text2motion/datasets/dataset.py

import codecs as cs
import os
import random
from os.path import join as pjoin

import numpy as np
import torch
from torch.utils import data
from tqdm import tqdm

from .utils import drop_shapes_from_motion_arr, load_label_from_file

class Text2MotionDataset(data.Dataset):
    """Dataset for Text2Motion generation task.

    """
    def __init__(self, opt, mean, std, split_file, times=1, w_vectorizer=None, eval_mode=False):
        self.opt = opt
        self.max_length = 20
        self.times = times
        self.w_vectorizer = w_vectorizer
        self.eval_mode = eval_mode
        min_motion_len = 40 if self.opt.dataset_name =='t2m' else 24

        joints_num = opt.joints_num

        data_dict = {}
        id_list = []
        print(f"split file: {split_file}")
        with cs.open(split_file, 'r') as f:
            for line in f.readlines():
                id_list.append(line.strip())

        new_name_list = []
        length_list = []
        print(f"id-list length: {len(id_list)}")
        for name in tqdm(id_list):
            try:
                print(f"attempting to load motion for {name} at {pjoin(opt.motion_dir, name + '.npy')}")
                motion = np.load(pjoin(opt.motion_dir, name + '.npy'))
                if self.opt.dataset_name.lower() == 'grab':
                    motion = drop_shapes_from_motion_arr(motion)
                    assert motion.shape[-1] == opt.dim_pose, f"motion shape {motion.shape} does not match dim_pose {opt.dim_pose}"
                    print(f"grab motion shape: {motion.shape}")
                print(f"len of motion: {len(motion)}")
                # TODO (elmc): verify we don't need this for GRAB data
                # if (len(motion)) < min_motion_len or (len(motion) >= 200):
                #     continue
                text_data = []
                flag = False
                with cs.open(pjoin(opt.text_dir, name + '.txt')) as f:
                    for line in f.readlines():
                        text_dict = {}
                        line_split = line.strip().split('#')
                        caption = line_split[0]
                        # append face_text to caption
                        emotion_label = load_label_from_file(pjoin(opt.face_text_dir, name + '.txt'))
                        caption = f"{emotion_label} {caption}"
                        f_tag = 0.0
                        to_tag = 0.0
                        # TODO (elmc): add actual tokens back for grab
                        tokens = []
                        if self.opt.dataset_name.lower() != 'grab':
                            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:
                            n_motion = motion[int(f_tag*20) : int(to_tag*20)]
                            if (len(n_motion)) < min_motion_len or (len(n_motion) >= 200):
                                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))

                if flag:
                    data_dict[name] = {'motion': motion,
                                       'length': len(motion),
                                       'text':text_data}
                    new_name_list.append(name)
                    length_list.append(len(motion))
            except Exception as e:
                # Some motion may not exist in KIT dataset
                print(f"failed to load motion for {name} at {pjoin(opt.motion_dir, name + '.npy')} due to {e}")

        if not new_name_list or not length_list:
            raise ValueError(f'No data loaded, new_name_list has len {len(new_name_list)} and length_list has len {len(length_list)}')
        name_list, length_list = zip(*sorted(zip(new_name_list, length_list), key=lambda x: x[1]))
        print(f"LOADED length of name_list: {len(name_list)}")
        # TODO (elmc): calculate mean and std and save to load here?
        if opt.is_train:
        #     # TODO (elle): how best to standardize the data?

        #     # root_rot_velocity (B, seq_len, 1)
        #     std[0:1] = std[0:1] / opt.feat_bias
        #     # root_linear_velocity (B, seq_len, 2)
        #     std[1:3] = std[1:3] / opt.feat_bias
        #     # root_y (B, seq_len, 1)
        #     std[3:4] = std[3:4] / opt.feat_bias
        #     # ric_data (B, seq_len, (joint_num - 1)*3)
        #     std[4: 4 + (joints_num - 1) * 3] = std[4: 4 + (joints_num - 1) * 3] / 1.0
        #     # rot_data (B, seq_len, (joint_num - 1)*6)
        #     std[4 + (joints_num - 1) * 3: 4 + (joints_num - 1) * 9] = std[4 + (joints_num - 1) * 3: 4 + (
        #                 joints_num - 1) * 9] / 1.0
        #     # local_velocity (B, seq_len, joint_num*3)
        #     std[4 + (joints_num - 1) * 9: 4 + (joints_num - 1) * 9 + joints_num * 3] = std[
        #                                                                                4 + (joints_num - 1) * 9: 4 + (
        #                                                                                            joints_num - 1) * 9 + joints_num * 3] / 1.0
        #     # foot contact (B, seq_len, 4)
        #     std[4 + (joints_num - 1) * 9 + joints_num * 3:] = std[
        #                                                       4 + (joints_num - 1) * 9 + joints_num * 3:] / opt.feat_bias

            # assert 4 + (joints_num - 1) * 9 + joints_num * 3 + 4 == mean.shape[-1]
            # TODO (elmc): add back in
            np.save(pjoin(opt.meta_dir, 'mean.npy'), mean)
            np.save(pjoin(opt.meta_dir, 'std.npy'), std)

        self.mean = mean
        self.std = std
        self.length_arr = np.array(length_list)
        self.data_dict = data_dict
        self.name_list = name_list

    def inv_transform(self, data):
        return data * self.std + self.mean

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

    def __len__(self):
        # authors explain why they multiple self.times here instead of increasing epochs
        # https://github.com/mingyuan-zhang/MotionDiffuse/issues/12
        # also say it's not necessary set use persistent_workers = True in build_dataloader
        return self.real_len() * self.times

    def __getitem__(self, item):
        idx = item % self.real_len()
        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 = text_data['caption']
        max_motion_length = self.opt.max_motion_length
        # TODO (elmc): delete this and replace with if m_length >= self..etc
        # motion = motion[:max_motion_length]
        # TODO (elmc): add back in
        if m_length >= self.opt.max_motion_length:
            idx = random.randint(0, len(motion) - max_motion_length)
            motion = motion[idx: idx + max_motion_length]
        else:
            padding_len = max_motion_length - m_length
            D = motion.shape[1]
            padding_zeros = np.zeros((padding_len, D))
            motion = np.concatenate((motion, padding_zeros), axis=0)

        assert len(motion) == max_motion_length
        "Z Normalization"
        # TODO (elmc): add standardization back in
        motion = (motion - self.mean) / self.std

        if self.eval_mode:
            tokens = text_data['tokens']
            if len(tokens) < self.opt.max_text_len:
                # pad with "unk"
                tokens = ['sos/OTHER'] + tokens + ['eos/OTHER']
                sent_len = len(tokens)
                tokens = tokens + ['unk/OTHER'] * (self.opt.max_text_len + 2 - sent_len)
            else:
                # crop
                tokens = tokens[:self.opt.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)
            return word_embeddings, pos_one_hots, caption, sent_len, motion, m_length
        return caption, motion, m_length