def test_can_return_tensor_with_more_than_one_element(tmpdir):
"""Ensure {validation,test}_step return values are not included as callback metrics.
#6623
"""
class TestModel(BoringModel):
def validation_step(self, batch, *args, **kwargs):
return {"val": torch.tensor([0, 1])}
def validation_epoch_end(self, outputs):
# ensure validation step returns still appear here
assert len(outputs) == 2
assert all(list(d) == ["val"] for d in outputs) # check keys
assert all(
torch.equal(d["val"], torch.tensor([0, 1]))
for d in outputs) # check values
def test_step(self, batch, *args, **kwargs):
return {"test": torch.tensor([0, 1])}
def test_epoch_end(self, outputs):
assert len(outputs) == 2
assert all(list(d) == ["test"] for d in outputs) # check keys
assert all(
torch.equal(d["test"], torch.tensor([0, 1]))
for d in outputs) # check values
model = TestModel()
trainer = Trainer(default_root_dir=tmpdir,
fast_dev_run=2,
enable_progress_bar=False)
trainer.fit(model)
trainer.validate(model)
trainer.test(model)
def test__logger_connector__epoch_result_store__test_multi_dataloaders(tmpdir, num_dataloaders):
"""
Tests that LoggerConnector will properly capture logged information in multi_dataloaders scenario
"""
os.environ['PL_DEV_DEBUG'] = '1'
class TestModel(BoringModel):
test_losses = {}
@Helper.decorator_with_arguments(fx_name="test_step")
def test_step(self, batch, batch_idx, dl_idx=0):
output = self.layer(batch)
loss = self.loss(batch, output)
primary_key = str(dl_idx)
if primary_key not in self.test_losses:
self.test_losses[primary_key] = []
self.test_losses[primary_key].append(loss)
self.log("test_loss", loss, on_step=True, on_epoch=True)
return {"test_loss": loss}
def test_dataloader(self):
return [torch.utils.data.DataLoader(RandomDataset(32, 64)) for _ in range(num_dataloaders)]
model = TestModel()
model.val_dataloader = None
model.test_epoch_end = None
limit_test_batches = 4
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=0,
limit_val_batches=0,
limit_test_batches=limit_test_batches,
max_epochs=1,
log_every_n_steps=1,
weights_summary=None,
)
trainer.test(model)
test_results = trainer.logger_connector._cached_results["test"]
generated = test_results(fx_name="test_step")
assert len(generated) == num_dataloaders
for dl_idx in range(num_dataloaders):
generated = len(test_results(fx_name="test_step", dl_idx=str(dl_idx)))
assert generated == limit_test_batches
test_results.has_batch_loop_finished = True
for dl_idx in range(num_dataloaders):
expected = torch.stack(model.test_losses[str(dl_idx)]).mean()
generated = test_results(fx_name="test_step", dl_idx=str(dl_idx), reduced=True)["test_loss_epoch"]
assert abs(expected.item() - generated.item()) < 1e-6
def train(hparams):
NUM_GPUS = hparams.num_gpus
USE_AMP = False # True if NUM_GPUS > 1 else False
MAX_EPOCHS = 50
dataset = load_link_dataset(hparams.dataset, hparams=hparams)
hparams.n_classes = dataset.n_classes
model = LATTELinkPredictor(hparams,
dataset,
collate_fn="triples_batch",
metrics=[hparams.dataset])
wandb_logger = WandbLogger(name=model.name(),
tags=[dataset.name()],
project="multiplex-comparison")
trainer = Trainer(
gpus=NUM_GPUS,
distributed_backend='ddp' if NUM_GPUS > 1 else None,
auto_lr_find=False,
max_epochs=MAX_EPOCHS,
early_stop_callback=EarlyStopping(monitor='val_loss',
patience=10,
min_delta=0.01,
strict=False),
logger=wandb_logger,
# regularizers=regularizers,
weights_summary='top',
amp_level='O1' if USE_AMP else None,
precision=16 if USE_AMP else 32)
trainer.fit(model)
trainer.test(model)
def test_epoch_results_cache_dp(tmpdir):
root_device = torch.device("cuda", 0)
class TestModel(BoringModel):
def training_step(self, *args, **kwargs):
result = super().training_step(*args, **kwargs)
