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import os
from os.path import join as pjoin
import torch
from models.mask_transformer.transformer import MaskTransformer, ResidualTransformer
from models.vq.model import RVQVAE
from options.eval_option import EvalT2MOptions
from utils.get_opt import get_opt
from motion_loaders.dataset_motion_loader import get_dataset_motion_loader
from models.t2m_eval_wrapper import EvaluatorModelWrapper
import utils.eval_t2m as eval_t2m
from utils.fixseed import fixseed
import numpy as np
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,
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 {vq_opt.name} Completed!')
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
def load_res_model(res_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
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')
out_dir = pjoin(root_dir, 'eval')
os.makedirs(out_dir, exist_ok=True)
out_path = pjoin(out_dir, "%s.log"%opt.ext)
f = open(pjoin(out_path), 'w')
model_opt_path = pjoin(root_dir, 'opt.txt')
model_opt = get_opt(model_opt_path, device=opt.device)
clip_version = 'ViT-B/32'
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_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
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)
assert res_opt.vq_name == model_opt.vq_name
dataset_opt_path = 'checkpoints/kit/Comp_v6_KLD005/opt.txt' if opt.dataset_name == 'kit' \
else 'checkpoints/t2m/Comp_v6_KLD005/opt.txt'
wrapper_opt = get_opt(dataset_opt_path, torch.device('cuda'))
eval_wrapper = EvaluatorModelWrapper(wrapper_opt)
##### ---- Dataloader ---- #####
opt.nb_joints = 21 if opt.dataset_name == 'kit' else 22
eval_val_loader, _ = get_dataset_motion_loader(dataset_opt_path, 32, 'test', device=opt.device)
# model_dir = pjoin(opt.)
for file in os.listdir(model_dir):
if opt.which_epoch != "all" and opt.which_epoch not in file:
continue
print('loading checkpoint {}'.format(file))
t2m_transformer = load_trans_model(model_opt, file)
t2m_transformer.eval()
vq_model.eval()
res_model.eval()
t2m_transformer.to(opt.device)
vq_model.to(opt.device)
res_model.to(opt.device)
fid = []
div = []
top1 = []
top2 = []
top3 = []
matching = []
mm = []
repeat_time = 20
for i in range(repeat_time):
with torch.no_grad():
best_fid, best_div, Rprecision, best_matching, best_mm = \
eval_t2m.evaluation_mask_transformer_test_plus_res(eval_val_loader, vq_model, res_model, t2m_transformer,
i, eval_wrapper=eval_wrapper,
time_steps=opt.time_steps, cond_scale=opt.cond_scale,
temperature=opt.temperature, topkr=opt.topkr,
force_mask=opt.force_mask, cal_mm=True)
fid.append(best_fid)
div.append(best_div)
top1.append(Rprecision[0])
top2.append(Rprecision[1])
top3.append(Rprecision[2])
matching.append(best_matching)
mm.append(best_mm)
fid = np.array(fid)
div = np.array(div)
top1 = np.array(top1)
top2 = np.array(top2)
top3 = np.array(top3)
matching = np.array(matching)
mm = np.array(mm)
print(f'{file} final result:')
print(f'{file} final result:', file=f, flush=True)
msg_final = f"\tFID: {np.mean(fid):.3f}, conf. {np.std(fid) * 1.96 / np.sqrt(repeat_time):.3f}\n" \
f"\tDiversity: {np.mean(div):.3f}, conf. {np.std(div) * 1.96 / np.sqrt(repeat_time):.3f}\n" \
f"\tTOP1: {np.mean(top1):.3f}, conf. {np.std(top1) * 1.96 / np.sqrt(repeat_time):.3f}, TOP2. {np.mean(top2):.3f}, conf. {np.std(top2) * 1.96 / np.sqrt(repeat_time):.3f}, TOP3. {np.mean(top3):.3f}, conf. {np.std(top3) * 1.96 / np.sqrt(repeat_time):.3f}\n" \
f"\tMatching: {np.mean(matching):.3f}, conf. {np.std(matching) * 1.96 / np.sqrt(repeat_time):.3f}\n" \
f"\tMultimodality:{np.mean(mm):.3f}, conf.{np.std(mm) * 1.96 / np.sqrt(repeat_time):.3f}\n\n"
# logger.info(msg_final)
print(msg_final)
print(msg_final, file=f, flush=True)
f.close()
# python eval_t2m_trans.py --name t2m_nlayer8_nhead6_ld384_ff1024_cdp0.1_vq --dataset_name t2m --gpu_id 3 --cond_scale 4 --time_steps 18 --temperature 1 --topkr 0.9 --gumbel_sample --ext cs4_ts18_tau1_topkr0.9_gs |