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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()
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