from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from catalyst.metrics._additive import AdditiveValueMetric
from catalyst.metrics._metric import ICallbackBatchMetric
from catalyst.metrics.functional._accuracy import accuracy, multilabel_accuracy
from catalyst.metrics.functional._misc import get_default_topk_args
[docs]class AccuracyMetric(ICallbackBatchMetric):
"""
This metric computes accuracy for multiclass classification case.
It computes mean value of accuracy and it's approximate std value
(note that it's not a real accuracy std but std of accuracy over batch mean values).
Args:
topk_args: list of `topk` for accuracy@topk computing
num_classes: number of classes
compute_on_call: if True, computes and returns metric value during metric call
prefix: metric prefix
suffix: metric suffix
Examples:
.. code-block:: python
import torch
from catalyst import metrics
outputs = torch.tensor([
[0.2, 0.5, 0.0, 0.3],
[0.9, 0.1, 0.0, 0.0],
[0.0, 0.1, 0.6, 0.3],
[0.0, 0.8, 0.2, 0.0],
])
targets = torch.tensor([3, 0, 2, 2])
metric = metrics.AccuracyMetric(topk_args=(1, 3))
metric.reset()
metric.update(outputs, targets)
metric.compute()
# (
# (0.5, 1.0), # top1, top3 mean
# (0.0, 0.0), # top1, top3 std
# )
metric.compute_key_value()
# {
# 'accuracy': 0.5,
# 'accuracy/std': 0.0,
# 'accuracy01': 0.5,
# 'accuracy01/std': 0.0,
# 'accuracy03': 1.0,
# 'accuracy03/std': 0.0,
# }
metric.reset()
metric(outputs, targets)
# (
# (0.5, 1.0), # top1, top3 mean
# (0.0, 0.0), # top1, top3 std
# )
.. code-block:: python
import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl
# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples,) * num_classes).to(torch.int64)
# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}
# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])
# model training
runner = dl.SupervisedRunner(
input_key="features", output_key="logits", target_key="targets", loss_key="loss"
)
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
logdir="./logdir",
num_epochs=3,
valid_loader="valid",
valid_metric="accuracy03",
minimize_valid_metric=False,
verbose=True,
callbacks=[
dl.AccuracyCallback(
input_key="logits", target_key="targets", num_classes=num_classes
),
dl.PrecisionRecallF1SupportCallback(
input_key="logits", target_key="targets", num_classes=num_classes
),
dl.AUCCallback(input_key="logits", target_key="targets"),
],
)
.. note::
Please follow the `minimal examples`_ sections for more use cases.
.. _`minimal examples`: https://github.com/catalyst-team/catalyst#minimal-examples
"""
def __init__(
self,
topk_args: List[int] = None,
num_classes: int = None,
compute_on_call: bool = True,
prefix: str = None,
suffix: str = None,
):
"""Init AccuracyMetric"""
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix)
self.metric_name_mean = f"{self.prefix}accuracy{self.suffix}"
self.metric_name_std = f"{self.prefix}accuracy{self.suffix}/std"
self.topk_args: List[int] = topk_args or get_default_topk_args(num_classes)
self.additive_metrics: List[AdditiveValueMetric] = [
AdditiveValueMetric() for _ in range(len(self.topk_args))
]
def reset(self) -> None:
"""Reset all fields"""
for metric in self.additive_metrics:
metric.reset()
def update(self, logits: torch.Tensor, targets: torch.Tensor) -> List[float]:
"""
Updates metric value with accuracy for new data and return intermediate metrics values.
Args:
logits: tensor of logits
targets: tensor of targets
Returns:
list of accuracy@k values
"""
values = accuracy(logits, targets, topk=self.topk_args)
values = [v.item() for v in values]
for value, metric in zip(values, self.additive_metrics):
metric.update(value, len(targets))
return values
def update_key_value(self, logits: torch.Tensor, targets: torch.Tensor) -> Dict[str, float]:
"""
Update metric value with accuracy for new data and return intermediate metrics
values in key-value format.
