/
dataset.py
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/
dataset.py
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from torchvision import get_image_backend
from datasets.videodataset import VideoDataset
from datasets.videodataset_multiclips import (VideoDatasetMultiClips,
collate_fn)
from datasets.activitynet import ActivityNet
from datasets.loader import VideoLoader, VideoLoaderHDF5, VideoLoaderFlowHDF5
import pdb
def image_name_formatter(x):
return 'image_{:05d}.jpg'.format(x)
def sthv2_image_name_formatter(x):
return '{:06d}.jpg'.format(x)
def sthv1_image_name_formatter(x):
return '{:05d}.jpg'.format(x)
def get_training_data(video_path,
annotation_path,
dataset_name,
input_type,
file_type,
spatial_transform=None,
temporal_transform=None,
target_transform=None):
assert dataset_name in [
'kinetics', 'activitynet', 'ucf101', 'hmdb51', 'mit', 'somethingv2',
'somethingv1'
]
assert input_type in ['rgb', 'flow']
assert file_type in ['jpg', 'hdf5']
if 'somethingv1' in dataset_name:
formatter = sthv1_image_name_formatter
elif 'somethingv2' in dataset_name:
formatter = sthv2_image_name_formatter
else:
formatter = image_name_formatter
if file_type == 'jpg':
assert input_type == 'rgb', 'flow input is supported only when input type is hdf5.'
if get_image_backend() == 'accimage':
from datasets.loader import ImageLoaderAccImage
loader = VideoLoader(formatter, ImageLoaderAccImage())
else:
loader = VideoLoader(formatter)
video_path_formatter = (
lambda root_path, label, video_id: root_path / label / video_id)
else:
if input_type == 'rgb':
loader = VideoLoaderHDF5()
else:
loader = VideoLoaderFlowHDF5()
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / '{}.hdf5'.format(video_id))
if dataset_name == 'activitynet':
training_data = ActivityNet(video_path,
annotation_path,
'training',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter)
else:
training_data = VideoDataset(video_path,
annotation_path,
'training',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter)
return training_data
def get_validation_data(video_path,
annotation_path,
dataset_name,
input_type,
file_type,
spatial_transform=None,
temporal_transform=None,
target_transform=None):
assert dataset_name in [
'kinetics', 'activitynet', 'ucf101', 'hmdb51', 'mit', 'somethingv2',
'somethingv1'
]
assert input_type in ['rgb', 'flow']
assert file_type in ['jpg', 'hdf5']
if 'somethingv1' in dataset_name:
formatter = sthv1_image_name_formatter
elif 'somethingv2' in dataset_name:
formatter = sthv2_image_name_formatter
else:
formatter = image_name_formatter
if file_type == 'jpg':
assert input_type == 'rgb', 'flow input is supported only when input type is hdf5.'
if get_image_backend() == 'accimage':
from datasets.loader import ImageLoaderAccImage
loader = VideoLoader(formatter, ImageLoaderAccImage())
else:
loader = VideoLoader(formatter)
video_path_formatter = (
lambda root_path, label, video_id: root_path / label / video_id)
else:
if input_type == 'rgb':
loader = VideoLoaderHDF5()
else:
loader = VideoLoaderFlowHDF5()
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / '{}.hdf5'.format(video_id))
if dataset_name == 'activitynet':
validation_data = ActivityNet(video_path,
annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter)
else:
validation_data = VideoDatasetMultiClips(
video_path,
annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter)
return validation_data, collate_fn
def get_inference_data(video_path,
annotation_path,
dataset_name,
input_type,
file_type,
inference_subset,
spatial_transform=None,
temporal_transform=None,
target_transform=None):
assert dataset_name in [
'kinetics', 'activitynet', 'ucf101', 'hmdb51', 'mit', 'somethingv2'
]
assert input_type in ['rgb', 'flow']
assert file_type in ['jpg', 'hdf5']
assert inference_subset in ['train', 'val', 'test']
if file_type == 'jpg':
assert input_type == 'rgb', 'flow input is supported only when input type is hdf5.'
if get_image_backend() == 'accimage':
from datasets.loader import ImageLoaderAccImage
loader = VideoLoader(image_name_formatter, ImageLoaderAccImage())
else:
loader = VideoLoader(image_name_formatter)
video_path_formatter = (
lambda root_path, label, video_id: root_path / label / video_id)
else:
if input_type == 'rgb':
loader = VideoLoaderHDF5()
else:
loader = VideoLoaderFlowHDF5()
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / '{}.hdf5'.format(video_id))
if inference_subset == 'train':
subset = 'training'
elif inference_subset == 'val':
subset = 'validation'
elif inference_subset == 'test':
subset = 'testing'
if dataset_name == 'activitynet':
inference_data = ActivityNet(video_path,
annotation_path,
subset,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter,
is_untrimmed_setting=True)
else:
inference_data = VideoDatasetMultiClips(
video_path,
annotation_path,
subset,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter,
target_type=['video_id', 'segment'])
return inference_data, collate_fn