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| from typing import List | |
| import torch | |
| from torch import Tensor | |
| from torchmetrics import Metric | |
| from torchmetrics.functional import pairwise_euclidean_distance | |
| from .utils import * | |
| import os | |
| from mGPT.config import instantiate_from_config | |
| class MMMetrics(Metric): | |
| full_state_update = True | |
| def __init__(self, cfg, dataname='humanml3d', mm_num_times=10, dist_sync_on_step=True, **kwargs): | |
| super().__init__(dist_sync_on_step=dist_sync_on_step) | |
| self.name = "MultiModality scores" | |
| self.cfg = cfg | |
| self.dataname = dataname | |
| 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") | |
| # chached batches | |
| self.add_state("mm_motion_embeddings", default=[], dist_reduce_fx=None) | |
| # T2M Evaluator | |
| self._get_t2m_evaluator(cfg) | |
| def _get_t2m_evaluator(self, cfg): | |
| """ | |
| load T2M text encoder and motion encoder for evaluating | |
| """ | |
| # init module | |
| self.t2m_textencoder = instantiate_from_config(cfg.METRIC.TM2T.t2m_textencoder) | |
| self.t2m_moveencoder = instantiate_from_config(cfg.METRIC.TM2T.t2m_moveencoder) | |
| self.t2m_motionencoder = instantiate_from_config(cfg.METRIC.TM2T.t2m_motionencoder) | |
| # load pretrianed | |
| if self.dataname == "kit": | |
| dataname = "kit" | |
| else: | |
| dataname = "t2m" | |
| t2m_checkpoint = torch.load(os.path.join( | |
| cfg.METRIC.TM2T.t2m_path, dataname, | |
| "text_mot_match/model/finest.tar"), | |
| map_location="cpu") | |
| self.t2m_textencoder.load_state_dict(t2m_checkpoint["text_encoder"]) | |
| self.t2m_moveencoder.load_state_dict( | |
| t2m_checkpoint["movement_encoder"]) | |
| self.t2m_motionencoder.load_state_dict( | |
| t2m_checkpoint["motion_encoder"]) | |
| # freeze params | |
| self.t2m_textencoder.eval() | |
| self.t2m_moveencoder.eval() | |
| self.t2m_motionencoder.eval() | |
| for p in self.t2m_textencoder.parameters(): | |
| p.requires_grad = False | |
| for p in self.t2m_moveencoder.parameters(): | |
| p.requires_grad = False | |
| for p in self.t2m_motionencoder.parameters(): | |
| p.requires_grad = False | |
| def compute(self, sanity_flag): | |
| count = self.count.item() | |
| count_seq = self.count_seq.item() | |
| # init metrics | |
| metrics = {metric: getattr(self, metric) for metric in self.metrics} | |
| # if in sanity check stage then jump | |
| if sanity_flag: | |
| return metrics | |
| # cat all embeddings | |
| all_mm_motions = torch.cat(self.mm_motion_embeddings, | |
| axis=0).cpu().numpy() | |
| metrics['MultiModality'] = calculate_multimodality_np( | |
| all_mm_motions, self.mm_num_times) | |
| # Reset | |
| self.reset() | |
| return {**metrics} | |
| def update( | |
| self, | |
| feats_rst: Tensor, | |
| lengths_rst: List[int], | |
| ): | |
| self.count += sum(lengths_rst) | |
| self.count_seq += len(lengths_rst) | |
| align_idx = np.argsort(lengths_rst)[::-1].copy() | |
| feats_rst = feats_rst[align_idx] | |
| lengths_rst = np.array(lengths_rst)[align_idx] | |
| recmotion_embeddings = self.get_motion_embeddings( | |
| feats_rst, lengths_rst) | |
| cache = [0] * len(lengths_rst) | |
| for i in range(len(lengths_rst)): | |
| cache[align_idx[i]] = recmotion_embeddings[i:i + 1] | |
| mm_motion_embeddings = torch.cat(cache, axis=0).unsqueeze(0) | |
| # self.mm_motion_embeddings.extend(cache) | |
| # print(mm_motion_embeddings.shape) | |
| # # store all mm motion embeddings | |
| self.mm_motion_embeddings.append(mm_motion_embeddings) | |
| def get_motion_embeddings(self, feats: Tensor, lengths: List[int]): | |
| m_lens = torch.tensor(lengths) | |
| m_lens = torch.div(m_lens, | |
| self.cfg.DATASET.HUMANML3D.UNIT_LEN, | |
| rounding_mode="floor") | |
| mov = self.t2m_moveencoder(feats[..., :-4]).detach() | |
| emb = self.t2m_motionencoder(mov, m_lens) | |
| # [bs, nlatent*ndim] <= [bs, nlatent, ndim] | |
| return torch.flatten(emb, start_dim=1).detach() | |