import os
from os.path import join as pjoin
import torch
import torch.nn.functional as F
from models.mask_transformer.transformer import MaskTransformer, ResidualTransformer
from models.vq.model import RVQVAE, LengthEstimator
from options.eval_option import EvalT2MOptions
from utils.get_opt import get_opt
from utils.fixseed import fixseed
from visualization.joints2bvh import Joint2BVHConvertor
from torch.distributions.categorical import Categorical
from utils.motion_process import recover_from_ric
from utils.plot_script import plot_3d_motion
from utils.paramUtil import t2m_kinematic_chain
import numpy as np
clip_version = 'ViT-B/32'
def load_vq_model(vq_opt):
# opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'opt.txt')
vq_model = RVQVAE(vq_opt,
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='cpu')
model_key = 'vq_model' if 'vq_model' in ckpt else 'net'
vq_model.load_state_dict(ckpt[model_key])
print(f'Loading VQ Model {vq_opt.name} Completed!')
return vq_model, vq_opt
def load_trans_model(model_opt, 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='cpu')
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 Transformer {opt.name} from epoch {ckpt["ep"]}!')
return t2m_transformer
def load_res_model(res_opt, vq_opt, opt):
res_opt.num_quantizers = vq_opt.num_quantizers
res_opt.num_tokens = vq_opt.nb_code
res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,
cond_mode='text',
latent_dim=res_opt.latent_dim,
ff_size=res_opt.ff_size,
num_layers=res_opt.n_layers,
num_heads=res_opt.n_heads,
dropout=res_opt.dropout,
clip_dim=512,
shared_codebook=vq_opt.shared_codebook,
cond_drop_prob=res_opt.cond_drop_prob,
# codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,
share_weight=res_opt.share_weight,
clip_version=clip_version,
opt=res_opt)
ckpt = torch.load(pjoin(res_opt.checkpoints_dir, res_opt.dataset_name, res_opt.name, 'model', 'net_best_fid.tar'),
map_location=opt.device)
missing_keys, unexpected_keys = res_transformer.load_state_dict(ckpt['res_transformer'], strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
print(f'Loading Residual Transformer {res_opt.name} from epoch {ckpt["ep"]}!')
return res_transformer
def load_len_estimator(opt):
model = LengthEstimator(512, 50)
ckpt = torch.load(pjoin(opt.checkpoints_dir, opt.dataset_name, 'length_estimator', 'model', 'finest.tar'),
map_location=opt.device)
model.load_state_dict(ckpt['estimator'])
print(f'Loading Length Estimator from epoch {ckpt["epoch"]}!')
return model
if __name__ == '__main__':
parser = EvalT2MOptions()
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)
dim_pose = 251 if opt.dataset_name == 'kit' else 263
# out_dir = pjoin(opt.check)
root_dir = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name)
model_dir = pjoin(root_dir, 'model')
result_dir = pjoin('./generation', opt.ext)
joints_dir = pjoin(result_dir, 'joints')
animation_dir = pjoin(result_dir, 'animations')
os.makedirs(joints_dir, exist_ok=True)
os.makedirs(animation_dir,exist_ok=True)
model_opt_path = pjoin(root_dir, 'opt.txt')
model_opt = get_opt(model_opt_path, device=opt.device)
#######################
######Loading RVQ######
#######################
vq_opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, model_opt.vq_name, 'opt.txt')
vq_opt = get_opt(vq_opt_path, device=opt.device)
vq_opt.dim_pose = dim_pose
vq_model, vq_opt = load_vq_model(vq_opt)
model_opt.num_tokens = vq_opt.nb_code
model_opt.num_quantizers = vq_opt.num_quantizers
model_opt.code_dim = vq_opt.code_dim
#################################
######Loading R-Transformer######
#################################
res_opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.res_name, 'opt.txt')
res_opt = get_opt(res_opt_path, device=opt.device)
res_model = load_res_model(res_opt, vq_opt, opt)
assert res_opt.vq_name == model_opt.vq_name
#################################
######Loading M-Transformer######
#################################
t2m_transformer = load_trans_model(model_opt, opt, 'latest.tar')
##################################
#####Loading Length Predictor#####
##################################
length_estimator = load_len_estimator(model_opt)
t2m_transformer.eval()
vq_model.eval()
res_model.eval()
length_estimator.eval()
res_model.to(opt.device)
t2m_transformer.to(opt.device)
vq_model.to(opt.device)
length_estimator.to(opt.device)
##### ---- Dataloader ---- #####
opt.nb_joints = 21 if opt.dataset_name == 'kit' else 22
mean = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, model_opt.vq_name, 'meta', 'mean.npy'))
std = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, model_opt.vq_name, 'meta', 'std.npy'))
def inv_transform(data):
return data * std + mean
prompt_list = []
length_list = []
est_length = False
if opt.text_prompt != "":
prompt_list.append(opt.text_prompt)
if opt.motion_length == 0:
est_length = True
else:
length_list.append(opt.motion_length)
elif opt.text_path != "":
with open(opt.text_path, 'r') as f:
lines = f.readlines()
for line in lines:
infos = line.split('#')
prompt_list.append(infos[0])
if len(infos) == 1 or (not infos[1].isdigit()):
est_length = True
length_list = []
else:
length_list.append(int(infos[-1]))
else:
raise "A text prompt, or a file a text prompts are required!!!"