self.log("train_loss_epoch", result["loss"], on_step=False, on_epoch=True)
return result
def training_step_end(self, training_step_outputs): # required for dp
loss = training_step_outputs["loss"].mean()
return loss
def training_epoch_end(self, outputs):
assert all(out["loss"].device == root_device for out in outputs)
assert self.trainer.callback_metrics["train_loss_epoch"].device == root_device
def validation_step(self, *args, **kwargs):
val_loss = torch.rand(1, device=torch.device("cuda", 1))
self.log("val_loss_epoch", val_loss, on_step=False, on_epoch=True)
return val_loss
def validation_epoch_end(self, outputs):
assert all(loss.device == root_device for loss in outputs)
assert self.trainer.callback_metrics["val_loss_epoch"].device == root_device
def test_step(self, *args, **kwargs):
test_loss = torch.rand(1, device=torch.device("cuda", 1))
self.log("test_loss_epoch", test_loss, on_step=False, on_epoch=True)
return test_loss
def test_epoch_end(self, outputs):
assert all(loss.device == root_device for loss in outputs)
assert self.trainer.callback_metrics["test_loss_epoch"].device == root_device
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=4)
def val_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=4)
def test_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=4)
model = TestModel()
trainer = Trainer(
default_root_dir=tmpdir,
strategy="dp",
accelerator="gpu",
devices=2,
limit_train_batches=2,
limit_val_batches=2,
max_epochs=1,
)
trainer.fit(model)
trainer.test(model)
def run_encoder(train, test, epochs):
"""
Instances and runs autoencoder.
Parameters:
train (pandas.DataFrame): DataFrame of training data
test (pandas.DataFrame): DataFrame of testing data
epochs (int): Training epochs
Returns:
Autoencoder loss on test data
"""
# Instances training dataset
data_train = MELoader(train)
# Instances testing dataset
data_test = MELoader(test)
# Instances non-mechanistic autoencoder
feats = data_train.data.shape[1]
encoder = NMEncoder(feats, feats // 2)
# Instances PyTorch Lightning trainer
trainer = Trainer(gpus=1, num_nodes=1, max_epochs=epochs)
# Performs model fitting on training set
trainer.fit(encoder, DataLoader(dataset=data_train))
# Performs test on testing set
performance = trainer.test(encoder, DataLoader(dataset=data_test))
return performance[0]["test_loss"]
def train(hparams: Namespace):
NUM_GPUS = hparams.num_gpus
USE_AMP = False # True if NUM_GPUS > 1 else False
MAX_EPOCHS = 50
neighbor_sizes = [
hparams.n_neighbors,
]
for t in range(1, hparams.t_order):
neighbor_sizes.extend([neighbor_sizes[-1] // 2])
print("neighbor_sizes", neighbor_sizes)
hparams.neighbor_sizes = neighbor_sizes
dataset = load_node_dataset(hparams.dataset,
method="LATTE",
hparams=hparams,
train_ratio=None,
dir_path=hparams.dir_path)
METRICS = [
"precision", "recall", "f1",
"accuracy" if dataset.multilabel else hparams.dataset, "top_k"
]
hparams.loss_type = "BCE" if dataset.multilabel else hparams.loss_type
hparams.n_classes = dataset.n_classes
model = LATTENodeClassifier(hparams,
dataset,
collate_fn="neighbor_sampler",
metrics=METRICS)
logger = WandbLogger(name=model.name(),
tags=[dataset.name()],
project="multiplex-comparison")
trainer = Trainer(
gpus=NUM_GPUS,
distributed_backend='ddp' if NUM_GPUS > 1 else None,
gradient_clip_val=hparams.gradient_clip_val,
# auto_lr_find=True,
max_epochs=MAX_EPOCHS,
# early_stop_callback=EarlyStopping(monitor='val_loss', patience=5, min_delta=0.001, strict=False),
logger=logger,
amp_level='O1' if USE_AMP else None,
precision=16 if USE_AMP else 32)
trainer.fit(model)
trainer.test(model)
def run_encoder(train,
test,
epochs,
width,
depth,
dropout_prob=0.2,
reg_coef=0):
"""
Instances and runs extendable autoencoder.
