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import torch
from torchmetrics import Metric
from torchmetrics.utilities import dim_zero_cat
from .utils import calculate_multimodality_np
class MMMetrics(Metric):
def __init__(self, mm_num_times: int = 10, dist_sync_on_step: bool = True) -> None:
super().__init__(dist_sync_on_step=dist_sync_on_step)
self.name = "MultiModality scores"
self.mm_num_times = mm_num_times
self.add_state("count", default=torch.tensor(0), dist_reduce_fx="sum")
self.add_state("count_seq", default=torch.tensor(0), dist_reduce_fx="sum")
self.metrics = ["MultiModality"]
self.add_state("MultiModality", default=torch.tensor(0.), dist_reduce_fx="sum")
# cached batches
self.add_state("mm_motion_embeddings", default=[], dist_reduce_fx='cat')
def compute(self) -> dict:
# init metrics
metrics = {metric: getattr(self, metric) for metric in self.metrics}
# cat all embeddings
all_mm_motions = dim_zero_cat(self.mm_motion_embeddings).cpu().numpy()
metrics['MultiModality'] = calculate_multimodality_np(all_mm_motions, self.mm_num_times)
return metrics
def update(self, mm_motion_embeddings: torch.Tensor, lengths: list[int]) -> None:
self.count += sum(lengths)
self.count_seq += len(lengths)
# store all mm motion embeddings
self.mm_motion_embeddings.append(mm_motion_embeddings)
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