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import os
import random
import logging
import codecs as cs
from os.path import join as pjoin
import numpy as np
from rich.progress import track
import torch
from torch.utils.data import Dataset
from .scripts.motion_process import recover_from_ric
from .utils.word_vectorizer import WordVectorizer
logger = logging.getLogger(__name__)
class MotionDataset(Dataset):
def __init__(self, mean: np.ndarray, std: np.ndarray,
split_file: str, motion_dir: str, window_size: int,
tiny: bool = False, progress_bar: bool = True, **kwargs) -> None:
self.data = []
self.lengths = []
id_list = []
with cs.open(split_file, "r") as f:
for line in f.readlines():
id_list.append(line.strip())
maxdata = 10 if tiny else 1e10
if progress_bar:
enumerator = enumerate(
track(
id_list,
f"Loading HumanML3D {split_file.split('/')[-1].split('.')[0]}",
))
else:
enumerator = enumerate(id_list)
count = 0
for i, name in enumerator:
if count > maxdata:
break
try:
motion = np.load(pjoin(motion_dir, name + '.npy'))
if motion.shape[0] < window_size:
continue
self.lengths.append(motion.shape[0] - window_size)
self.data.append(motion)
except Exception as e:
print(e)
pass
self.cumsum = np.cumsum([0] + self.lengths)
if not tiny:
logger.info("Total number of motions {}, snippets {}".format(len(self.data), self.cumsum[-1]))
self.mean = mean
self.std = std
self.window_size = window_size
def __len__(self) -> int:
return self.cumsum[-1]
def __getitem__(self, item: int) -> tuple:
if item != 0:
motion_id = np.searchsorted(self.cumsum, item) - 1
idx = item - self.cumsum[motion_id] - 1
else:
motion_id = 0
idx = 0
motion = self.data[motion_id][idx:idx + self.window_size]
"Z Normalization"
motion = (motion - self.mean) / self.std
return motion, self.window_size
class Text2MotionDataset(Dataset):
def __init__(
self,
mean: np.ndarray,
std: np.ndarray,
split_file: str,
w_vectorizer: WordVectorizer,
max_motion_length: int,
min_motion_length: int,
max_text_len: int,
unit_length: int,
motion_dir: str,
text_dir: str,
fps: int,
padding_to_max: bool,
njoints: int,
tiny: bool = False,
progress_bar: bool = True,
**kwargs,
) -> None:
self.w_vectorizer = w_vectorizer
self.max_motion_length = max_motion_length
self.min_motion_length = min_motion_length
self.max_text_len = max_text_len
self.unit_length = unit_length
self.padding_to_max = padding_to_max
self.njoints = njoints
data_dict = {}
id_list = []
with cs.open(split_file, "r") as f:
for line in f.readlines():
id_list.append(line.strip())
self.id_list = id_list
maxdata = 10 if tiny else 1e10
if progress_bar:
enumerator = enumerate(
track(
id_list,
f"Loading HumanML3D {split_file.split('/')[-1].split('.')[0]}",
))
else:
enumerator = enumerate(id_list)
count = 0
bad_count = 0
new_name_list = []
length_list = []
for i, name in enumerator:
if count > maxdata:
break
try:
motion = np.load(pjoin(motion_dir, name + ".npy"))
if len(motion) < self.min_motion_length or len(motion) >= self.max_motion_length:
bad_count += 1
continue
text_data = []
flag = False
with cs.open(pjoin(text_dir, name + ".txt")) as f:
for line in f.readlines():
text_dict = {}
line_split = line.strip().split("#")
caption = line_split[0]
tokens = line_split[1].split(" ")
f_tag = float(line_split[2])
to_tag = float(line_split[3])
f_tag = 0.0 if np.isnan(f_tag) else f_tag
to_tag = 0.0 if np.isnan(to_tag) else to_tag
text_dict["caption"] = caption
text_dict["tokens"] = tokens
if f_tag == 0.0 and to_tag == 0.0:
flag = True
text_data.append(text_dict)
else:
try:
n_motion = motion[int(f_tag * fps): int(to_tag * fps)]
if (len(n_motion)) < self.min_motion_length or \
len(n_motion) >= self.max_motion_length:
continue
new_name = random.choice("ABCDEFGHIJKLMNOPQRSTUVW") + "_" + name
while new_name in data_dict:
new_name = random.choice("ABCDEFGHIJKLMNOPQRSTUVW") + "_" + name
data_dict[new_name] = {
"motion": n_motion,
"length": len(n_motion),
"text": [text_dict],
}
new_name_list.append(new_name)
length_list.append(len(n_motion))
except ValueError:
print(line_split)
print(line_split[2], line_split[3], f_tag, to_tag, name)
if flag:
data_dict[name] = {
"motion": motion,
"length": len(motion),
"text": text_data,
}
new_name_list.append(name)
length_list.append(len(motion))
count += 1
except Exception as e:
print(e)
pass
name_list, length_list = zip(
*sorted(zip(new_name_list, length_list), key=lambda x: x[1]))
if not tiny:
logger.info(f"Reading {len(self.id_list)} motions from {split_file}.")
logger.info(f"Total {len(name_list)} motions are used.")
logger.info(f"{bad_count} motion sequences not within the length range of "
f"[{self.min_motion_length}, {self.max_motion_length}) are filtered out.")
