File size: 7,378 Bytes
823807d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
import os
import torch
import numpy as np

from torch.utils.data import DataLoader
from os.path import join as pjoin

from models.mask_transformer.transformer import ResidualTransformer
from models.mask_transformer.transformer_trainer import ResidualTransformerTrainer
from models.vq.model import RVQVAE

from options.train_option import TrainT2MOptions

from utils.plot_script import plot_3d_motion
from utils.motion_process import recover_from_ric
from utils.get_opt import get_opt
from utils.fixseed import fixseed
from utils.paramUtil import t2m_kinematic_chain, kit_kinematic_chain

from data.t2m_dataset import Text2MotionDataset
from motion_loaders.dataset_motion_loader import get_dataset_motion_loader
from models.t2m_eval_wrapper import EvaluatorModelWrapper


def plot_t2m(data, save_dir, captions, m_lengths):
    data = train_dataset.inv_transform(data)

    # print(ep_curves.shape)
    for i, (caption, joint_data) in enumerate(zip(captions, data)):
        joint_data = joint_data[:m_lengths[i]]
        joint = recover_from_ric(torch.from_numpy(joint_data).float(), opt.joints_num).numpy()
        save_path = pjoin(save_dir, '%02d.mp4'%i)
        # print(joint.shape)
        plot_3d_motion(save_path, kinematic_chain, joint, title=caption, fps=20)

def load_vq_model():
    opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'opt.txt')
    vq_opt = get_opt(opt_path, opt.device)
    vq_model = RVQVAE(vq_opt,
                dim_pose,
                vq_opt.nb_code,
                vq_opt.code_dim,
                vq_opt.output_emb_width,
                vq_opt.down_t,
                vq_opt.stride_t,
                vq_opt.width,
                vq_opt.depth,
                vq_opt.dilation_growth_rate,
                vq_opt.vq_act,
                vq_opt.vq_norm)
    ckpt = torch.load(pjoin(vq_opt.checkpoints_dir, vq_opt.dataset_name, vq_opt.name, 'model', 'net_best_fid.tar'),
                            map_location=opt.device)
    model_key = 'vq_model' if 'vq_model' in ckpt else 'net'
    vq_model.load_state_dict(ckpt[model_key])
    print(f'Loading VQ Model {opt.vq_name}')
    vq_model.to(opt.device)
    return vq_model, vq_opt

if __name__ == '__main__':
    parser = TrainT2MOptions()
    opt = parser.parse()
    fixseed(opt.seed)

    opt.device = torch.device("cpu" if opt.gpu_id == -1 else "cuda:" + str(opt.gpu_id))
    torch.autograd.set_detect_anomaly(True)

    opt.save_root = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name)
    opt.model_dir = pjoin(opt.save_root, 'model')
    # opt.meta_dir = pjoin(opt.save_root, 'meta')
    opt.eval_dir = pjoin(opt.save_root, 'animation')
    opt.log_dir = pjoin('./log/res/', opt.dataset_name, opt.name)

    os.makedirs(opt.model_dir, exist_ok=True)
    # os.makedirs(opt.meta_dir, exist_ok=True)
    os.makedirs(opt.eval_dir, exist_ok=True)
    os.makedirs(opt.log_dir, exist_ok=True)

    if opt.dataset_name == 't2m':
        opt.data_root = './dataset/HumanML3D'
        opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
        opt.joints_num = 22
        opt.max_motion_len = 55
        dim_pose = 263
        radius = 4
        fps = 20
        kinematic_chain = t2m_kinematic_chain
        dataset_opt_path = './checkpoints/t2m/Comp_v6_KLD005/opt.txt'

    elif opt.dataset_name == 'kit': #TODO
        opt.data_root = './dataset/KIT-ML'
        opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
        opt.joints_num = 21
        radius = 240 * 8
        fps = 12.5
        dim_pose = 251
        opt.max_motion_len = 55
        kinematic_chain = kit_kinematic_chain
        dataset_opt_path = './checkpoints/kit/Comp_v6_KLD005/opt.txt'

    else:
        raise KeyError('Dataset Does Not Exist')

    opt.text_dir = pjoin(opt.data_root, 'texts')

    vq_model, vq_opt = load_vq_model()

    clip_version = 'ViT-B/32'

    opt.num_tokens = vq_opt.nb_code
    opt.num_quantizers = vq_opt.num_quantizers

    # if opt.is_v2:
    res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,
                                          cond_mode='text',
                                          latent_dim=opt.latent_dim,
                                          ff_size=opt.ff_size,
                                          num_layers=opt.n_layers,
                                          num_heads=opt.n_heads,
                                          dropout=opt.dropout,
                                          clip_dim=512,
                                          shared_codebook=vq_opt.shared_codebook,
                                          cond_drop_prob=opt.cond_drop_prob,
                                          # codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,
                                            share_weight=opt.share_weight,
                                          clip_version=clip_version,
                                          opt=opt)
    # else:
    #     res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,
    #                                           cond_mode='text',
    #                                           latent_dim=opt.latent_dim,
    #                                           ff_size=opt.ff_size,
    #                                           num_layers=opt.n_layers,
    #                                           num_heads=opt.n_heads,
    #                                           dropout=opt.dropout,
    #                                           clip_dim=512,
    #                                           shared_codebook=vq_opt.shared_codebook,
    #                                           cond_drop_prob=opt.cond_drop_prob,
    #                                           # codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,
    #                                           clip_version=clip_version,
    #                                           opt=opt)


    all_params = 0
    pc_transformer = sum(param.numel() for param in res_transformer.parameters_wo_clip())

    print(res_transformer)
    # print("Total parameters of t2m_transformer net: {:.2f}M".format(pc_transformer / 1000_000))
    all_params += pc_transformer

    print('Total parameters of all models: {:.2f}M'.format(all_params / 1000_000))

    mean = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'meta', 'mean.npy'))
    std = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'meta', 'std.npy'))

    train_split_file = pjoin(opt.data_root, 'train.txt')
    val_split_file = pjoin(opt.data_root, 'val.txt')

    train_dataset = Text2MotionDataset(opt, mean, std, train_split_file)
    val_dataset = Text2MotionDataset(opt, mean, std, val_split_file)

    train_loader = DataLoader(train_dataset, batch_size=opt.batch_size, num_workers=4, shuffle=True, drop_last=True)
    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size, num_workers=4, shuffle=True, drop_last=True)

    eval_val_loader, _ = get_dataset_motion_loader(dataset_opt_path, 32, 'val', device=opt.device)

    wrapper_opt = get_opt(dataset_opt_path, torch.device('cuda'))
    eval_wrapper = EvaluatorModelWrapper(wrapper_opt)

    trainer = ResidualTransformerTrainer(opt, res_transformer, vq_model)

    trainer.train(train_loader, val_loader, eval_val_loader, eval_wrapper=eval_wrapper, plot_eval=plot_t2m)