def get_test_set(opt, spatial_transform, temporal_transform, target_transform):
assert opt.dataset in ['jester', 'egogesture', 'nv', 'sc', 'ems']
assert opt.test_subset in ['val', 'test']
if opt.test_subset == 'val':
subset = 'validation'
else:
subset = 'testing'
if opt.dataset == 'ems':
test_data = EMS(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 == 'sc':
test_data = Jester(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 == 'jester':
test_data = Jester(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 == '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 == 'nv':
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_validation_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['jester', 'egogesture', 'nv']
if opt.dataset == 'jester':
validation_data = Jester(opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'egogesture':
validation_data = EgoGesture(opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'nv':
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', 'nvgesture']
assert opt.test_subset in ['val', 'test']
if opt.test_subset == 'val':
subset = 'validation'
else:
subset = 'testing'
if 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 == 'nvgesture':
test_data = NV(opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
else:
raise ValueError("Given dataset not available")
return test_data
def get_validation_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['kinetics', 'jester', 'ucf101']
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)
return validation_data
def get_training_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['kinetics', 'jester', 'ucf101']
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)
return training_data
def get_validation_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['KSL', 'jester', 'SLR', 'KETI']
if opt.dataset == 'KSL':
validation_data = KSL(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 == 'KETI':
validation_data = KETI(opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
return validation_data
def get_test_set(opt, spatial_transform, temporal_transform, target_transform):
assert opt.dataset in ['kinetics', 'activitynet', 'ucf101', 'hmdb51']
assert opt.test_subset in ['val', 'test']
if opt.test_subset == 'val':
subset = 'val'
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 == 'activitynet':
test_data = ActivityNet(opt.video_path,
opt.annotation_path,
subset,
True,
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 == 'hmdb51':
test_data = HMDB51(opt.video_path,
opt.annotation_path,
subset,
-1,
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)
return test_data
def get_validation_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['kinetics', 'activitynet', 'ucf101', 'hmdb51', '20bn-jester']
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 == 'activitynet':
validation_data = ActivityNet(
opt.video_path,
opt.annotation_path,
'validation',
False,
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 == 'hmdb51':
validation_data = HMDB51(
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 == '20bn-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)
return validation_data
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_training_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['kinetics', 'activitynet', 'ucf101', 'hmdb51', '20bn-jester']
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)
elif opt.dataset == 'activitynet':
training_data = ActivityNet(
opt.video_path,
opt.annotation_path,
'training',
False,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform)
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)
elif opt.dataset == 'hmdb51':
training_data = HMDB51(
opt.video_path,
opt.annotation_path,
'training',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform)
elif opt.dataset == '20bn-jester':
training_data = Jester(
opt.video_path,
opt.annotation_path,
'training',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform)
return training_data
def get_validation_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['kinetics', 'jester', 'ucf101', 'nvgesture']
if 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)
else:
raise ValueError("Given dataset not available")
return validation_data
def get_test_set(opt, spatial_transform, temporal_transform, target_transform):
assert opt.dataset in ['kinetics', 'jester', 'ucf101', 'dad']
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 == 'dad':
test_data = Dad(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)
return test_data
def get_training_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['KSL', 'jester', 'SLR', 'KETI']
if opt.dataset == 'KSL':
training_data = KSL(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 == 'SLR':
training_data = SLR(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 == 'KETI':
training_data = KETI(opt.video_path,
opt.annotation_path,
'training',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
return training_data
def get_validation_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['jester', 'SHGD']
if opt.dataset == 'jester':
validation_data = Jester(opt.video_path,
opt.annotation_path,
opt.val_list,
opt.modality,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
else:
validation_data = SHGD(opt.video_path,
opt.annotation_path,
opt.val_list,
opt.modality,
spatial_transform,
temporal_transform,
target_transform,
sample_duration=opt.sample_duration)
return validation_data
def get_training_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['kinetics', 'jester', 'ucf101', 'nvgesture']
if 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 == 'nvgesture':
training_data = NV(opt.video_path,
opt.annotation_path,
'training',
n_samples_for_each_video=1,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
else:
raise ValueError("Given dataset not available")
return training_data