self.mean = mean
self.std = std
control_args = kwargs['control_args']
self.control_mode = None
if os.path.exists(control_args.MEAN_STD_PATH):
self.raw_mean = np.load(pjoin(control_args.MEAN_STD_PATH, 'Mean_raw.npy'))
self.raw_std = np.load(pjoin(control_args.MEAN_STD_PATH, 'Std_raw.npy'))
else:
self.raw_mean = self.raw_std = None
if not tiny and control_args.CONTROL:
self.t_ctrl = control_args.TEMPORAL
self.training_control_joints = np.array(control_args.TRAIN_JOINTS)
self.testing_control_joints = np.array(control_args.TEST_JOINTS)
self.training_density = control_args.TRAIN_DENSITY
self.testing_density = control_args.TEST_DENSITY
self.control_mode = 'val' if ('test' in split_file or 'val' in split_file) else 'train'
if self.control_mode == 'train':
logger.info(f'Training Control Joints: {self.training_control_joints}')
logger.info(f'Training Control Density: {self.training_density}')
else:
logger.info(f'Testing Control Joints: {self.testing_control_joints}')
logger.info(f'Testing Control Density: {self.testing_density}')
logger.info(f"Temporal Control: {self.t_ctrl}")
self.data_dict = data_dict
self.name_list = name_list
def __len__(self) -> int:
return len(self.name_list)
def random_mask(self, joints: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
choose_joint = self.testing_control_joints
length = joints.shape[0]
density = self.testing_density
if density in [1, 2, 5]:
choose_seq_num = density
else:
choose_seq_num = int(length * density / 100)
if self.t_ctrl:
choose_seq = np.arange(0, choose_seq_num)
else:
choose_seq = np.random.choice(length, choose_seq_num, replace=False)
choose_seq.sort()
mask_seq = np.zeros((length, self.njoints, 3))
for cj in choose_joint:
mask_seq[choose_seq, cj] = 1.0
joints = (joints - self.raw_mean) / self.raw_std
joints = joints * mask_seq
return joints, mask_seq
def random_mask_train(self, joints: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
if self.t_ctrl:
choose_joint = self.training_control_joints
else:
num_joints = len(self.training_control_joints)
num_joints_control = 1
choose_joint = np.random.choice(num_joints, num_joints_control, replace=False)
choose_joint = self.training_control_joints[choose_joint]
length = joints.shape[0]
if self.training_density == 'random':
choose_seq_num = np.random.choice(length - 1, 1) + 1
else:
choose_seq_num = int(length * random.uniform(self.training_density[0], self.training_density[1]) / 100)
if self.t_ctrl:
choose_seq = np.arange(0, choose_seq_num)
else:
choose_seq = np.random.choice(length, choose_seq_num, replace=False)
choose_seq.sort()
mask_seq = np.zeros((length, self.njoints, 3))
for cj in choose_joint:
mask_seq[choose_seq, cj] = 1
joints = (joints - self.raw_mean) / self.raw_std
joints = joints * mask_seq
return joints, mask_seq
def __getitem__(self, idx: int) -> tuple:
data = self.data_dict[self.name_list[idx]]
motion, m_length, text_list = data["motion"], data["length"], data["text"]
# Randomly select a caption
text_data = random.choice(text_list)
caption, tokens = text_data["caption"], text_data["tokens"]
if len(tokens) < self.max_text_len:
# pad with "unk"
tokens = ["sos/OTHER"] + tokens + ["eos/OTHER"]
sent_len = len(tokens)
tokens = tokens + ["unk/OTHER"] * (self.max_text_len + 2 - sent_len)
else:
# crop
tokens = tokens[:self.max_text_len]
tokens = ["sos/OTHER"] + tokens + ["eos/OTHER"]
sent_len = len(tokens)
pos_one_hots = []
word_embeddings = []
for token in tokens:
word_emb, pos_oh = self.w_vectorizer[token]
pos_one_hots.append(pos_oh[None, :])
word_embeddings.append(word_emb[None, :])
pos_one_hots = np.concatenate(pos_one_hots, axis=0)
word_embeddings = np.concatenate(word_embeddings, axis=0)
# Crop the motions in to times of 4, and introduce small variations
if self.unit_length < 10:
coin2 = np.random.choice(["single", "single", "double"])
else:
coin2 = "single"
if coin2 == "double":
m_length = (m_length // self.unit_length - 1) * self.unit_length
elif coin2 == "single":
m_length = (m_length // self.unit_length) * self.unit_length
idx = random.randint(0, len(motion) - m_length)
motion = motion[idx:idx + m_length]
hint, hint_mask = None, None
if self.control_mode is not None:
joints = recover_from_ric(torch.from_numpy(motion).float(), self.njoints)
joints = joints.numpy()
if self.control_mode == 'train':
hint, hint_mask = self.random_mask_train(joints)
else:
hint, hint_mask = self.random_mask(joints)
if self.padding_to_max:
padding = np.zeros((self.max_motion_length - m_length, *hint.shape[1:]))
hint = np.concatenate([hint, padding], axis=0)
hint_mask = np.concatenate([hint_mask, padding], axis=0)
"Z Normalization"
motion = (motion - self.mean) / self.std
if self.padding_to_max:
padding = np.zeros((self.max_motion_length - m_length, motion.shape[1]))
motion = np.concatenate([motion, padding], axis=0)
return (word_embeddings,
pos_one_hots,
caption,
sent_len,
motion,
m_length,
"_".join(tokens),
(hint, hint_mask))
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