|
|
| 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 |
|
|
