def setUp(self):
train_dataset_path = VmafConfig.test_resource_path(
'test_image_dataset_diffdim.py')
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.h5py_filepath = VmafConfig.workdir_path('test.hdf5')
self.h5py_file = DisYUVRawVideoExtractor.open_h5py_file(
self.h5py_filepath)
optional_dict2 = {'h5py_file': self.h5py_file}
_, self.features = run_executors_in_parallel(
DisYUVRawVideoExtractor,
train_assets,
fifo_mode=True,
delete_workdir=True,
parallelize=
False, # CAN ONLY USE SERIAL MODE FOR DisYRawVideoExtractor
result_store=None,
optional_dict=None,
optional_dict2=optional_dict2,
)
self.model_filename = VmafConfig.workspace_path(
"model", "test_save_load.pkl")
def test_run_dis_yuv_raw_video_extractor_parallel(self):
print 'test on running dis YUV raw video extractor...'
ref_path = VmafConfig.test_resource_path("yuv", "src01_hrc00_576x324.yuv")
dis_path = VmafConfig.test_resource_path("yuv", "src01_hrc01_576x324.yuv")
asset = Asset(dataset="test", content_id=0, asset_id=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324})
asset_original = Asset(dataset="test", content_id=0, asset_id=2,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324})
h5py_file = DisYUVRawVideoExtractor.open_h5py_file(self.h5py_filepath)
self.fextractor = DisYUVRawVideoExtractor(
[asset, asset_original], None, fifo_mode=False,
optional_dict={'channels': 'yu'},
optional_dict2={'h5py_file': h5py_file}
)
with self.assertRaises(AssertionError):
self.fextractor.run(parallelize=True)
DisYUVRawVideoExtractor.close_h5py_file(h5py_file)
def test_run_parallel_dis_y_fextractor(self):
print 'test on running dis YUV raw video extractor in parallel (disabled)...'
ref_path = VmafConfig.test_resource_path("yuv",
"src01_hrc00_576x324.yuv")
dis_path = VmafConfig.test_resource_path("yuv",
"src01_hrc01_576x324.yuv")
asset = Asset(dataset="test",
content_id=0,
asset_id=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={
'width': 576,
'height': 324
})
asset_original = Asset(dataset="test",
content_id=0,
asset_id=2,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324
})
h5py_file = DisYUVRawVideoExtractor.open_h5py_file(self.h5py_filepath)
optional_dict2 = {'h5py_file': h5py_file}
fextractor = DisYUVRawVideoExtractor([asset, asset_original],
None,
fifo_mode=True,
delete_workdir=True,
result_store=None,
optional_dict={'channels': 'yu'},
optional_dict2=optional_dict2)
self.fextractors = [fextractor]
fextractor.run(parallelize=False
) # Can't run parallel: can't pickle FileID objects
results = fextractor.results
self.assertAlmostEqual(np.mean(results[0]['dis_y']),
61.332006579182384,
places=4)
self.assertAlmostEquals(np.mean(results[1]['dis_y']),
59.788567297525148,
places=4)
self.assertAlmostEqual(np.mean(results[0]['dis_u']),
115.23227407335962,
places=4)
self.assertAlmostEquals(np.mean(results[1]['dis_u']),
114.49701717535437,
places=4)
with self.assertRaises(KeyError):
np.mean(results[0]['dis_v'])
DisYUVRawVideoExtractor.close_h5py_file(h5py_file)
def test_run_dis_yuv_raw_video_extractor_parallel(self):
print 'test on running dis YUV raw video extractor...'
ref_path = VmafConfig.test_resource_path("yuv", "src01_hrc00_576x324.yuv")
dis_path = VmafConfig.test_resource_path("yuv", "src01_hrc01_576x324.yuv")
asset = Asset(dataset="test", content_id=0, asset_id=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324})
asset_original = Asset(dataset="test", content_id=0, asset_id=2,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324})
h5py_file = DisYUVRawVideoExtractor.open_h5py_file(self.h5py_filepath)
self.fextractor = DisYUVRawVideoExtractor(
[asset, asset_original], None, fifo_mode=False,
optional_dict={'channels': 'yu'},
optional_dict2={'h5py_file': h5py_file}
)
with self.assertRaises(AssertionError):
self.fextractor.run(parallelize=True)
DisYUVRawVideoExtractor.close_h5py_file(h5py_file)
def test_run_dis_yuv_raw_video_extractor(self):
print('test on running dis YUV raw video extractor...')
