def test_metric_learning_pipeline():
"""
Test if classification pipeline can run and compute metrics.
In this test we check that LoaderMetricCallback works with
CMCMetric (ICallbackLoaderMetric).
"""
with TemporaryDirectory() as tmp_dir:
dataset_train = datasets.MnistMLDataset(root=tmp_dir, download=True)
sampler = data.BalanceBatchSampler(labels=dataset_train.get_labels(), p=5, k=10)
train_loader = DataLoader(
dataset=dataset_train, sampler=sampler, batch_size=sampler.batch_size,
)
dataset_val = datasets.MnistQGDataset(root=tmp_dir, transform=None, gallery_fraq=0.2)
val_loader = DataLoader(dataset=dataset_val, batch_size=1024)
model = DummyModel(num_features=28 * 28, num_classes=NUM_CLASSES)
optimizer = Adam(model.parameters(), lr=0.001)
sampler_inbatch = data.HardTripletsSampler(norm_required=False)
criterion = nn.TripletMarginLossWithSampler(margin=0.5, sampler_inbatch=sampler_inbatch)
callbacks = OrderedDict(
{
"cmc": dl.ControlFlowCallback(
LoaderMetricCallback(
CMCMetric(
topk_args=[1],
embeddings_key="embeddings",
labels_key="targets",
is_query_key="is_query",
),
input_key=["embeddings", "is_query"],
target_key=["targets"],
),
loaders="valid",
),
"control": dl.PeriodicLoaderCallback(
valid_loader_key="valid", valid_metric_key="cmc", valid=2
),
}
)
runner = CustomRunner(input_key="features", output_key="embeddings")
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
callbacks=callbacks,
loaders=OrderedDict({"train": train_loader, "valid": val_loader}),
verbose=False,
valid_loader="valid",
num_epochs=4,
)
assert "cmc01" in runner.loader_metrics
def run_ml_pipeline(sampler_inbatch: data.IInbatchTripletSampler) -> float:
"""
Full metric learning pipeline, including train and val.
This function is also used as minimal example in README.md, section name:
'CV - MNIST with Metric Learning'.
Args:
sampler_inbatch: sampler to forming triplets
Returns:
best metric value
"""
# 1. train and valid datasets
dataset_root = "./data"
transforms = t.Compose([t.ToTensor(), t.Normalize((0.1307, ), (0.3081, ))])
dataset_train = datasets.MnistMLDataset(
root=dataset_root,
train=True,
download=True,
transform=transforms,
)
sampler = data.BalanceBatchSampler(labels=dataset_train.get_labels(),
p=5,
k=10)
train_loader = DataLoader(dataset=dataset_train,
sampler=sampler,
batch_size=sampler.batch_size)
dataset_val = datasets.MnistQGDataset(root=dataset_root,
transform=transforms,
gallery_fraq=0.2)
val_loader = DataLoader(dataset=dataset_val, batch_size=1024)
# 2. model and optimizer
model = models.SimpleConv(features_dim=16)
optimizer = Adam(model.parameters(), lr=0.0005)
# 3. criterion with triplets sampling
criterion = nn.TripletMarginLossWithSampler(
margin=0.5, sampler_inbatch=sampler_inbatch)
# 4. training with catalyst Runner
callbacks = [
dl.ControlFlowCallback(dl.CriterionCallback(), loaders="train"),
dl.ControlFlowCallback(dl.CMCScoreCallback(topk_args=[1]),
loaders="valid"),
dl.PeriodicLoaderCallback(valid=100),
]
runner = dl.SupervisedRunner(device=utils.get_device())
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
callbacks=callbacks,
loaders={
"train": train_loader,
"valid": val_loader
},
minimize_metric=False,
verbose=True,
valid_loader="valid",
num_epochs=100,
main_metric="cmc01",
)
return runner.best_valid_metrics["cmc01"]
def train_experiment(device, engine=None):
with TemporaryDirectory() as logdir:
# 1. train and valid loaders
transforms = Compose([ToTensor(), Normalize((0.1307, ), (0.3081, ))])
train_dataset = datasets.MnistMLDataset(root=os.getcwd(),
download=True,
transform=transforms)
sampler = data.BatchBalanceClassSampler(
labels=train_dataset.get_labels(),
num_classes=5,
num_samples=10,
num_batches=10)
train_loader = DataLoader(dataset=train_dataset, batch_sampler=sampler)
valid_dataset = datasets.MnistQGDataset(root=os.getcwd(),
transform=transforms,
gallery_fraq=0.2)
valid_loader = DataLoader(dataset=valid_dataset, batch_size=1024)
# 2. model and optimizer
model = models.MnistSimpleNet(out_features=16)
optimizer = Adam(model.parameters(), lr=0.001)
# 3. criterion with triplets sampling
sampler_inbatch = data.HardTripletsSampler(norm_required=False)
criterion = nn.TripletMarginLossWithSampler(
margin=0.5, sampler_inbatch=sampler_inbatch)
# 4. training with catalyst Runner
callbacks = [
dl.ControlFlowCallback(
dl.CriterionCallback(input_key="embeddings",
target_key="targets",
metric_key="loss"),
loaders="train",
),
dl.ControlFlowCallback(
dl.CMCScoreCallback(
embeddings_key="embeddings",
labels_key="targets",
is_query_key="is_query",
topk_args=[1],
),
loaders="valid",
),
dl.PeriodicLoaderCallback(valid_loader_key="valid",
valid_metric_key="cmc01",
minimize=False,
valid=2),
]
runner = CustomRunner(input_key="features", output_key="embeddings")
runner.train(
engine=engine or dl.DeviceEngine(device),
model=model,
criterion=criterion,
optimizer=optimizer,
callbacks=callbacks,
loaders={
"train": train_loader,
"valid": valid_loader
},
verbose=False,
logdir=logdir,
valid_loader="valid",
valid_metric="cmc01",
minimize_valid_metric=False,
num_epochs=2,
)