Parameters:
train (pandas.DataFrame): DataFrame of training data
test (pandas.DataFrame): DataFrame of testing data
epochs (int): Training epochs
width (int): Number of latent attributes
depth (int): Number of encoding/decoding layers
dropout_prob (float, default=0.2): Probability of drop-out
reg_coef (float, default=0): Regularization coefficient
Returns:
Autoencoder loss on test data
"""
# Instances training dataset
data_train = MELoader(train)
# Instances testing dataset
data_test = MELoader(test)
# Instances non-mechanistic autoencoder
feats = data_train.data.shape[1]
encoder = NMEncoder(feats,
width,
dropout_prob=dropout_prob,
n_layers=depth,
reg_coef=reg_coef)
# Instances PyTorch Lightning trainer
trainer = Trainer(
auto_scale_batch_size=True,
auto_select_gpus=True,
checkpoint_callback=False,
gpus=1,
logger=False,
max_epochs=epochs,
# progress_bar_refresh_rate=0,
weights_summary=None,
)
# Performs model fitting on training set
trainer.fit(encoder, DataLoader(dataset=data_train))
# Performs test on testing set
performance = trainer.test(encoder, DataLoader(dataset=data_test))
loss = performance[0]["test_loss"]
latent = performance[0]["latent"]
return loss, latent
def test_metrics_reset(tmpdir):
"""Tests that metrics are reset correctly after the end of the train/val/test epoch."""
class TestModel(LightningModule):
def __init__(self):
super().__init__()
self.layer = torch.nn.Linear(32, 1)
def _create_metrics(self):
acc = Accuracy()
acc.reset = mock.Mock(side_effect=acc.reset)
ap = AveragePrecision(num_classes=1, pos_label=1)
ap.reset = mock.Mock(side_effect=ap.reset)
return acc, ap
def setup(self, stage):
fn = stage
if fn == "fit":
for stage in ("train", "validate"):
acc, ap = self._create_metrics()
self.add_module(f"acc_{fn}_{stage}", acc)
self.add_module(f"ap_{fn}_{stage}", ap)
else:
acc, ap = self._create_metrics()
stage = self.trainer.state.stage
self.add_module(f"acc_{fn}_{stage}", acc)
self.add_module(f"ap_{fn}_{stage}", ap)
def forward(self, x):
return self.layer(x)
def _step(self, batch):
fn, stage = self.trainer.state.fn, self.trainer.state.stage
logits = self(batch)
loss = logits.sum()
self.log(f"loss/{fn}_{stage}", loss)
acc = self._modules[f"acc_{fn}_{stage}"]
ap = self._modules[f"ap_{fn}_{stage}"]
preds = torch.rand(len(batch)) # Fake preds
labels = torch.randint(0, 1, [len(batch)]) # Fake targets
acc(preds, labels)
ap(preds, labels)
# Metric.forward calls reset so reset the mocks here
acc.reset.reset_mock()
ap.reset.reset_mock()
self.log(f"acc/{fn}_{stage}", acc)
self.log(f"ap/{fn}_{stage}", ap)
return loss
def training_step(self, batch, batch_idx, *args, **kwargs):
return self._step(batch)
def validation_step(self, batch, batch_idx, *args, **kwargs):
if self.trainer.sanity_checking:
return
return self._step(batch)
def test_step(self, batch, batch_idx, *args, **kwargs):
return self._step(batch)
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=1)
return [optimizer], [lr_scheduler]
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64))
def val_dataloader(self):
return DataLoader(RandomDataset(32, 64))
def test_dataloader(self):
return DataLoader(RandomDataset(32, 64))
def _assert_called(model, fn, stage):
acc = model._modules[f"acc_{fn}_{stage}"]
ap = model._modules[f"ap_{fn}_{stage}"]
acc.reset.assert_called_once()
ap.reset.assert_called_once()
model = TestModel()
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=2,
limit_val_batches=2,
limit_test_batches=2,
max_epochs=1,
enable_progress_bar=False,
num_sanity_val_steps=2,
enable_checkpointing=False,
)
trainer.fit(model)
_assert_called(model, "fit", "train")
_assert_called(model, "fit", "validate")
trainer.validate(model)
_assert_called(model, "validate", "validate")
trainer.test(model)
_assert_called(model, "test", "test")
def test_fx_validator_integration(tmpdir):
"""Tries to log inside all `LightningModule` and `Callback` hooks to check any expected errors."""
