Spaces:
Running
Running
File size: 9,045 Bytes
c0eac48 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
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 |