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train_res_transformer.py
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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 MaskTransformer, 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('./log/vq', 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
def load_trans_model(model_opt, which_model):
t2m_transformer = MaskTransformer(code_dim=model_opt.code_dim,
cond_mode='text',
latent_dim=model_opt.latent_dim,
ff_size=model_opt.ff_size,
num_layers=model_opt.n_layers,
num_heads=model_opt.n_heads,
dropout=model_opt.dropout,
clip_dim=512,
cond_drop_prob=model_opt.cond_drop_prob,
clip_version=clip_version,
opt=model_opt)
ckpt = torch.load(pjoin(model_opt.checkpoints_dir, model_opt.dataset_name, model_opt.name, 'model', which_model),
map_location=opt.device)
model_key = 't2m_transformer' if 't2m_transformer' in ckpt else 'trans'
# print(ckpt.keys())
missing_keys, unexpected_keys = t2m_transformer.load_state_dict(ckpt[model_key], strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
print(f'Loading Mask Transformer {opt.name} from epoch {ckpt["ep"]}!')
return t2m_transformer
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)
from exit.utils import init_save_folder
init_save_folder(opt.save_root)
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('./log/vq', opt.dataset_name, opt.vq_name, 'meta', 'mean.npy'))
std = np.load(pjoin('./log/vq', 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)
### Add t2m_transformer ###
root_dir = pjoin('./log/t2m', opt.dataset_name, opt.trans)
model_opt_path = pjoin(root_dir, 'opt.txt')
model_opt = get_opt(model_opt_path, device=opt.device)
model_opt.num_tokens = vq_opt.nb_code
model_opt.num_quantizers = vq_opt.num_quantizers
model_opt.code_dim = vq_opt.code_dim
t2m_transformer = load_trans_model(model_opt, 'net_best_fid.tar')
t2m_transformer.eval()
t2m_transformer.to(opt.device)
###############################
trainer.train(train_loader, val_loader, eval_val_loader, eval_wrapper=eval_wrapper, plot_eval=plot_t2m, t2m_transformer=t2m_transformer)