finedance 测试笔记

依赖库：

预测流程：

音乐wav切割120帧

general_all.py改进

smplx 学习笔记：

依赖库：

import pickle5 as pickle

pypi尚pickle5最高python版本3.7：

pickle5 · PyPI

解决方法，改为

import pickle

预测流程：

Data preprocessing
python data/code/pre_motion.py

处理运动数据 npy转npy

音乐wav切割120帧

data/code/pre_music.py

Training
accelerate launch train_seq.py
Generate
python data/code/slice_music_motion.py
python generate_all.py --motion_save_dir generated/finedance_seq_120_dancer --save_motions
Generate dance by custom music
python test.py --music_dir 'your music dir' --save_motions
Visualization
python render.py --modir eval/motions --gpu 0

general_all.py改进

import glob
import os,sys
from functools import cmp_to_key
from pathlib import Path# import jukemirlib
import numpy as np
import torch
from tqdm import tqdmfrom args import FineDance_parse_test_opt
from train_seq import EDGE
from dataset.FineDance_dataset import get_train_test_list# test_list = ["063", "132", "143", "036", "098", "198", "130", "012", "211", "193", "179", "065", "137", "161", "092", "120", "037", "109", "204", "144"]def test(opt):# split = get_train_test_dict(opt.datasplit)train_list, test_list, ignore_list = get_train_test_list(opt.datasplit)for file in os.listdir(music_dir):if file[:3] in ignore_list:continueif not file[:3] in test_list:continuefile_name = file[:-4]music_fea = np.load(os.path.join(music_dir, file))music_fea = torch.from_numpy(music_fea).cuda().unsqueeze(0)music_fea = music_fea.repeat(count, 1, 1)all_filenames = [file_name]*count# directory for optionally saving the dances for evalfk_out = Noneif opt.save_motions:fk_out = opt.motion_save_diros.makedirs(fk_out,exist_ok=True)model = EDGE(opt, opt.feature_type, opt.checkpoint)model.eval()data_tuple = None, music_fea, all_filenamesmodel.render_sample(data_tuple, "test", opt.render_dir, render_count=10, mode='normal', fk_out=fk_out, render=not opt.no_render)print("Done")if __name__ == "__main__":test_list = ["063", "144"]data_dir=r'E:\迅雷下载/'music_dir = data_dir+"data/finedance/div_by_time/music_npy_120"count = 10opt = FineDance_parse_test_opt()test(opt)# python test.py --save_motions