not_supported = {
None: "`self.trainer` reference is not registered",
"on_before_accelerator_backend_setup": "You can't",
"setup": "You can't",
"configure_sharded_model": "You can't",
"on_configure_sharded_model": "You can't",
"configure_optimizers": "You can't",
"on_fit_start": "You can't",
"on_pretrain_routine_start": "You can't",
"on_pretrain_routine_end": "You can't",
"train_dataloader": "You can't",
"val_dataloader": "You can't",
"on_validation_end": "You can't",
"on_train_end": "You can't",
"on_fit_end": "You can't",
"teardown": "You can't",
"on_sanity_check_start": "You can't",
"on_sanity_check_end": "You can't",
"prepare_data": "You can't",
"configure_callbacks": "You can't",
"on_validation_model_eval": "You can't",
"on_validation_model_train": "You can't",
"lr_scheduler_step": "You can't",
"on_save_checkpoint": "You can't",
"on_load_checkpoint": "You can't",
"on_exception": "You can't",
}
model = HookedModel(not_supported)
with pytest.warns(UserWarning, match=not_supported[None]):
model.log("foo", 1)
callback = HookedCallback(not_supported)
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=2,
limit_train_batches=1,
limit_val_batches=1,
limit_test_batches=1,
limit_predict_batches=1,
callbacks=callback,
)
with pytest.deprecated_call(match="is deprecated in"):
trainer.fit(model)
not_supported.update({
# `lightning_module` ref is now present from the `fit` call
"on_before_accelerator_backend_setup": "You can't",
"test_dataloader": "You can't",
"on_test_model_eval": "You can't",
"on_test_model_train": "You can't",
"on_test_end": "You can't",
})
with pytest.deprecated_call(match="is deprecated in"):
trainer.test(model, verbose=False)
not_supported.update(
{k: "result collection is not registered yet"
for k in not_supported})
not_supported.update({
"predict_dataloader":
"result collection is not registered yet",
"on_predict_model_eval":
"result collection is not registered yet",
"on_predict_start":
"result collection is not registered yet",
"on_predict_epoch_start":
"result collection is not registered yet",
"on_predict_batch_start":
"result collection is not registered yet",
"predict_step":
"result collection is not registered yet",
"on_predict_batch_end":
"result collection is not registered yet",
"on_predict_epoch_end":
"result collection is not registered yet",
"on_predict_end":
"result collection is not registered yet",
})
with pytest.deprecated_call(match="is deprecated in"):
trainer.predict(model)
def train(hparams):
EMBEDDING_DIM = 128
USE_AMP = None
NUM_GPUS = hparams.num_gpus
MAX_EPOCHS = 1000
batch_order = 11
dataset = load_node_dataset(hparams.dataset,
hparams.method,
hparams=hparams,
train_ratio=hparams.train_ratio)
METRICS = [
"precision",
"recall",
"f1",
"accuracy",
"top_k" if dataset.multilabel else "ogbn-mag",
]
if hparams.method == "HAN":
USE_AMP = False
model_hparams = {
"embedding_dim": EMBEDDING_DIM,
"batch_size": 2**batch_order,
"num_layers": 2,
"collate_fn": "HAN_batch",
"train_ratio": dataset.train_ratio,
"loss_type": "BINARY_CROSS_ENTROPY"
if dataset.multilabel else "SOFTMAX_CROSS_ENTROPY",
"n_classes": dataset.n_classes,
"lr": 0.001,
}
model = HAN(Namespace(**model_hparams),
dataset=dataset,
metrics=METRICS)
elif hparams.method == "GTN":
USE_AMP = False
model_hparams = {
"embedding_dim": EMBEDDING_DIM,
