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dataset.py
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dataset.py
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from datasets.kinetics import Kinetics
from datasets.ucf101 import UCF101
from datasets.jester import Jester
from datasets.egogesture import EgoGesture
from datasets.nv import NV
from datasets.egogesture_online import EgoGestureOnline
from datasets.nv_online import NVOnline
def get_training_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['kinetics', 'jester', 'ucf101', 'egogesture', 'nvgesture']
if opt.train_validate:
subset = ['training', 'validation']
else:
subset = 'training'
if opt.dataset == 'kinetics':
training_data = Kinetics(
opt.video_path,
opt.annotation_path,
'training',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'jester':
training_data = Jester(
opt.video_path,
opt.annotation_path,
'training',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'ucf101':
training_data = UCF101(
opt.video_path,
opt.annotation_path,
'training',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'egogesture':
training_data = EgoGesture(
opt.video_path,
opt.annotation_path,
subset,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality)
elif opt.dataset == 'nvgesture':
training_data = NV(
opt.video_path,
opt.annotation_path,
subset,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality)
return training_data
def get_validation_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['kinetics', 'jester', 'ucf101', 'egogesture', 'nvgesture']
if opt.dataset == 'kinetics':
validation_data = Kinetics(
opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'jester':
validation_data = Jester(
opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'ucf101':
validation_data = UCF101(
opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'egogesture':
validation_data = EgoGesture(
opt.video_path,
opt.annotation_path,
'testing',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'nvgesture':
validation_data = NV(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality)
return validation_data
def get_test_set(opt, spatial_transform, temporal_transform, target_transform):
assert opt.dataset in ['kinetics', 'jester', 'ucf101', 'egogesture', 'nvgesture']
assert opt.test_subset in ['val', 'test']
if opt.test_subset == 'val':
subset = 'validation'
elif opt.test_subset == 'test':
subset = 'testing'
if opt.dataset == 'kinetics':
test_data = Kinetics(
opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'jester':
test_data = Jester(
opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'ucf101':
test_data = UCF101(
opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'egogesture':
test_data = EgoGesture(
opt.video_path,
opt.annotation_path,
subset,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'nvgesture':
test_data = NV(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality)
return test_data
def get_online_data(opt, spatial_transform, temporal_transform, target_transform):
assert opt.dataset in [ 'egogesture', 'nvgesture']
whole_path = opt.whole_path
if opt.dataset == 'egogesture':
online_data = EgoGestureOnline(
opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality="RGB-D",
stride_len = opt.stride_len,
sample_duration=opt.sample_duration)
if opt.dataset == 'nvgesture':
online_data = NVOnline(
opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality="RGB-D",
stride_len = opt.stride_len,
sample_duration=opt.sample_duration)
return online_data