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data_loader.py
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240 lines (202 loc) · 10.6 KB
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import logging
import pickle
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset
__all__ = ['MMDataLoader']
logger = logging.getLogger('MMSA')
class MMDataset(Dataset):
def __init__(self, args, mode='train'):
self.mode = mode
self.args = args
DATASET_MAP = {
'mosi': self.__init_mosi,
'mosei': self.__init_mosei,
'sims': self.__init_sims,
'simsv2':self.__init_simsv2,
}
DATASET_MAP[args['dataset_name']]()
def __init_mosi(self):
if self.args['custom_feature']:
# use custom feature file extracted with MMSA-FET
with open(self.args['custom_feature'], 'rb') as f:
data = pickle.load(f)
else:
# use deault feature file specified in config file
with open(self.args['featurePath'], 'rb') as f:
data = pickle.load(f)
if self.args.get('use_bert', None):
self.text = data[self.mode]['text_bert'].astype(np.float32)
self.args['feature_dims'][0] = 768
else:
self.text = data[self.mode]['text'].astype(np.float32)
self.args['feature_dims'][0] = self.text.shape[2]
self.audio = data[self.mode]['audio'].astype(np.float32)
self.args['feature_dims'][1] = self.audio.shape[2]
self.vision = data[self.mode]['vision'].astype(np.float32)
self.args['feature_dims'][2] = self.vision.shape[2]
self.raw_text = data[self.mode]['raw_text']
self.ids = data[self.mode]['id']
# Overide with custom modality features
if self.args['feature_T']:
with open(self.args['feature_T'], 'rb') as f:
data_T = pickle.load(f)
if self.args.get('use_bert', None):
self.text = data_T[self.mode]['text_bert'].astype(np.float32)
self.args['feature_dims'][0] = 768
else:
self.text = data_T[self.mode]['text'].astype(np.float32)
self.args['feature_dims'][0] = self.text.shape[2]
if self.args['feature_A']:
with open(self.args['feature_A'], 'rb') as f:
data_A = pickle.load(f)
self.audio = data_A[self.mode]['audio'].astype(np.float32)
self.args['feature_dims'][1] = self.audio.shape[2]
if self.args['feature_V']:
with open(self.args['feature_V'], 'rb') as f:
data_V = pickle.load(f)
self.vision = data_V[self.mode]['vision'].astype(np.float32)
self.args['feature_dims'][2] = self.vision.shape[2]
self.labels = {
# 'M': data[self.mode][self.args['train_mode']+'_labels'].astype(np.float32)
'M': np.array(data[self.mode]['regression_labels']).astype(np.float32)
}
if 'sims' in self.args['dataset_name']:
for m in "TAV":
self.labels[m] = data[self.mode]['regression' + '_labels_' + m].astype(np.float32)
logger.info(f"{self.mode} samples: {self.labels['M'].shape}")
if not self.args['need_data_aligned']:
if self.args['feature_A']:
self.audio_lengths = list(data_A[self.mode]['audio_lengths'])
else:
self.audio_lengths = data[self.mode]['audio_lengths']
if self.args['feature_V']:
self.vision_lengths = list(data_V[self.mode]['vision_lengths'])
else:
self.vision_lengths = data[self.mode]['vision_lengths']
self.audio[self.audio == -np.inf] = 0
if self.args.get('data_missing'):
# Currently only support unaligned data missing.
self.text_m, self.text_length, self.text_mask, self.text_missing_mask = self.generate_m(self.text[:,0,:], self.text[:,1,:], None,
self.args.missing_rate[0], self.args.missing_seed[0], mode='text')
Input_ids_m = np.expand_dims(self.text_m, 1)
Input_mask = np.expand_dims(self.text_mask, 1)
Segment_ids = np.expand_dims(self.text[:,2,:], 1)
self.text_m = np.concatenate((Input_ids_m, Input_mask, Segment_ids), axis=1)
if self.args['need_data_aligned']:
self.audio_lengths = np.sum(self.text[:,1,:], axis=1, dtype=np.int32)
self.vision_lengths = np.sum(self.text[:,1,:], axis=1, dtype=np.int32)
self.audio_m, self.audio_length, self.audio_mask, self.audio_missing_mask = self.generate_m(self.audio, None, self.audio_lengths,
self.args.missing_rate[1], self.args.missing_seed[1], mode='audio')
self.vision_m, self.vision_length, self.vision_mask, self.vision_missing_mask = self.generate_m(self.vision, None, self.vision_lengths,
self.args.missing_rate[2], self.args.missing_seed[2], mode='vision')
if self.args.get('need_normalized'):
self.__normalize()
def __init_mosei(self):
return self.__init_mosi()
def __init_sims(self):
return self.__init_mosi()
def __init_simsv2(self):
return self.__init_mosi()
def generate_m(self, modality, input_mask, input_len, missing_rate, missing_seed, mode='text'):
if mode == 'text':
input_len = np.argmin(input_mask, axis=1)
elif mode == 'audio' or mode == 'vision':
input_mask = np.array([np.array([1] * length + [0] * (modality.shape[1] - length)) for length in input_len])
np.random.seed(missing_seed)
missing_mask = (np.random.uniform(size=input_mask.shape) > missing_rate) * input_mask
assert missing_mask.shape == input_mask.shape
if mode == 'text':