ref_path = VmafConfig.test_resource_path("yuv",
"src01_hrc00_576x324.yuv")
dis_path = VmafConfig.test_resource_path("yuv",
"src01_hrc01_576x324.yuv")
asset = Asset(dataset="test",
content_id=0,
asset_id=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={
'width': 576,
'height': 324
})
asset_original = Asset(dataset="test",
content_id=0,
asset_id=2,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324
})
h5py_file = DisYUVRawVideoExtractor.open_h5py_file(self.h5py_filepath)
self.fextractor = DisYUVRawVideoExtractor(
[asset, asset_original],
None,
fifo_mode=False,
optional_dict={'channels': 'yu'},
optional_dict2={'h5py_file': h5py_file})
self.fextractor.run()
results = self.fextractor.results
self.assertAlmostEqual(np.mean(results[0]['dis_y']),
61.332006579182384,
places=4)
self.assertAlmostEqual(np.mean(results[1]['dis_y']),
59.788567297525148,
places=4)
self.assertAlmostEqual(np.mean(results[0]['dis_u']),
115.23227407335962,
places=4)
self.assertAlmostEqual(np.mean(results[1]['dis_u']),
114.49701717535437,
places=4)
with self.assertRaises(KeyError):
np.mean(results[0]['dis_v'])
DisYUVRawVideoExtractor.close_h5py_file(h5py_file)
def setUp(self):
train_dataset_path = VmafConfig.test_resource_path("test_image_dataset_diffdim.py")
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.h5py_filepath = VmafConfig.workdir_path('test.hdf5')
self.h5py_file = DisYUVRawVideoExtractor.open_h5py_file(self.h5py_filepath)
optional_dict2 = {'h5py_file': self.h5py_file}
fextractor = DisYUVRawVideoExtractor(
train_assets,
None,
fifo_mode=True,
delete_workdir=True,
result_store=None,
optional_dict=None,
optional_dict2=optional_dict2,
)
fextractor.run(parallelize=False) # CAN ONLY USE SERIAL MODE FOR DisYRawVideoExtractor
self.features = fextractor.results
def setUp(self):
train_dataset_path = VmafConfig.test_resource_path("test_image_dataset_diffdim.py")
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.h5py_filepath = VmafConfig.workdir_path('test.hdf5')
self.h5py_file = DisYUVRawVideoExtractor.open_h5py_file(self.h5py_filepath)
optional_dict2 = {'h5py_file': self.h5py_file}
fextractor = DisYUVRawVideoExtractor(
train_assets,
None,
fifo_mode=True,
delete_workdir=True,
result_store=None,
optional_dict=None,
optional_dict2=optional_dict2,
)
fextractor.run(parallelize=False) # CAN ONLY USE SERIAL MODE FOR DisYRawVideoExtractor
self.features = fextractor.results
def test_run_parallel_dis_y_fextractor(self):
print 'test on running dis YUV raw video extractor in parallel (disabled)...'
ref_path = VmafConfig.test_resource_path("yuv", "src01_hrc00_576x324.yuv")
dis_path = VmafConfig.test_resource_path("yuv", "src01_hrc01_576x324.yuv")
asset = Asset(dataset="test", content_id=0, asset_id=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324})
asset_original = Asset(dataset="test", content_id=0, asset_id=2,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324})
h5py_file = DisYUVRawVideoExtractor.open_h5py_file(self.h5py_filepath)
optional_dict2 = {'h5py_file': h5py_file}
fextractor = DisYUVRawVideoExtractor(
[asset, asset_original],
None,
fifo_mode=True,
delete_workdir=True,
result_store=None,
optional_dict={'channels': 'yu'},
optional_dict2=optional_dict2
)
self.fextractors = [fextractor]
fextractor.run(parallelize=False) # Can't run parallel: can't pickle FileID objects
results = fextractor.results
self.assertAlmostEqual(np.mean(results[0]['dis_y']), 61.332006579182384, places=4)
self.assertAlmostEquals(np.mean(results[1]['dis_y']), 59.788567297525148, places=4)
self.assertAlmostEqual(np.mean(results[0]['dis_u']), 115.23227407335962, places=4)
self.assertAlmostEquals(np.mean(results[1]['dis_u']), 114.49701717535437, places=4)
with self.assertRaises(KeyError):
np.mean(results[0]['dis_v'])
DisYUVRawVideoExtractor.close_h5py_file(h5py_file)
def setUp(self):
train_dataset_path = config.ROOT + '/python/test/resource/test_image_dataset_diffdim.py'
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.h5py_filepath = config.ROOT + '/workspace/workdir/test.hdf5'
self.h5py_file = DisYUVRawVideoExtractor.open_h5py_file(
self.h5py_filepath)
optional_dict2 = {'h5py_file': self.h5py_file}
_, self.features = run_executors_in_parallel(
DisYUVRawVideoExtractor,
