | |
def forward(self, x): | |
return self.model(x) | |
def training_step(self, batch, batch_idx, optimizer_idx): | |
# batch of tasks | |
meta_optimizer, inner_optimiser = self.optimizers() | |
train_inputs, train_targets = batch["train"] | |
test_inputs, test_targets = batch["test"] | |
outer_loss = torch.tensor(0.0, device=self.device) | |
for task_idx, (train_input, train_target, test_input, test_target) in enumerate( | |
zip(train_inputs, train_targets, test_inputs, test_targets) | |
): | |
with higher.innerloop_ctx( | |
self.model, inner_optimiser, copy_initial_weights=False | |
) as (fmodel, diffopt): | |
train_logit = fmodel(train_input) | |
inner_loss = F.cross_entropy(train_logit, train_target) | |
diffopt.step(inner_loss) | |
test_logit = fmodel(test_input) | |
outer_loss += F.cross_entropy(test_logit, test_target) | |
self.log_dict( | |
{ | |
"outer_loss": outer_loss, | |
"accuracy": self.accuracy(test_logit, test_target), | |
} | |
) | |
outer_loss.div_(args.batch_size) | |
self.manual_backward(outer_loss, meta_optimizer) | |
return outer_loss | |
def configure_optimizers(self): | |
meta_optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) | |
inner_optimiser = torch.optim.SGD(self.parameters(), lr=args.step_size) | |
return [meta_optimizer, inner_optimiser] | |
class OmniglotDataModule(pl.LightningDataModule): | |
def __init__( | |
self, | |
data_dir: str, | |
shots: int, | |
ways: int, | |
shuffle_ds: bool, | |
test_shots: int, | |
meta_train: bool, | |
download: bool, | |
batch_size: str, | |
shuffle: bool, | |
num_workers: int, | |
): | |
super().__init__() | |
self.data_dir = data_dir | |
self.shots = shots | |
self.ways = ways | |
self.shuffle_ds = shuffle_ds | |
self.test_shots = test_shots | |
self.meta_train = meta_train | |
self.download = download | |
self.batch_size = batch_size | |
self.shuffle = shuffle | |
self.num_workers = num_workers | |
def setup(self, stage=None): | |
self.task_dataset = omniglot( | |
self.data_dir, | |
shots=self.shots, | |
ways=self.ways, | |
shuffle=self.shuffle_ds, | |
test_shots=self.test_shots, | |
meta_train=self.meta_train, | |
download=self.download, | |
) | |
def train_dataloader(self): | |
return BatchMetaDataLoader( | |
self.task_dataset, | |
batch_size=self.batch_size, | |
shuffle=self.shuffle, | |
num_workers=self.num_workers, | |
) | |
if __name__ == "__main__": | |
logger.warning( | |
"This script is an example to showcase the data-loading " | |
"features of Torchmeta in conjunction with using higher to " | |
'make models "unrollable" and optimizers differentiable, ' | |
"and as such has been very lightly tested." | |
) | |
args = parser.parse_args() | |
dm = OmniglotDataModule( | |
"data", | |
shots=args.num_shots, | |
ways=args.num_ways, | |
shuffle_ds=True, | |
test_shots=15, | |
meta_train=True, | |
download=args.download, | |
batch_size=args.batch_size, | |
shuffle=True, | |
num_workers=8, | |
) | |
model = MAML( | |
model=ConvolutionalNeuralNetwork(1, args.num_ways, hidden_size=args.hidden_size) | |
) | |
trainer = Trainer( | |
automatic_optimization=False, | |
profiler=True, | |
max_epochs=args.n_epochs, | |
fast_dev_run=False, | |
num_sanity_val_steps=2, | |
) | |
trainer.fit(model, datamodule=dm) |
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