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minY_dataloader.py
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minY_dataloader.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import torch as th
from torch.utils.data import Dataset
import pickle
import torch.nn.functional as F
import numpy as np
import re
from torch.utils.data.dataloader import default_collate
class Youcook_DataLoader(Dataset):
"""Youcook dataset loader."""
def __init__(
self,
data,
we,
we_dim=300,
max_words=30,
num_frames_multiplier=5,
tri_modal=False,
):
"""
Args:
"""
self.data = pickle.load(open(data, 'rb'))
self.we = we
self.we_dim = we_dim
self.max_words = max_words
self.num_frames_multiplier = num_frames_multiplier
self.tri_modal = tri_modal
def __len__(self):
return len(self.data)
def custom_collate(self, batch):
return default_collate(batch)
def _zero_pad_tensor(self, tensor, size):
if len(tensor) >= size:
return tensor[:size]
else:
zero = np.zeros((size - len(tensor), self.we_dim), dtype=np.float32)
return np.concatenate((tensor, zero), axis=0)
def _tokenize_text(self, sentence):
w = re.findall(r"[\w']+", str(sentence))
return w
def _words_to_we(self, words):
words = [word for word in words if word in self.we.vocab]
if words:
we = self._zero_pad_tensor(self.we[words], self.max_words)
return th.from_numpy(we)
else:
return th.zeros(self.max_words, self.we_dim)
def __getitem__(self, idx):
# load 2d and 3d features (features are pooled over the time dimension)
feat_2d = F.normalize(th.from_numpy(self.data[idx]['2d']).float(), dim=0)
feat_3d = F.normalize(th.from_numpy(self.data[idx]['3d']).float(), dim=0)
video = th.cat((feat_2d, feat_3d))
# load audio and zero pad/truncate if necessary
audio = self.data[idx]['audio']
target_length = 1024 * self.num_frames_multiplier
nframes = audio.numpy().shape[1]
p = target_length - nframes
if p > 0:
audio = np.pad(audio, ((0,0),(0,p)), 'constant', constant_values=(0,0))
elif p < 0:
audio = audio[:,0:p]
audio = th.FloatTensor(audio)
caption = ''
if self.tri_modal:
caption = self._words_to_we(self._tokenize_text(self.data[idx]['caption']))
return {'video': video, 'text': caption, 'video_id': self.data[idx]['id'],
'audio': audio, 'nframes': nframes}
class Youcook_DataLoader_label(Dataset):
"""Youcook dataset loader."""
def __init__(
self,
data,
we,
pseudo_v,
pseudo_a,
we_dim=300,
max_words=30,
num_frames_multiplier=5,
tri_modal=False,
):
"""
Args:
"""
self.data = pickle.load(open(data, 'rb')) #9000*4800
self.we = we
self.we_dim = we_dim
self.max_words = max_words
self.num_frames_multiplier = num_frames_multiplier
self.tri_modal = tri_modal
self.pseudo_v = pseudo_v
self.pseudo_a = pseudo_a
def __len__(self):
return len(self.data)
def custom_collate(self, batch):
return default_collate(batch)
def _zero_pad_tensor(self, tensor, size):
if len(tensor) >= size:
return tensor[:size]
else:
zero = np.zeros((size - len(tensor), self.we_dim), dtype=np.float32)
return np.concatenate((tensor, zero), axis=0)
def _tokenize_text(self, sentence):
w = re.findall(r"[\w']+", str(sentence))
return w
def _words_to_we(self, words):
words = [word for word in words if word in self.we.vocab]
if words:
we = self._zero_pad_tensor(self.we[words], self.max_words)
return th.from_numpy(we)
else:
return th.zeros(self.max_words, self.we_dim)
def __getitem__(self, idx):
# load 2d and 3d features (features are pooled over the time dimension)
feat_2d = F.normalize(th.from_numpy(self.data[idx]['2d']).float(), dim=0)
feat_3d = F.normalize(th.from_numpy(self.data[idx]['3d']).float(), dim=0)
video = th.cat((feat_2d, feat_3d))
# load audio and zero pad/truncate if necessary
audio = self.data[idx]['audio']
target_length = 1024 * self.num_frames_multiplier
nframes = audio.numpy().shape[1]
p = target_length - nframes
if p > 0:
audio = np.pad(audio, ((0, 0), (0, p)), 'constant', constant_values=(0, 0))
elif p < 0:
audio = audio[:, 0:p]
audio = th.FloatTensor(audio)
caption = ''
if self.tri_modal:
caption = self._words_to_we(self._tokenize_text(self.data[idx]['caption']))
return {'video': video, 'text': caption, 'video_id': self.data[idx]['id'],
'audio': audio, 'nframes': nframes, 'pseudo_v':self.pseudo_v[idx], 'pseudo_a':self.pseudo_a[idx]}
class Youcook_DataLoader_knn(Dataset):
"""Youcook dataset loader."""
