| |
|
| | import torch |
| | from os.path import join as pjoin |
| | import numpy as np |
| | from models.modules import MovementConvEncoder, TextEncoderBiGRUCo, MotionEncoderBiGRUCo |
| | from utils.word_vectorizer import POS_enumerator |
| |
|
| | def build_models(opt): |
| | movement_enc = MovementConvEncoder(opt.dim_pose-4, opt.dim_movement_enc_hidden, opt.dim_movement_latent) |
| | text_enc = TextEncoderBiGRUCo(word_size=opt.dim_word, |
| | pos_size=opt.dim_pos_ohot, |
| | hidden_size=opt.dim_text_hidden, |
| | output_size=opt.dim_coemb_hidden, |
| | device=opt.device) |
| |
|
| | motion_enc = MotionEncoderBiGRUCo(input_size=opt.dim_movement_latent, |
| | hidden_size=opt.dim_motion_hidden, |
| | output_size=opt.dim_coemb_hidden, |
| | device=opt.device) |
| |
|
| | checkpoint = torch.load(pjoin(opt.checkpoints_dir, opt.dataset_name, 'text_mot_match', 'model', 'finest.tar'), |
| | map_location=opt.device) |
| | movement_enc.load_state_dict(checkpoint['movement_encoder']) |
| | text_enc.load_state_dict(checkpoint['text_encoder']) |
| | motion_enc.load_state_dict(checkpoint['motion_encoder']) |
| | print('Loading Evaluation Model Wrapper (Epoch %d) Completed!!' % (checkpoint['epoch'])) |
| | return text_enc, motion_enc, movement_enc |
| |
|
| |
|
| | class EvaluatorModelWrapper(object): |
| |
|
| | def __init__(self, opt): |
| |
|
| | if opt.dataset_name == 't2m': |
| | opt.dim_pose = 263 |
| | elif opt.dataset_name == 'kit': |
| | opt.dim_pose = 251 |
| | else: |
| | raise KeyError('Dataset not Recognized!!!') |
| |
|
| | opt.dim_word = 300 |
| | opt.max_motion_length = 196 |
| | opt.dim_pos_ohot = len(POS_enumerator) |
| | opt.dim_motion_hidden = 1024 |
| | opt.max_text_len = 20 |
| | opt.dim_text_hidden = 512 |
| | opt.dim_coemb_hidden = 512 |
| |
|
| | |
| |
|
| | self.text_encoder, self.motion_encoder, self.movement_encoder = build_models(opt) |
| | self.opt = opt |
| | self.device = opt.device |
| |
|
| | self.text_encoder.to(opt.device) |
| | self.motion_encoder.to(opt.device) |
| | self.movement_encoder.to(opt.device) |
| |
|
| | self.text_encoder.eval() |
| | self.motion_encoder.eval() |
| | self.movement_encoder.eval() |
| |
|
| | |
| | def get_co_embeddings(self, word_embs, pos_ohot, cap_lens, motions, m_lens): |
| | with torch.no_grad(): |
| | word_embs = word_embs.detach().to(self.device).float() |
| | pos_ohot = pos_ohot.detach().to(self.device).float() |
| | motions = motions.detach().to(self.device).float() |
| |
|
| | '''Movement Encoding''' |
| | movements = self.movement_encoder(motions[..., :-4]).detach() |
| | m_lens = m_lens // self.opt.unit_length |
| | motion_embedding = self.motion_encoder(movements, m_lens) |
| |
|
| | '''Text Encoding''' |
| | text_embedding = self.text_encoder(word_embs, pos_ohot, cap_lens) |
| | return text_embedding, motion_embedding |
| |
|
| | |
| | def get_motion_embeddings(self, motions, m_lens): |
| | with torch.no_grad(): |
| | motions = motions.detach().to(self.device).float() |
| |
|
| | align_idx = np.argsort(m_lens.data.tolist())[::-1].copy() |
| | motions = motions[align_idx] |
| | m_lens = m_lens[align_idx] |
| |
|
| | '''Movement Encoding''' |
| | movements = self.movement_encoder(motions[..., :-4]).detach() |
| | m_lens = m_lens // self.opt.unit_length |
| | motion_embedding = self.motion_encoder(movements, m_lens) |
| | return motion_embedding |
| |
|
