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