def __init__(
self,
data,
we,
knn_v,
knn_a,
we_dim=300,
max_words=30,
num_frames_multiplier=5,
tri_modal=False,
):
"""
Args:
"""
self.data = pickle.load(open(data, 'rb')) #9000*4800
self.we = we
self.we_dim = we_dim
self.max_words = max_words
self.num_frames_multiplier = num_frames_multiplier
self.tri_modal = tri_modal
self.knn_v = knn_v
self.knn_a = knn_a
def __len__(self):
return len(self.data)
def custom_collate(self, batch):
return default_collate(batch)
def _zero_pad_tensor(self, tensor, size):
if len(tensor) >= size:
return tensor[:size]
else:
zero = np.zeros((size - len(tensor), self.we_dim), dtype=np.float32)
return np.concatenate((tensor, zero), axis=0)
def _tokenize_text(self, sentence):
w = re.findall(r"[\w']+", str(sentence))
return w
def _words_to_we(self, words):
words = [word for word in words if word in self.we.vocab]
if words:
we = self._zero_pad_tensor(self.we[words], self.max_words)
return th.from_numpy(we)
else:
return th.zeros(self.max_words, self.we_dim)
def __getitem__(self, idx):
video_feature = []
text_feature = []
audio_feature = []
nframes_list = []
caption_text = []
for i in self.knn_v[idx]:
# load 2d and 3d features (features are pooled over the time dimension)
feat_2d = F.normalize(th.from_numpy(self.data[i]['2d']).float(), dim=0)
feat_3d = F.normalize(th.from_numpy(self.data[i]['3d']).float(), dim=0)
video = th.cat((feat_2d, feat_3d))
video_feature.append(video.numpy())
# load audio and zero pad/truncate if necessary
audio = self.data[i]['audio']
target_length = 1024 * self.num_frames_multiplier
nframes = audio.numpy().shape[1]
nframes_list.append(nframes)
p = target_length - nframes
if p > 0:
audio = np.pad(audio, ((0, 0), (0, p)), 'constant', constant_values=(0, 0))
elif p < 0:
audio = audio[:, 0:p]
audio = th.FloatTensor(audio)
audio_feature.append(audio.numpy())
caption = ''
if self.tri_modal:
caption = self._words_to_we(self._tokenize_text(self.data[i]['caption']))
text_feature.append(caption.numpy())
video_f = np.asarray(video_feature)
text_f = np.asarray(text_feature)
audio_f = np.asarray(audio_feature)
nframes_l = np.asarray(nframes_list)
"""
print('dataload')
print(video_f.shape)
print(text_f.shape)
print(audio_f.shape)
print(nframes_l.shape)
print('dataload_fin')
"""
#caption_text =
return {'video': video_f, 'text': text_f, 'video_id': self.data[i]['id'],
'audio': audio_f, 'nframes': nframes_l}