def __init__(self,
coefficient: float = None,
name: str = "Mixup",
options: "MixupTask.Options" = None):
super().__init__(coefficient=coefficient, name=name, options=options)
self.options: MixupTask.Options
logger = get_logger(__file__)
# Exponential moving average versions of the encoder and output head.
self.mean_encoder: nn.Module = deepcopy(AuxiliaryTask.encoder)
self.mean_classifier: nn.Module = deepcopy(AuxiliaryTask.encoder)
self.previous_task: Optional[Task] = None
self.epoch_in_task: Optional[int] = 0
self.epoch_length: Optional[int] = 0
self.update_number: Optional[int] = 0
self.consistency_criterion = softmax_mse_loss
from typing import Dict, List, Union, ClassVar
from pathlib import Path
import wandb
import matplotlib.pyplot as plt
from simple_parsing import field, list_field, mutable_field
from sequoia.common.loss import Loss
from sequoia.common.metrics import ClassificationMetrics, Metrics, RegressionMetrics
from sequoia.utils.logging_utils import get_logger
from sequoia.utils.plotting import PlotSectionLabel, autolabel
from sequoia.utils.utils import mean
from sequoia.settings.assumptions.incremental import IncrementalSetting
from .. import Results
logger = get_logger(__file__)
class ClassIncrementalResults(IncrementalSetting.Results):
"""Results for a ClassIncrementalSetting.
The main objective in this setting is the average test accuracy over all
tasks.
The plots to generate are:
- Accuracy per task
- Average Test Accuray over the course of testing
- Confusion matrix at the end of testing
All of these will be created from the list of test metrics (Classification
metrics for now).
