File size: 5,107 Bytes
8b3f571 | 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 | import numpy as np
import random, torch, joblib
from torch.utils.data import Dataset
from mmengine.registry import DATASETS, FUNCTIONS
import time
from torch.nn.utils.rnn import pad_sequence
FPS = 30
@DATASETS.register_module()
class Humanml3Dataset(Dataset):
'''
data format:
dict(
motion_272: np.ndarray (T, 272)
# text
text: [[caption, start_time, end_time], ...],
# music
music_feat:
# traj
pivot_traj:
)
'''
def __init__(self, split_file='', window_size=-1, unit_length=4, mean_path='', std_path='',
min_motion_length=60, max_motion_length=300, repeat_time=1, interval=1, add_traj=False):
super().__init__()
self.min_motion_length = min_motion_length
self.max_motion_length = max_motion_length
self.window_size = window_size
self.unit_length = unit_length
self.repeat_time = repeat_time
# self.condition_types = ['text', 'music', 'traj', 'video']
st = time.time()
print('load data...')
data_infos = joblib.load(split_file)
print(f'load data with {time.time() - st}s')
self.data_infos = []
for data_info in data_infos:
if data_info['motion_272'].shape[0] > self.min_motion_length:
self.data_infos.append(data_info)
self.data_infos = self.data_infos[::interval]
self.motion_mean = torch.from_numpy(np.load(mean_path).astype(np.float32))
self.motion_std = torch.from_numpy(np.load(std_path).astype(np.float32))
self.add_traj = add_traj
def __len__(self):
return len(self.data_infos) * self.repeat_time
def __getitem__(self, index):
data_info = self.data_infos[index % len(self.data_infos)]
# 1. load motion
motion = torch.as_tensor(data_info['motion_272'], dtype=torch.float32)
# 2. load text condition
text_data = ''
has_text = False
start_time, end_time = 0, 0
if 'text' in data_info.keys():
# text_data = random.choice(data_info['text'])
text_data, start_time, end_time = random.choice(data_info['text'])
has_text = True
if end_time > 0: # start / end time 如果都是0的话, 表示不需要clip
motion = motion[int(start_time * FPS):int(end_time * FPS)]
# 3. clip motion
T = motion.shape[0]
if self.window_size > 0:
assert T >= self.window_size
idx = 0 if T == self.window_size else random.randint(0, T - self.window_size)
motion = motion[idx:idx+self.window_size]
motion_length = self.window_size
else:
motion_length = (T // self.unit_length) * self.unit_length
if motion_length > self.max_motion_length:
motion_length = self.max_motion_length
idx = random.randint(0, T - motion_length)
motion = motion[idx:idx+motion_length]
motion = (motion - self.motion_mean) / self.motion_std
return dict(
motion=motion,
motion_length=motion_length,
mean=self.motion_mean,
std=self.motion_std,
text_data=text_data,
)
@DATASETS.register_module()
class MMDataset(Humanml3Dataset):
def __getitem__(self, index):
data_info = self.data_infos[index % len(self.data_infos)]
# 1. load motion
motion = torch.as_tensor(data_info['motion_272'], dtype=torch.float32)
# 2. load text condition
text_data = random.choice(data_info['text'])
# 3. clip motion
T = motion.shape[0]
if self.window_size > 0:
assert T >= self.window_size
idx = 0 if T == self.window_size else random.randint(0, T - self.window_size)
motion = motion[idx:idx+self.window_size]
motion_length = self.window_size
else:
motion_length = (T // self.unit_length) * self.unit_length
if motion_length > self.max_motion_length:
motion_length = self.max_motion_length
idx = random.randint(0, T - motion_length)
motion = motion[idx:idx+motion_length]
motion = (motion - self.motion_mean) / self.motion_std
return dict(
motion=motion,
motion_length=motion_length,
mean=self.motion_mean,
std=self.motion_std,
text_data=text_data,
)
@FUNCTIONS.register_module()
def text_collate_fn(batch):
motion = [item['motion'] for item in batch] # [motion: T, C]
motion_length = torch.as_tensor([item['motion_length'] for item in batch])
motion = pad_sequence(motion, batch_first=True)
mean = batch[0]['mean']
std = batch[0]['std']
text_data = [item['text_data'] for item in batch]
return dict(
motion=motion,
motion_length=motion_length,
mean=mean,
std=std,
text_data=text_data,
) |