dataset/dataset_TM_train.py

import torch
from torch.utils import data
import numpy as np
from os.path import join as pjoin
import random
import codecs as cs
from tqdm import tqdm
import utils.paramUtil as paramUtil
from torch.utils.data._utils.collate import default_collate


def collate_fn(batch):
    batch.sort(key=lambda x: x[3], reverse=True)
    return default_collate(batch)


'''For use of training text-2-motion generative model'''
class Text2MotionDataset(data.Dataset):
    def __init__(self, dataset_name, feat_bias=5, unit_length=4, codebook_size=1024, tokenizer_name=None):
        self.max_length = 64
        self.pointer = 0
        self.dataset_name = dataset_name
        self.unit_length = unit_length
        self.mot_end_idx = codebook_size
        self.mot_pad_idx = codebook_size + 1

        print(f"Loading dataset: {dataset_name}")

        if dataset_name == 't2m':
            self.data_root = './dataset/Sample1'
            self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
            self.text_dir = pjoin(self.data_root, 'texts')
            self.joints_num = 22
            radius = 4
            fps = 20
            self.max_motion_length = 26 if unit_length == 8 else 51
            dim_pose = 263
            kinematic_chain = paramUtil.t2m_kinematic_chain
        elif dataset_name == 'kit':
            self.data_root = './dataset/KIT-ML'
            self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
            self.text_dir = pjoin(self.data_root, 'texts')
            self.joints_num = 21
            radius = 240 * 8
            fps = 12.5
            dim_pose = 251
            self.max_motion_length = 26 if unit_length == 8 else 51
            kinematic_chain = paramUtil.kit_kinematic_chain

        split_file = pjoin(self.data_root, 'train.txt')

        id_list = []
        with cs.open(split_file, 'r') as f:
            for line in f.readlines():
                id_list.append(line.strip())

        new_name_list = []
        data_dict = {}
        for name in tqdm(id_list):
            try:
                m_token_list = np.load(pjoin(self.data_root, tokenizer_name, f'{name}.npy'))

                with cs.open(pjoin(self.text_dir, f'{name}.txt')) as f:
                    text_data = []
                    flag = False
                    lines = f.readlines()

                    for line in lines:
                        try:
                            text_dict = {}
                            line_split = line.strip().split('#')
                            caption = line_split[0]
                            t_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'] = t_tokens
                            if f_tag == 0.0 and to_tag == 0.0:
                                flag = True
                                text_data.append(text_dict)
                            else:
                                m_token_list_new = [tokens[int(f_tag * fps / unit_length): int(to_tag * fps / unit_length)] for tokens in m_token_list if int(f_tag * fps / unit_length) < int(to_tag * fps / unit_length)]

                                if len(m_token_list_new) == 0:
                                    continue
                                new_name = f'{name}_{f_tag}_{to_tag}'

                                data_dict[new_name] = {'m_token_list': m_token_list_new,
                                                       'text': [text_dict]}
                                new_name_list.append(new_name)
                        except:
                            pass

                if flag:
                    data_dict[name] = {'m_token_list': m_token_list,
                                       'text': text_data}
                    new_name_list.append(name)
            except:
                pass

        self.data_dict = data_dict
        self.name_list = new_name_list

        print(f"Dataset loaded. Number of samples: {len(self.data_dict)}")

    def __len__(self):
        return len(self.data_dict)

    def __getitem__(self, item):
        data = self.data_dict[self.name_list[item]]
        m_token_list, text_list = data['m_token_list'], data['text']
        m_tokens = random.choice(m_token_list)

        text_data = random.choice(text_list)
        caption = text_data['caption']

        coin = np.random.choice([False, False, True])
        if coin:
            coin2 = np.random.choice([True, False])
            if coin2:
                m_tokens = m_tokens[:-1]
            else:
                m_tokens = m_tokens[1:]
        m_tokens_len = m_tokens.shape[0]

        if m_tokens_len + 1 < self.max_motion_length:
            m_tokens = np.concatenate([m_tokens, np.ones((1), dtype=int) * self.mot_end_idx, np.ones((self.max_motion_length - 1 - m_tokens_len), dtype=int) * self.mot_pad_idx], axis=0)
        else:
            m_tokens = np.concatenate([m_tokens, np.ones((1), dtype=int) * self.mot_end_idx], axis=0)

        return caption, m_tokens.reshape(-1), m_tokens_len


def DATALoader(dataset_name,
                batch_size, codebook_size, tokenizer_name, unit_length=4,
                num_workers = 8) : 

    train_loader = torch.utils.data.DataLoader(Text2MotionDataset(dataset_name, codebook_size = codebook_size, tokenizer_name = tokenizer_name, unit_length=unit_length),
                                              batch_size,
                                              shuffle=True,
                                              num_workers=num_workers,
                                              #collate_fn=collate_fn,
                                              drop_last = True)
    

    return train_loader


def cycle(iterable):
    while True:
        for x in iterable:
            yield x