"num_channels": len(dataset.metapaths),
"num_layers": 2,
"batch_size": 2**batch_order,
"collate_fn": "HAN_batch",
"train_ratio": dataset.train_ratio,
"loss_type": "BINARY_CROSS_ENTROPY"
if dataset.multilabel else "SOFTMAX_CROSS_ENTROPY",
"n_classes": dataset.n_classes,
"lr": 0.001,
}
model = GTN(Namespace(**model_hparams),
dataset=dataset,
metrics=METRICS)
elif hparams.method == "MetaPath2Vec":
USE_AMP = False
model_hparams = {
"embedding_dim": EMBEDDING_DIM,
"walk_length": 50,
"context_size": 7,
"walks_per_node": 5,
"num_negative_samples": 5,
"sparse": True,
"batch_size": 400,
"train_ratio": dataset.train_ratio,
"n_classes": dataset.n_classes,
"lr": 0.01,
}
model = MetaPath2Vec(Namespace(**model_hparams),
dataset=dataset,
metrics=METRICS)
elif hparams.method == "HGT":
USE_AMP = False
model_hparams = {
"embedding_dim": EMBEDDING_DIM,
"num_channels": len(dataset.metapaths),
"n_layers": 2,
"attn_heads": 8,
"attn_dropout": 0.2,
"prev_norm": True,
"last_norm": True,
"nb_cls_dense_size": 0,
"nb_cls_dropout": 0.0,
"use_class_weights": False,
"batch_size": 2**batch_order,
"n_epoch": MAX_EPOCHS,
"train_ratio": dataset.train_ratio,
"loss_type":
"BCE" if dataset.multilabel else "SOFTMAX_CROSS_ENTROPY",
"n_classes": dataset.n_classes,
"collate_fn": "collate_HGT_batch",
"lr": 0.001, # Not used here, defaults to 1e-3
}
model = HGT(Namespace(**model_hparams), dataset, metrics=METRICS)
elif "LATTE" in hparams.method:
USE_AMP = False
num_gpus = 1
if "-1" in hparams.method:
n_layers = 1
elif "-2" in hparams.method:
n_layers = 2
elif "-3" in hparams.method:
n_layers = 3
else:
n_layers = 2
model_hparams = {
"embedding_dim": EMBEDDING_DIM,
"layer_pooling": "concat",
"n_layers": n_layers,
"batch_size": 2**batch_order,
"nb_cls_dense_size": 0,
"nb_cls_dropout": 0.4,
"activation": "relu",
"dropout": 0.2,
"attn_heads": 2,
"attn_activation": "sharpening",
"batchnorm": False,
"layernorm": False,
"edge_sampling": False,
"edge_threshold": 0.5,
"attn_dropout": 0.2,
"loss_type":
"BCE" if dataset.multilabel else "SOFTMAX_CROSS_ENTROPY",
"use_proximity": True if "proximity" in hparams.method else False,
"neg_sampling_ratio": 2.0,
"n_classes": dataset.n_classes,
"use_class_weights": False,
"lr": 0.001,
"momentum": 0.9,
"weight_decay": 1e-2,
}
model_hparams.update(hparams.__dict__)
metrics = [
"precision", "recall", "micro_f1", "macro_f1",
"accuracy" if dataset.multilabel else "ogbn-mag", "top_k"
]
model = LATTENodeClf(Namespace(**model_hparams),
dataset,
collate_fn="neighbor_sampler",
metrics=metrics)
MAX_EPOCHS = 250
wandb_logger = WandbLogger(name=model.name(),
tags=[dataset.name()],
anonymous=True,
project="anon-demo")
wandb_logger.log_hyperparams(model_hparams)
trainer = Trainer(gpus=NUM_GPUS,
distributed_backend='dp' if NUM_GPUS > 1 else None,
max_epochs=MAX_EPOCHS,
stochastic_weight_avg=True,
callbacks=[
EarlyStopping(monitor='val_loss',
patience=10,
min_delta=0.0001,
strict=False)
],
logger=wandb_logger,
weights_summary='top',
amp_level='O1' if USE_AMP and NUM_GPUS > 0 else None,
precision=16 if USE_AMP else 32)
trainer.fit(model)
model.register_hooks()
trainer.test(model)
wandb_logger.log_metrics(
model.clustering_metrics(n_runs=10, compare_node_types=False))
def train(hparams):
EMBEDDING_DIM = 128
NUM_GPUS = hparams.num_gpus
batch_order = 11