train_assets,
fifo_mode=True,
delete_workdir=True,
parallelize=
False, # CAN ONLY USE SERIAL MODE FOR DisYRawVideoExtractor
result_store=None,
optional_dict=None,
optional_dict2=optional_dict2,
)
num_test = 50
n_epochs = 30
seed = 0 # None
# read input dataset
dataset_path = config.ROOT + '/resource/dataset/BSDS500_noisy_dataset.py'
dataset = import_python_file(dataset_path)
assets = read_dataset(dataset)
# shuffle assets
np.random.seed(seed)
np.random.shuffle(assets)
assets = assets[:(num_train + num_test)]
raw_video_h5py_filepath = config.ROOT + '/workspace/workdir/rawvideo.hdf5'
raw_video_h5py_file = DisYUVRawVideoExtractor.open_h5py_file(raw_video_h5py_filepath)
print '======================== Extract raw YUVs =============================='
_, raw_yuvs = run_executors_in_parallel(
DisYUVRawVideoExtractor,
assets,
fifo_mode=True,
delete_workdir=True,
parallelize=False, # CAN ONLY USE SERIAL MODE FOR DisYRawVideoExtractor
result_store=None,
optional_dict=None,
optional_dict2={'h5py_file': raw_video_h5py_file})
patch_h5py_filepath = config.ROOT + '/workspace/workdir/patch.hdf5'
patch_h5py_file = ToddNoiseClassifierTrainTestModel.open_h5py_file(patch_h5py_filepath)
def main():
# parameters
num_train = 500
num_test = 50
n_epochs = 30
seed = 0 # None
# read input dataset
dataset_path = VmafConfig.resource_path('dataset',
'BSDS500_noisy_dataset.py')
dataset = import_python_file(dataset_path)
assets = read_dataset(dataset)
# shuffle assets
np.random.seed(seed)
np.random.shuffle(assets)
assets = assets[:(num_train + num_test)]
raw_video_h5py_filepath = VmafConfig.workdir_path('rawvideo.hdf5')
raw_video_h5py_file = DisYUVRawVideoExtractor.open_h5py_file(
raw_video_h5py_filepath)
print(
'======================== Extract raw YUVs =============================='
)
_, raw_yuvs = run_executors_in_parallel(
DisYUVRawVideoExtractor,
assets,
fifo_mode=True,
delete_workdir=True,
parallelize=False, # CAN ONLY USE SERIAL MODE FOR DisYRawVideoExtractor
result_store=None,
optional_dict=None,
optional_dict2={'h5py_file': raw_video_h5py_file})
patch_h5py_filepath = VmafConfig.workdir_path('patch.hdf5')
patch_h5py_file = ToddNoiseClassifierTrainTestModel.open_h5py_file(
patch_h5py_filepath)
model = ToddNoiseClassifierTrainTestModel(
param_dict={
'seed': seed,
'n_epochs': n_epochs,
},
logger=None,
optional_dict2={ # for options that won't impact the result
# 'checkpoints_dir': VmafConfig.workspace_path('checkpoints_dir'),
'h5py_file': patch_h5py_file,
})
print(
'============================ Train model ==============================='
)
xys = ToddNoiseClassifierTrainTestModel.get_xys_from_results(
raw_yuvs[:num_train])
model.train(xys)
print(
'=========================== Evaluate model ============================='
)
xs = ToddNoiseClassifierTrainTestModel.get_xs_from_results(
raw_yuvs[num_train:])
ys = ToddNoiseClassifierTrainTestModel.get_ys_from_results(
raw_yuvs[num_train:])
result = model.evaluate(xs, ys)
print("")
print("f1 test %g, errorrate test %g" %
(result['f1'], result['errorrate']))
# tear down
DisYUVRawVideoExtractor.close_h5py_file(raw_video_h5py_file)
ToddNoiseClassifierTrainTestModel.close_h5py_file(patch_h5py_file)
os.remove(raw_video_h5py_filepath)
os.remove(patch_h5py_filepath)
print('Done.')
num_test = 50
n_epochs = 30
seed = 0 # None
# read input dataset
dataset_path = VmafConfig.resource_path('dataset', 'BSDS500_noisy_dataset.py')
dataset = import_python_file(dataset_path)
assets = read_dataset(dataset)
# shuffle assets
np.random.seed(seed)
np.random.shuffle(assets)
assets = assets[:(num_train + num_test)]
raw_video_h5py_filepath = VmafConfig.workdir_path('rawvideo.hdf5')
raw_video_h5py_file = DisYUVRawVideoExtractor.open_h5py_file(raw_video_h5py_filepath)
print '======================== Extract raw YUVs =============================='
_, raw_yuvs = run_executors_in_parallel(
DisYUVRawVideoExtractor,
assets,
fifo_mode=True,
delete_workdir=True,
parallelize=False, # CAN ONLY USE SERIAL MODE FOR DisYRawVideoExtractor
result_store=None,
optional_dict=None,
optional_dict2={'h5py_file': raw_video_h5py_file})
patch_h5py_filepath = VmafConfig.workdir_path('patch.hdf5')
patch_h5py_file = ToddNoiseClassifierTrainTestModel.open_h5py_file(patch_h5py_filepath)
