-
Notifications
You must be signed in to change notification settings - Fork 8
/
Copy pathdemo.py
182 lines (158 loc) · 6.81 KB
/
demo.py
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
import logging
import os
import sys
import time
from builtins import ValueError
from multiprocessing.sharedctypes import Value
from pathlib import Path
from functools import cmp_to_key
from tempfile import TemporaryDirectory
import numpy as np
import torch, glob, random
import torch.backends.cudnn as cudnn
from torch.utils.data import ConcatDataset, DataLoader
# from torchsummary import summary
from tqdm import tqdm
from dld.config import parse_args
# from mld.datasets.get_dataset import get_datasets
from dld.data.get_data import get_datasets
# from dld.data.sampling import subsample, upsample
from dld.models.get_model import get_module
from dld.utils.logger import create_logger
from dld.data.utils.audio import slice_audio
from dld.data.utils.audio import extract as extract_music35
key_func = lambda x: int(os.path.splitext(x)[0].split("_")[-1].split("slice")[-1])
def stringintcmp_(a, b):
aa, bb = "".join(a.split("_")[:-1]), "".join(b.split("_")[:-1])
ka, kb = key_func(a), key_func(b)
if aa < bb:
return -1
if aa > bb:
return 1
if ka < kb:
return -1
if ka > kb:
return 1
return 0
stringintkey = cmp_to_key(stringintcmp_)
def main():
"""
get input music
"""
# parse options
cfg = parse_args(phase="demo")
print("cfg.device", cfg.DEVICE)
cfg.DEVICE = cfg.DEVICE[0]
device = f"cuda:{cfg.DEVICE}"
# sys.exit(0)
cfg.FOLDER = cfg.TEST.FOLDER
cfg.Name = "demo--" + cfg.NAME
logger = create_logger(cfg, phase="demo")
musicdir = cfg.DEMO.MusicDir
# test_list = ["063", "132", "143", "036", "098", "198", "130", "012", "211", "193", "179", "065", "137", "161", "092", "120", "037", "109", "204", "144"]
test_list = ["063"]
feature_func = extract_music35
sample_length = cfg.FINEDANCE.full_seq_len * 8 /30 # 总长度，8 是指生成8段full_seq_len
sample_size = int(sample_length / (cfg.FINEDANCE.full_seq_len/60) ) - 1
print("sample_length", sample_length)
print("sample_size", sample_size)
temp_dir_list = []
all_cond = []
all_filenames = []
if cfg.DEMO.use_cached_features:
print("Using precomputed features")
# all subdirectories
dir_list = glob.glob(os.path.join(cfg.DEMO.use_cached_features, "*/"))
for dir in dir_list:
file_list = sorted(glob.glob(f"{dir}/*.wav"), key=stringintkey)
juke_file_list = sorted(glob.glob(f"{dir}/*.npy"), key=stringintkey)
assert len(file_list) == len(juke_file_list)
# random chunk after sanity check
rand_idx = random.randint(0, len(file_list) - sample_size)
file_list = file_list[rand_idx : rand_idx + sample_size]
juke_file_list = juke_file_list[rand_idx : rand_idx + sample_size]
cond_list = [np.load(x) for x in juke_file_list]
all_filenames.append(file_list)
all_cond.append(torch.from_numpy(np.array(cond_list)))
else:
print("Computing features for input music")
for wav_file in glob.glob(os.path.join(musicdir, "*.wav")):
songname = os.path.splitext(os.path.basename(wav_file))[0]
if songname not in test_list:
continue
# create temp folder (or use the cache folder if specified)
if cfg.DEMO.use_cached_features:
save_dir = os.path.join(cfg.DEMO.use_cached_features, songname)
Path(save_dir).mkdir(parents=True, exist_ok=True)
dirname = save_dir
else:
temp_dir = TemporaryDirectory()
temp_dir_list.append(temp_dir)
dirname = temp_dir.name
# slice the audio file
print(f"Slicing {wav_file}")
slice_audio(wav_file, cfg.FINEDANCE.full_seq_len/60, cfg.FINEDANCE.full_seq_len/30, dirname)
file_list = sorted(glob.glob(f"{dirname}/*.wav"), key=stringintkey)
# randomly sample a chunk of length at most sample_size
rand_idx = random.randint(0, len(file_list) - int(sample_size))
cond_list = []
# generate juke representations
print(f"Computing features for {wav_file}")
for idx, file in enumerate(tqdm(file_list)):
# if not caching then only calculate for the interested range
if (not cfg.DEMO.use_cached_features) and (not (rand_idx <= idx < rand_idx + sample_size)):
continue
# audio = jukemirlib.load_audio(file)
# reps = jukemirlib.extract(
# audio, layers=[66], downsample_target_rate=30
# )[66]
reps, _ = feature_func(file)[:cfg.FINEDANCE.full_seq_len]
reps = reps[:cfg.FINEDANCE.full_seq_len]
print("reps", reps.shape)
# save reps
if cfg.DEMO.use_cached_features:
featurename = os.path.splitext(file)[0] + ".npy"
np.save(featurename, reps)
# if in the random range, put it into the list of reps we want
# to actually use for generation
if rand_idx <= idx < rand_idx + sample_size:
cond_list.append(reps)
cond_list = torch.from_numpy(np.array(cond_list))
all_cond.append(cond_list)
all_filenames.append(file_list[rand_idx : rand_idx + sample_size])
output_dir = Path(
os.path.join(cfg.FOLDER, str(cfg.model.model_type), str(cfg.NAME),
"samples_long_" + cfg.TIME))
output_dir.mkdir(parents=True, exist_ok=True)
# cuda options
# if cfg.ACCELERATOR == "gpu":
# os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
# str(x) for x in cfg.DEVICE)
# device = torch.device("cuda")
# load dataset to extract nfeats dim of model
dataset = get_datasets(cfg, logger=logger, phase="test")[0]
# create mld model
total_time = time.time()
model = get_module(cfg, dataset)
logger.info("Loading checkpoints from {}".format(cfg.TEST.CHECKPOINTS))
state_dict = torch.load(cfg.TEST.CHECKPOINTS,
map_location="cpu")["state_dict"]
model.load_state_dict(state_dict, strict=True)
logger.info("model {} loaded".format(cfg.model.model_type))
model.to(device)
model.eval()
start_time = time.time()
print("Generating dances")
for i in range(len(all_cond)):
data_tuple = None, all_cond[i], all_filenames[i]
model.render_sample_ori(
data_tuple, "test", output_dir, render_count=-1, fk_out=output_dir, render=cfg.DEMO.RENDER, setmode="long"
)
print("Done")
torch.cuda.empty_cache()
for temp_dir in temp_dir_list:
temp_dir.cleanup()
total = time.time() - start_time
print("total time is ", total)
if __name__ == "__main__":
main()