dataset = load_node_dataset(hparams.dataset, hparams.method, hparams=hparams, train_ratio=hparams.train_ratio)
METRICS = ["precision", "recall", "f1", "accuracy", "top_k" if dataset.multilabel else "ogbn-mag", ]
if hparams.method == "HAN":
USE_AMP = True
model_hparams = {
"embedding_dim": EMBEDDING_DIM,
"batch_size": 2 ** batch_order * NUM_GPUS,
"num_layers": 2,
"collate_fn": "HAN_batch",
"train_ratio": dataset.train_ratio,
"loss_type": "BINARY_CROSS_ENTROPY" if dataset.multilabel else "SOFTMAX_CROSS_ENTROPY",
"n_classes": dataset.n_classes,
"lr": 0.0005 * NUM_GPUS,
}
model = HAN(Namespace(**model_hparams), dataset=dataset, metrics=METRICS)
elif hparams.method == "GTN":
USE_AMP = True
model_hparams = {
"embedding_dim": EMBEDDING_DIM,
"num_channels": len(dataset.metapaths),
"num_layers": 2,
"batch_size": 2 ** batch_order * NUM_GPUS,
"collate_fn": "HAN_batch",
"train_ratio": dataset.train_ratio,
"loss_type": "BINARY_CROSS_ENTROPY" if dataset.multilabel else "SOFTMAX_CROSS_ENTROPY",
"n_classes": dataset.n_classes,
"lr": 0.0005 * NUM_GPUS,
}
model = GTN(Namespace(**model_hparams), dataset=dataset, metrics=METRICS)
elif hparams.method == "MetaPath2Vec":
USE_AMP = True
model_hparams = {
"embedding_dim": EMBEDDING_DIM,
"walk_length": 50,
"context_size": 7,
"walks_per_node": 5,
"num_negative_samples": 5,
"sparse": True,
"batch_size": 400 * NUM_GPUS,
"train_ratio": dataset.train_ratio,
"n_classes": dataset.n_classes,
"lr": 0.01 * NUM_GPUS,
}
model = MetaPath2Vec(Namespace(**model_hparams), dataset=dataset, metrics=METRICS)
elif "LATTE" in hparams.method:
USE_AMP = False
num_gpus = 1
if "-1" in hparams.method:
t_order = 1
elif "-2" in hparams.method:
t_order = 2
elif "-3" in hparams.method:
t_order = 3
else:
t_order = 2
model_hparams = {
"embedding_dim": EMBEDDING_DIM,
"t_order": t_order,
"batch_size": 2 ** batch_order * max(num_gpus, 1),
"nb_cls_dense_size": 0,
"nb_cls_dropout": 0.4,
"activation": "relu",
"attn_heads": 2,
"attn_activation": "sharpening",
"attn_dropout": 0.2,
"loss_type": "BCE" if dataset.multilabel else "SOFTMAX_CROSS_ENTROPY",
"use_proximity": True if "proximity" in hparams.method else False,
"neg_sampling_ratio": 2.0,
"n_classes": dataset.n_classes,
"use_class_weights": False,
"lr": 0.001 * num_gpus,
"momentum": 0.9,
"weight_decay": 1e-2,
}
metrics = ["precision", "recall", "micro_f1",
"accuracy" if dataset.multilabel else "ogbn-mag", "top_k"]
model = LATTENodeClassifier(Namespace(**model_hparams), dataset, collate_fn="neighbor_sampler", metrics=metrics)
MAX_EPOCHS = 250
wandb_logger = WandbLogger(name=model.name(),
tags=[dataset.name()],
project="multiplex-comparison")
wandb_logger.log_hyperparams(model_hparams)
trainer = Trainer(
gpus=NUM_GPUS, auto_select_gpus=True,
distributed_backend='dp' if NUM_GPUS > 1 else None,
max_epochs=MAX_EPOCHS,
callbacks=[EarlyStopping(monitor='val_loss', patience=10, min_delta=0.0001, strict=False)],
logger=wandb_logger,
weights_summary='top',
amp_level='O1' if USE_AMP else None,
precision=16 if USE_AMP else 32
)
# trainer.fit(model)
trainer.fit(model, train_dataloader=model.valtrain_dataloader(), val_dataloaders=model.test_dataloader())
trainer.test(model)
def test_metrics_reset(tmpdir):
"""Tests that metrics are reset correctly after the end of the train/val/test epoch."""