# print('loading checkpoint {}'.format(file))
if est_length:
print("Since no motion length are specified, we will use estimated motion lengthes!!")
text_embedding = t2m_transformer.encode_text(prompt_list)
pred_dis = length_estimator(text_embedding)
probs = F.softmax(pred_dis, dim=-1) # (b, ntoken)
token_lens = Categorical(probs).sample() # (b, seqlen)
# lengths = torch.multinomial()
else:
token_lens = torch.LongTensor(length_list) // 4
token_lens = token_lens.to(opt.device).long()
m_length = token_lens * 4
captions = prompt_list
sample = 0
kinematic_chain = t2m_kinematic_chain
converter = Joint2BVHConvertor()
for r in range(opt.repeat_times):
print("-->Repeat %d"%r)
with torch.no_grad():
mids = t2m_transformer.generate(captions, token_lens,
timesteps=opt.time_steps,
cond_scale=opt.cond_scale,
temperature=opt.temperature,
topk_filter_thres=opt.topkr,
gsample=opt.gumbel_sample)
# print(mids)
# print(mids.shape)
mids = res_model.generate(mids, captions, token_lens, temperature=1, cond_scale=5)
pred_motions = vq_model.forward_decoder(mids)
pred_motions = pred_motions.detach().cpu().numpy()
data = inv_transform(pred_motions)
for k, (caption, joint_data) in enumerate(zip(captions, data)):
print("---->Sample %d: %s %d"%(k, caption, m_length[k]))
animation_path = pjoin(animation_dir, str(k))
joint_path = pjoin(joints_dir, str(k))
os.makedirs(animation_path, exist_ok=True)
os.makedirs(joint_path, exist_ok=True)
joint_data = joint_data[:m_length[k]]
joint = recover_from_ric(torch.from_numpy(joint_data).float(), 22).numpy()
bvh_path = pjoin(animation_path, "sample%d_repeat%d_len%d_ik.bvh"%(k, r, m_length[k]))
_, ik_joint = converter.convert(joint, filename=bvh_path, iterations=100)
bvh_path = pjoin(animation_path, "sample%d_repeat%d_len%d.bvh" % (k, r, m_length[k]))
_, joint = converter.convert(joint, filename=bvh_path, iterations=100, foot_ik=False)
save_path = pjoin(animation_path, "sample%d_repeat%d_len%d.mp4"%(k, r, m_length[k]))
ik_save_path = pjoin(animation_path, "sample%d_repeat%d_len%d_ik.mp4"%(k, r, m_length[k]))
plot_3d_motion(ik_save_path, kinematic_chain, ik_joint, title=caption, fps=20)
plot_3d_motion(save_path, kinematic_chain, joint, title=caption, fps=20)
np.save(pjoin(joint_path, "sample%d_repeat%d_len%d.npy"%(k, r, m_length[k])), joint)
np.save(pjoin(joint_path, "sample%d_repeat%d_len%d_ik.npy"%(k, r, m_length[k])), ik_joint)