def get_training_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['jester', 'nv', 'ipn']
if opt.train_validate:
subset = ['training', 'validation']
else:
subset = 'training'
if opt.dataset == 'jester':
training_data = Jester(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 == '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 == 'nv':
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)
elif opt.dataset == 'ipn':
training_data = IPN(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 == 'denso':
training_data = Denso(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,
no_subject_crop=opt.no_scrop)
elif opt.dataset == 'AHG':
training_data = AHG(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,
no_subject_crop=opt.no_scrop)
return training_data
def get_validation_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['jester', 'nv', 'ipn']
if opt.dataset == 'jester':
validation_data = Jester(opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'egogesture':
validation_data = EgoGesture(opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'nv':
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)
elif opt.dataset == 'ipn':
validation_data = IPN(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)
elif opt.dataset == 'denso':
validation_data = Denso(opt.video_path,
opt.annotation_path,
'validation',
true_valid=opt.true_valid,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality,
no_subject_crop=opt.no_scrop)
elif opt.dataset == 'AHG':
validation_data = AHG(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,
no_subject_crop=opt.no_scrop)
return validation_data
def get_test_set(opt, spatial_transform, temporal_transform, target_transform):
assert opt.dataset in ['jester', 'nv', 'ipn']
assert opt.test_subset in ['val', 'test']
if opt.test_subset == 'val':
subset = 'validation'
else:
subset = 'testing'
if opt.dataset == 'jester':
test_data = Jester(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 == '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 == 'nv':
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)
elif opt.dataset == 'ipn':
test_data = IPN(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,
use_preprocessing=opt.use_preprocessing)
elif opt.dataset == 'denso':
test_data = Denso(opt.video_path,
opt.annotation_path,
subset,
true_valid=opt.true_valid,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality,
no_subject_crop=opt.no_scrop)
elif opt.dataset == 'AHG':
test_data = AHG(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,
no_subject_crop=opt.no_scrop)
return test_data
def get_test_set(opt,
spatial_transform,
temporal_transform,
target_transform,
spatio_temporal_transform=None):
assert opt.dataset in [
'kinetics',
'activitynet',
'ucf101',
'hmdb51',
'kth',
'kth2',
'sth',
'sth_init',
'gtea',
'jester',
'ucf50',
'ucf50_color',
'real',
]
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 == 'activitynet':
test_data = ActivityNet(opt.video_path,
opt.annotation_path,
subset,
True,
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,
10,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'hmdb51':
test_data = HMDB51(opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'kth':
test_data = KTH(opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'kth2':
test_data = KTH2(opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'sth':
test_data = Something2(
opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_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=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'sth_init':
test_data = Something2Init(
opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'gtea':
test_data = GTEA(opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
test_split=opt.test_split)
elif opt.dataset == 'ucf50':
test_data = UCF50(opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
test_split=opt.test_split)
elif opt.dataset == 'ucf50_color':
test_data = UCF50(opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
test_split=opt.test_split)
elif opt.dataset == 'real':
test_data = REAL(opt.video_path,
opt.annotation_path,
subset,
0,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
return test_data
def get_validation_set(opt,
spatial_transform,
temporal_transform,
target_transform,
spatio_temporal_transform=None):
assert opt.dataset in [
'kinetics',
'activitynet',
'ucf101',
'hmdb51',
'kth',
'kth2',
'sth',
'sth_init',
'gtea',
'jester',
'ucf50',
'ucf50_color',
]
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 == 'activitynet':
validation_data = ActivityNet(opt.video_path,
opt.annotation_path,
'validation',
False,
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=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'hmdb51':
validation_data = HMDB51(
opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'kth':
validation_data = KTH(
opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'kth2':
validation_data = KTH2(
opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration)
elif opt.dataset == 'sth':
validation_data = Something2(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform)
elif opt.dataset == 'jester':
validation_data = Jester(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform)
elif opt.dataset == 'sth_init':
validation_data = Something2Init(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform)
elif opt.dataset == 'gtea':
validation_data = GTEA(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
test_split=opt.test_split)
elif opt.dataset == 'ucf50':
validation_data = UCF50(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
test_split=opt.test_split)
elif opt.dataset == 'ucf50_color':
validation_data = UCF50(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
spatio_temporal_transform=spatio_temporal_transform,
target_transform=target_transform,
test_split=opt.test_split)
return validation_data