class TestModel(LightningModule):
def __init__(self):
super().__init__()
self.layer = torch.nn.Linear(32, 1)
for stage in ['train', 'val', 'test']:
acc = Accuracy()
acc.reset = mock.Mock(side_effect=acc.reset)
ap = AveragePrecision(num_classes=1, pos_label=1)
ap.reset = mock.Mock(side_effect=ap.reset)
self.add_module(f"acc_{stage}", acc)
self.add_module(f"ap_{stage}", ap)
def forward(self, x):
return self.layer(x)
def _step(self, stage, batch):
labels = (batch.detach().sum(1) > 0).float() # Fake some targets
logits = self.forward(batch)
loss = torch.nn.functional.binary_cross_entropy_with_logits(
logits, labels.unsqueeze(1))
probs = torch.sigmoid(logits.detach())
self.log(f"loss/{stage}", loss)
acc = self._modules[f"acc_{stage}"]
ap = self._modules[f"ap_{stage}"]
labels_int = labels.to(torch.long)
acc(probs.flatten(), labels_int)
ap(probs.flatten(), labels_int)
# Metric.forward calls reset so reset the mocks here
acc.reset.reset_mock()
ap.reset.reset_mock()
self.log(f"{stage}/accuracy", acc)
self.log(f"{stage}/ap", ap)
return loss
def training_step(self, batch, batch_idx, *args, **kwargs):
return self._step('train', batch)
def validation_step(self, batch, batch_idx, *args, **kwargs):
return self._step('val', batch)
def test_step(self, batch, batch_idx, *args, **kwargs):
return self._step('test', batch)
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=1)
return [optimizer], [lr_scheduler]
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64))
def val_dataloader(self):
return DataLoader(RandomDataset(32, 64))
def test_dataloader(self):
return DataLoader(RandomDataset(32, 64))
def _assert_epoch_end(self, stage):
acc = self._modules[f"acc_{stage}"]
ap = self._modules[f"ap_{stage}"]
acc.reset.assert_called_once()
ap.reset.assert_called_once()
def teardown(self, stage):
if stage == TrainerFn.FITTING:
self._assert_epoch_end('train')
self._assert_epoch_end('val')
elif stage == TrainerFn.VALIDATING:
self._assert_epoch_end('val')
elif stage == TrainerFn.TESTING:
self._assert_epoch_end('test')
def _assert_called(model, stage):
acc = model._modules[f"acc_{stage}"]
ap = model._modules[f"ap_{stage}"]
assert acc.reset.call_count == 1
acc.reset.reset_mock()
assert ap.reset.call_count == 1
ap.reset.reset_mock()
model = TestModel()
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=2,
limit_val_batches=2,
limit_test_batches=2,
max_epochs=1,
progress_bar_refresh_rate=0,
num_sanity_val_steps=2,
)
trainer.fit(model)
_assert_called(model, 'train')
_assert_called(model, 'val')
trainer.validate(model)
_assert_called(model, 'val')
trainer.test(model)
_assert_called(model, 'test')
def main(model_name='usp_1d',
max_epochs=1020,
data_dir='./data',
dataset='sc09',
ps=False,
wn=False,
mx=False,
perc=1,
ts=False,
fd=False,
tts=False,
tm=False,
train=True,
order=True,
model_num=None):
model_name = model_name + '_' + str(int(perc * 100))
dataset_f = dataset
nsynth_class = None
if dataset == 'sc09':
sample_rate = 16000
n_classes = 10
length = 1
batch_size = 256
train_transform = transforms.Compose([
torchaudio.transforms.Resample(orig_freq=16000,
new_freq=sample_rate),
pad,
])
elif dataset == 'sc':
sample_rate = 16000
n_classes = 35
batch_size = 128
length = 1
train_transform = transforms.Compose([
torchaudio.transforms.Resample(orig_freq=16000,
new_freq=sample_rate),
partial(pad, length=length),
])
elif dataset == 'nsynth11':
sample_rate = 16000
n_classes = 11
batch_size = 32
max_epochs = 120
dataset = 'nsynth'
nsynth_class = 'instrument_family'
length = 4
train_transform = transforms.Compose([
torchaudio.transforms.Resample(orig_freq=16000,
new_freq=sample_rate),
partial(pad, length=length),
])
elif dataset == 'nsynth128':
sample_rate = 16000
n_classes = 128
batch_size = 16
max_epochs = 120
dataset = 'nsynth'
nsynth_class = 'pitch'
length = 4
train_transform = transforms.Compose([
