def test_meter():
"""
Tests:
* .reset()
* .add()
* .value()
"""
meter = meters.PrecisionRecallF1ScoreMeter()
# tests the .reset() method, which happens to be called in initialization
for key in ["tp", "fp", "fn"]:
assert meter.tp_fp_fn_counts[key] == 0, \
"Counts should be initialized to 0."
# testing .add() and .value() with tensors w/no batch size dim
binary_y, binary_pred = create_dummy_tensors_single()
meter.add(binary_pred, binary_y)
runs_tests_on_meter_counts_and_value(meter, num_tp_check=1)
# testing .add() and .value() with tensors w/the batch size dim
meter.reset()
batch_size = 16
binary_y, binary_pred = create_dummy_tensors_batched(batch_size)
meter.add(binary_pred, binary_y)
runs_tests_on_meter_counts_and_value(meter, num_tp_check=batch_size)
# testing with seg; shape (batch_size, n_channels, h, w)
meter.reset()
batch_size = 16
binary_y, binary_pred = create_dummy_tensors_seg(batch_size)
meter.add(binary_pred, binary_y)
runs_tests_on_meter_counts_and_value(meter,
num_tp_check=batch_size * 15 * 15)
def __init__(
self,
input_key: str = "targets",
output_key: str = "logits",
class_names: List[str] = None,
num_classes: int = 2,
threshold: float = 0.5,
activation: str = "Sigmoid",
):
"""
Args:
input_key (str): input key to use for metric calculation
specifies our ``y_true``.
output_key (str): output key to use for metric calculation;
specifies our ``y_pred``
class_names (List[str]): class names to display in the logs.
If None, defaults to indices for each class, starting from 0.
num_classes (int): Number of classes; must be > 1
threshold (float): threshold for outputs binarization
activation (str): An torch.nn activation applied to the outputs.
Must be one of ['none', 'Sigmoid', 'Softmax2d']
"""
# adjusting num_classes automatically if class_names is not None
num_classes = num_classes if class_names is None else len(class_names)
meter_list = [
meters.PrecisionRecallF1ScoreMeter(threshold)
for _ in range(num_classes)
]
super().__init__(
metric_names=["ppv", "tpr", "f1"],
meter_list=meter_list,
input_key=input_key,
output_key=output_key,
class_names=class_names,
num_classes=num_classes,
activation=activation,
)