def __init__(self,
path_key: str = "filepath",
probs_key: str = "logits",
activation: str = "Softmax",
out_file: str = 'infer_pred.txt'):
"""
Args:
metric_names (List[str]): of metrics to print
Make sure that they are in the same order that metrics
are outputted by the meters in `meter_list`
meter_list (list-like): List of meters.meter.Meter instances
len(meter_list) == num_classes
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
activation (str): An torch.nn activation applied to the logits.
Must be one of ['none', 'Sigmoid', 'Softmax2d']
"""
super().__init__(CallbackOrder.Logging)
self.input_key = path_key
self.output_key = probs_key
self.activation = activation
self.activation_fn = get_activation_fn(self.activation)
self.preds = []
self.out_file = out_file
def __init__(
self,
metric_names: List[str],
meter_list: List,
input_key: str = "targets",
output_key: str = "logits",
class_names: List[str] = None,
num_classes: int = 2,
activation: str = "Sigmoid",
):
"""
Args:
metric_names: of metrics to print
Make sure that they are in the same order that metrics
are outputted by the meters in `meter_list`
meter_list: List of meters.meter.Meter instances
len(meter_list) == num_classes
input_key: input key to use for metric calculation
specifies our ``y_true``.
output_key: output key to use for metric calculation;
specifies our ``y_pred``
class_names: class names to display in the logs.
If None, defaults to indices for each class, starting from 0.
num_classes: Number of classes; must be > 1
activation: An torch.nn activation applied to the logits.
Must be one of ['none', 'Sigmoid', 'Softmax2d']
"""
super().__init__(CallbackOrder.metric)
self.metric_names = metric_names
self.meters = meter_list
self.input_key = input_key
self.output_key = output_key
self.class_names = class_names
self.num_classes = num_classes
self.activation = activation
self.activation_fn = get_activation_fn(self.activation)