torchaudio.transforms.Resample(orig_freq=16000,
new_freq=sample_rate),
partial(pad, length=length),
])
elif dataset == 'esc50':
sample_rate = 16000
max_epochs = 2000
n_classes = 50
batch_size = 64
length = 5
train_transform = transforms.Compose([
torchaudio.transforms.Resample(orig_freq=44100,
new_freq=sample_rate),
partial(pad, length=length),
])
elif dataset == 'esc10':
sample_rate = 16000
n_classes = 10
max_epochs = 2000
batch_size = 64
length = 5
train_transform = transforms.Compose([
torchaudio.transforms.Resample(orig_freq=44100,
new_freq=sample_rate),
partial(pad, length=length),
])
# model_name = model_name + '_' + dataset
spec_transform = None
aug_transform = []
if order:
if fd:
aug_transform.append(transforms.RandomApply(add_fade))
model_name = model_name + '_fd'
if tm:
aug_transform.append(transforms.RandomApply(time_masking))
model_name = model_name + '_tm'
if tts:
aug_transform.append(
transforms.RandomApply(partial(time_stret, length=length)))
model_name = model_name + '_tts'
if ps:
aug_transform.append(transforms.RandomApply(pitch_shift))
model_name = model_name + '_ps'
if ts:
aug_transform.append(transforms.RandomApply(time_shift))
model_name = model_name + '_ts'
if wn:
aug_transform.append(transforms.RandomApply(add_white_noise))
model_name = model_name + '_wn'
if mx:
m_x = Mixed_Noise(data_dir, sample_rate)
aug_transform.append(transforms.RandomApply(m_x))
model_name = model_name + '_mx'
else:
if mx:
m_x = Mixed_Noise(data_dir, sample_rate)
aug_transform.append(transforms.RandomApply(m_x))
model_name = model_name + '_mx'
if wn:
aug_transform.append(transforms.RandomApply(add_white_noise))
model_name = model_name + '_wn'
if ts:
aug_transform.append(transforms.RandomApply(time_shift))
model_name = model_name + '_ts'
if ps:
aug_transform.append(transforms.RandomApply(pitch_shift))
model_name = model_name + '_ps'
if fd:
aug_transform.append(transforms.RandomApply(add_fade))
model_name = model_name + '_fd'
if tts:
aug_transform.append(
transforms.RandomApply(partial(time_stret, length=length)))
model_name = model_name + '_tts'
if tm:
aug_transform.append(transforms.RandomApply(time_masking))
model_name = model_name + '_tm'
aug_transform = transforms.Compose(aug_transform)
print(f"Model: {model_name}")
net = Main(batch_size=batch_size,
sampling_rate=sample_rate,
data_dir=data_dir,
dataset=dataset,
perc=perc,
nsynth_class=nsynth_class,
n_classes=n_classes,
train_transform=train_transform,
aug_transform=aug_transform,
spec_transform=spec_transform,
model=model_name)
model_path = os.path.join(MODELS_FOLDER, model_name, dataset_f)
os.makedirs(model_path, exist_ok=True)
checkpoint_callback = ModelCheckpoint(
filepath=model_path,
save_last=True,
mode='min',
period=10,
save_top_k=20000000,
)
if model_num is not None:
checkpoint = os.path.join(model_path,
get_last(os.listdir(model_path), model_num))
elif os.path.exists(model_path) and len(os.listdir(model_path)) > 0:
checkpoint = os.path.join(model_path, get_last(os.listdir(model_path)))
else:
checkpoint = None
logger = TensorBoardLogger(save_dir=LOGS_FOLDER,
version=dataset_f,
name=model_name)
# finetune in real-time
print(f"Loading model: {checkpoint}")
def to_device(batch, device):
(x1, x2), y = batch
x1 = x1.to(device)
y = y.to(device).squeeze()
return x1, y
online_eval = SSLOnlineEvaluator(hidden_dim=512,
z_dim=512,
num_classes=n_classes,
train_transform=train_transform,
data_dir=data_dir,
dataset=dataset,
batch_size=batch_size,
nsynth_class=nsynth_class)
online_eval.to_device = to_device
trainer = Trainer(resume_from_checkpoint=checkpoint,
distributed_backend='ddp',
max_epochs=max_epochs,
sync_batchnorm=True,
checkpoint_callback=checkpoint_callback,
logger=logger,
gpus=-1 if train else 1,
log_save_interval=25,
callbacks=[online_eval])
if train:
trainer.fit(net)
else:
trainer.test(net)