def __getitem__(self, idx):
if len(self.video_clips) == 0:
self.load_df()
video, audio, info = self.video_clips.get_clip(idx)
text = self.text_list[idx]
actual_idx = self.ids_list[idx]
label = [
self.class_to_idx[class_name] for class_name in self.labels[idx]
]
one_hot_label = torch.zeros(len(self.class_to_idx))
one_hot_label[label] = 1
if self.video_processor is not None:
video = self.video_processor(video)
if self.audio_processor is not None:
audio = self.audio_processor(audio)
sample = Sample()
sample.id = object_to_byte_tensor(actual_idx)
sample.video = video
sample.audio = audio
sample.update(self.text_processor({"text": text}))
sample.targets = one_hot_label
return sample
def create_sample(video_transfomred, audio_transfomred, text_tensor,
video_label):
label = [self.class_to_idx[l] for l in video_label]
one_hot_label = torch.zeros(len(self.class_to_idx))
one_hot_label[label] = 1
current_sample = Sample()
current_sample.video = video_transfomred
current_sample.audio = audio_transfomred
current_sample.update(text_tensor)
current_sample.targets = one_hot_label
current_sample.dataset_type = 'test'
current_sample.dataset_name = 'charades'
return SampleList([current_sample]).to(self.device)
