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evaluation_MARDM.py
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import os
from os.path import join as pjoin
import torch
import numpy as np
import random
from torch.utils.data import DataLoader
from models.AE import AE_models
from models.MARDM import MARDM_models
from utils.evaluators import Evaluators
from utils.datasets import Text2MotionDataset, collate_fn
from utils.eval_utils import evaluation_mardm
import argparse
def main(args):
#################################################################################
# Seed #
#################################################################################
torch.backends.cudnn.benchmark = False
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.autograd.set_detect_anomaly(True)
# setting this to true significantly increase training and sampling speed
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
#################################################################################
# Eval Data #
#################################################################################
if args.dataset_name == "t2m":
data_root = f'{args.dataset_dir}/HumanML3D/'
dim_pose = 67
else:
data_root = f'{args.dataset_dir}/KIT-ML/'
dim_pose = 64
motion_dir = pjoin(data_root, 'new_joint_vecs')
text_dir = pjoin(data_root, 'texts')
max_motion_length = 196
mean = np.load(pjoin(data_root, 'Mean.npy'))
std = np.load(pjoin(data_root, 'Std.npy'))
# mean = np.load(f'utils/eval_mean_std/{args.dataset_name}/eval_mean.npy')
# std = np.load(f'utils/eval_mean_std/{args.dataset_name}/eval_std.npy')
eval_mean = np.load(f'utils/eval_mean_std/{args.dataset_name}/eval_mean.npy')
eval_std = np.load(f'utils/eval_mean_std/{args.dataset_name}/eval_std.npy')
split_file = pjoin(data_root, 'test.txt')
eval_dataset = Text2MotionDataset(eval_mean, eval_std, split_file, args.dataset_name, motion_dir, text_dir,
4, max_motion_length, 20, evaluation=True)
eval_loader = DataLoader(eval_dataset, batch_size=32, num_workers=args.num_workers, drop_last=True,
collate_fn=collate_fn, shuffle=True)
#################################################################################
# Models #
#################################################################################
model_dir = pjoin(args.checkpoints_dir, args.dataset_name, args.name, 'model')
ae = AE_models[args.ae_model](input_width=dim_pose)
ckpt = torch.load(pjoin(args.checkpoints_dir, args.dataset_name, args.ae_name, 'model',
'latest.tar' if args.dataset_name == 't2m' else 'net_best_fid.tar'), map_location='cpu')
model_key = 'ae'
ae.load_state_dict(ckpt[model_key])
ema_mardm = MARDM_models[args.model](ae_dim=ae.output_emb_width, cond_mode='text')
model_dir = os.path.join(model_dir, 'latest.tar')
checkpoint = torch.load(model_dir, map_location='cpu')
missing_keys2, unexpected_keys2 = ema_mardm.load_state_dict(checkpoint['ema_mardm'], strict=False)
assert len(unexpected_keys2) == 0
assert all([k.startswith('clip_model.') for k in missing_keys2])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
eval_wrapper = Evaluators(args.dataset_name, device=device)
#################################################################################
# Training Loop #
#################################################################################
out_dir = pjoin(args.checkpoints_dir, args.dataset_name, args.name, 'eval')
os.makedirs(out_dir, exist_ok=True)
f = open(pjoin(out_dir, 'eval.log'), 'w')
ae.eval()
ae.to(device)
ema_mardm.eval()
ema_mardm.to(device)
fid = []
div = []
top1 = []
top2 = []
top3 = []
matching = []
mm = []
clip_scores = []
repeat_time = 20
for i in range(repeat_time):
with torch.no_grad():
best_fid, best_div, best_top1, best_top2, best_top3, best_matching, best_mm, clip_score = 1000, 0, 0, 0, 0, 100, 0, -1
best_fid, best_div, best_top1, best_top2, best_top3, best_matching, best_mm, clip_score, writer, save_now = evaluation_mardm(
model_dir, eval_loader, ema_mardm, ae, None, i, best_fid=best_fid, clip_score_old=clip_score,
best_div=best_div, best_top1=best_top1, best_top2=best_top2, best_top3=best_top3,
best_matching=best_matching, eval_wrapper=eval_wrapper, device=device, train_mean=mean, train_std=std,
time_steps=args.time_steps, cond_scale=args.cfg, temperature=args.temperature, cal_mm=args.cal_mm,
draw=False, hard_pseudo_reorder=args.hard_pseudo_reorder)
fid.append(best_fid)
div.append(best_div)
top1.append(best_top1)
top2.append(best_top2)
top3.append(best_top3)
matching.append(best_matching)
mm.append(best_mm)
clip_scores.append(clip_score)
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)
clip_scores = np.array(clip_scores)
print(f'final result:')
print(f'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" \
f"\tCLIP-Score:{np.mean(clip_scores):.3f}, conf.{np.std(clip_scores) * 1.96 / np.sqrt(repeat_time):.3f}\n\n"
print(msg_final)
print(msg_final, file=f, flush=True)
f.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--name', type=str, default='MARDM')
parser.add_argument('--ae_name', type=str, default="AE")
parser.add_argument('--ae_model', type=str, default='AE_Model')
parser.add_argument('--model', type=str, default='MARDM-SiT-XL')
parser.add_argument('--dataset_name', type=str, default='t2m')
parser.add_argument('--dataset_dir', type=str, default='./datasets')
parser.add_argument("--seed", type=int, default=3407)
parser.add_argument("--num_workers", type=int, default=4)
parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints')
parser.add_argument("--time_steps", default=18, type=int)
parser.add_argument("--cfg", default=4.5, type=float)
parser.add_argument("--temperature", default=1, type=float)
parser.add_argument('--cal_mm', action="store_false")
parser.add_argument('--hard_pseudo_reorder', action="store_true")
arg = parser.parse_args()
main(arg)