# CLS SEG Token unchanged.
for i, instance in enumerate(missing_mask):
instance[0] = instance[input_len[i] - 1] = 1
modality_m = missing_mask * modality + (100 * np.ones_like(modality)) * (input_mask - missing_mask) # UNK token: 100.
elif mode == 'audio' or mode == 'vision':
modality_m = missing_mask.reshape(modality.shape[0], modality.shape[1], 1) * modality
return modality_m, input_len, input_mask, missing_mask
def __truncate(self):
# NOTE: truncate input to specific length.
def do_truncate(modal_features, length):
if length == modal_features.shape[1]:
return modal_features
truncated_feature = []
padding = np.array([0 for i in range(modal_features.shape[2])])
for instance in modal_features:
for index in range(modal_features.shape[1]):
if((instance[index] == padding).all()):
if(index + length >= modal_features.shape[1]):
truncated_feature.append(instance[index:index+20])
break
else:
truncated_feature.append(instance[index:index+20])
break
truncated_feature = np.array(truncated_feature)
return truncated_feature
text_length, audio_length, video_length = self.args['seq_lens']
self.vision = do_truncate(self.vision, video_length)
self.text = do_truncate(self.text, text_length)
self.audio = do_truncate(self.audio, audio_length)
def __normalize(self):
# (num_examples,max_len,feature_dim) -> (max_len, num_examples, feature_dim)
self.vision = np.transpose(self.vision, (1, 0, 2))
self.audio = np.transpose(self.audio, (1, 0, 2))
# For visual and audio modality, we average across time:
# The original data has shape (max_len, num_examples, feature_dim)
# After averaging they become (1, num_examples, feature_dim)
self.vision = np.mean(self.vision, axis=0, keepdims=True)
self.audio = np.mean(self.audio, axis=0, keepdims=True)
# remove possible NaN values
self.vision[self.vision != self.vision] = 0
self.audio[self.audio != self.audio] = 0
self.vision = np.transpose(self.vision, (1, 0, 2))
self.audio = np.transpose(self.audio, (1, 0, 2))
def __len__(self):
return len(self.labels['M'])
def get_seq_len(self):
if 'use_bert' in self.args and self.args['use_bert']:
return (self.text.shape[2], self.audio.shape[1], self.vision.shape[1])
else:
return (self.text.shape[1], self.audio.shape[1], self.vision.shape[1])
def get_feature_dim(self):
return self.text.shape[2], self.audio.shape[2], self.vision.shape[2]
def __getitem__(self, index):
sample = {
'raw_text': self.raw_text[index],
'text': torch.Tensor(self.text[index]),
'audio': torch.Tensor(self.audio[index]),
'vision': torch.Tensor(self.vision[index]),
'index': index,
'id': self.ids[index],
'labels': {k: torch.Tensor(v[index].reshape(-1)) for k, v in self.labels.items()}
}
if not self.args['need_data_aligned']:
sample['audio_lengths'] = self.audio_lengths[index]
sample['vision_lengths'] = self.vision_lengths[index]
if self.args.get('data_missing'):
sample['text_m'] = torch.Tensor(self.text_m[index])
sample['text_missing_mask'] = torch.Tensor(self.text_missing_mask[index])
sample['audio_m'] = torch.Tensor(self.audio_m[index])
sample['audio_lengths'] = self.audio_lengths[index]
sample['audio_mask'] = self.audio_mask[index]
sample['audio_missing_mask'] = torch.Tensor(self.audio_missing_mask[index])
sample['vision_m'] = torch.Tensor(self.vision_m[index])
sample['vision_lengths'] = self.vision_lengths[index]
sample['vision_mask'] = self.vision_mask[index]
sample['vision_missing_mask'] = torch.Tensor(self.vision_missing_mask[index])
return sample
def MMDataLoader(args, num_workers):
datasets = {
'train': MMDataset(args, mode='train'),
'valid': MMDataset(args, mode='valid'),
'test': MMDataset(args, mode='test')
}
if 'seq_lens' in args:
args['seq_lens'] = datasets['train'].get_seq_len()
dataLoader = {
ds: DataLoader(datasets[ds],
batch_size=args['batch_size'],
num_workers=num_workers,
shuffle=True)
for ds in datasets.keys()
}
return dataLoader