Args:
logits: tensor of logits
targets: tensor of targets
Returns:
dict of accuracy@k values
"""
values = self.update(logits=logits, targets=targets)
output = {
f"{self.prefix}accuracy{key:02d}{self.suffix}": value
for key, value in zip(self.topk_args, values)
}
output[self.metric_name_mean] = output[f"{self.prefix}accuracy01{self.suffix}"]
return output
def compute(self) -> Tuple[List[float], List[float]]:
"""
Compute accuracy for all data
Returns:
list of mean values, list of std values
"""
means, stds = zip(*(metric.compute() for metric in self.additive_metrics))
return means, stds
def compute_key_value(self) -> Dict[str, float]:
"""
Compute accuracy for all data and return results in key-value format
Returns:
dict of metrics
"""
means, stds = self.compute()
output_mean = {
f"{self.prefix}accuracy{key:02d}{self.suffix}": value
for key, value in zip(self.topk_args, means)
}
output_std = {
f"{self.prefix}accuracy{key:02d}{self.suffix}/std": value
for key, value in zip(self.topk_args, stds)
}
output_mean[self.metric_name_mean] = output_mean[f"{self.prefix}accuracy01{self.suffix}"]
output_std[self.metric_name_std] = output_std[f"{self.prefix}accuracy01{self.suffix}/std"]
return {**output_mean, **output_std}
[docs]class MultilabelAccuracyMetric(AdditiveValueMetric, ICallbackBatchMetric):
"""
This metric computes accuracy for multilabel classification case.
It computes mean value of accuracy and it's approximate std value
(note that it's not a real accuracy std but std of accuracy over batch mean values).
Args:
compute_on_call: if True, computes and returns metric value during metric call
prefix: metric prefix
suffix: metric suffix
threshold: thresholds for model scores
Examples:
.. code-block:: python
import torch
from catalyst import metrics
outputs = torch.tensor([
[0.1, 0.9, 0.0, 0.8],
[0.96, 0.01, 0.85, 0.2],
[0.98, 0.4, 0.2, 0.1],
[0.1, 0.89, 0.2, 0.0],
])
targets = torch.tensor([
[0, 1, 1, 0],
[1, 0, 1, 0],
[0, 1, 0, 0],
[0, 1, 0, 0],
])
metric = metrics.MultilabelAccuracyMetric(threshold=0.6)
metric.reset()
metric.update(outputs, targets)
metric.compute()
# (0.75, 0.0) # mean, std
metric.compute_key_value()
# {
# 'accuracy': 0.75,
# 'accuracy/std': 0.0,
# }
metric.reset()
metric(outputs, targets)
# (0.75, 0.0) # mean, std
.. code-block:: python
import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl
# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32)
# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}
# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])
# model training
runner = dl.SupervisedRunner(
input_key="features", output_key="logits", target_key="targets", loss_key="loss"
)
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
logdir="./logdir",
num_epochs=3,
valid_loader="valid",
valid_metric="accuracy",
minimize_valid_metric=False,
verbose=True,
callbacks=[
dl.AUCCallback(input_key="logits", target_key="targets"),
dl.MultilabelAccuracyCallback(
input_key="logits", target_key="targets", threshold=0.5
)
]
)
.. note::
Please follow the `minimal examples`_ sections for more use cases.
.. _`minimal examples`: https://github.com/catalyst-team/catalyst#minimal-examples
"""
def __init__(
self,
threshold: Union[float, torch.Tensor] = 0.5,
compute_on_call: bool = True,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
):
"""Init MultilabelAccuracyMetric"""
super().__init__(compute_on_call=compute_on_call)
self.prefix = prefix or ""
self.suffix = suffix or ""
self.metric_name_mean = f"{self.prefix}accuracy{self.suffix}"
self.metric_name_std = f"{self.prefix}accuracy{self.suffix}/std"
self.threshold = threshold
def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> float:
"""
Update metric value with accuracy for new data and return intermediate metric value.
Args:
outputs: tensor of outputs
targets: tensor of true answers
Returns:
accuracy metric for outputs and targets
"""
metric = multilabel_accuracy(
outputs=outputs, targets=targets, threshold=self.threshold
).item()
super().update(value=metric, num_samples=np.prod(targets.shape))
return metric
def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[str, float]:
"""
Update metric value with accuracy for new data and return intermediate metric
value in key-value format.
Args:
outputs: tensor of outputs
targets: tensor of true answers
Returns:
accuracy metric for outputs and targets
"""
metric = self.update(outputs=outputs, targets=targets)
return {self.metric_name_mean: metric}
def compute_key_value(self) -> Dict[str, float]:
"""
Compute accuracy for all data and return results in key-value format
Returns:
dict of metrics
"""
metric_mean, metric_std = self.compute()
return {
self.metric_name_mean: metric_mean,
self.metric_name_std: metric_std,
}
__all__ = ["AccuracyMetric", "MultilabelAccuracyMetric"]