def test_train_test_on_raw_dataset_with_dis1st_thr(self):
train_dataset = import_python_file(
config.ROOT + '/python/test/resource/raw_dataset_sample.py')
model_param = import_python_file(
config.ROOT + '/python/test/resource/model_param_sample.py')
feature_param = import_python_file(
config.ROOT + '/python/test/resource/feature_param_sample.py')
train_fassembler, train_assets, train_stats, \
test_fassembler, test_assets, test_stats, _ = \
train_test_vmaf_on_dataset(
train_dataset=train_dataset, test_dataset=train_dataset,
feature_param=feature_param, model_param=model_param,
train_ax=None, test_ax=None, result_store=None,
parallelize=True,
logger=None,
fifo_mode=True,
output_model_filepath=self.output_model_filepath)
self.train_fassembler = train_fassembler
self.assertTrue(os.path.exists(self.output_model_filepath))
self.assertItemsEqual(train_stats['ys_label_pred'],
[93.565459224020742, 60.451618249440827,
93.565460383297108, 92.417462071278933])
self.assertItemsEqual(test_stats['ys_label_pred'],
[93.565459224020742, 60.451618249440827,
93.565460383297108, 92.417462071278933])
def test_train_test_on_raw_dataset_with_dis1st_thr(self):
train_dataset = import_python_file(
config.ROOT + '/python/test/resource/raw_dataset_sample.py')
model_param = import_python_file(
config.ROOT + '/python/test/resource/model_param_sample.py')
feature_param = import_python_file(
config.ROOT + '/python/test/resource/feature_param_sample.py')
train_fassembler, train_assets, train_stats, \
test_fassembler, test_assets, test_stats, _ = \
train_test_vmaf_on_dataset(
train_dataset=train_dataset, test_dataset=train_dataset,
feature_param=feature_param, model_param=model_param,
train_ax=None, test_ax=None, result_store=None,
parallelize=True,
logger=None,
fifo_mode=True,
output_model_filepath=self.output_model_filepath)
self.train_fassembler = train_fassembler
self.assertTrue(os.path.exists(self.output_model_filepath))
self.assertItemsEqual(train_stats['ys_label_pred'], [
93.565459224020742, 60.451618249440827, 93.565460383297108,
92.417462071278933
])
self.assertItemsEqual(test_stats['ys_label_pred'], [
93.565459224020742, 60.451618249440827, 93.565460383297108,
92.417462071278933
])
def test_train_test_on_dataset_with_dis1st_thr(self):
train_dataset = import_python_file(
config.ROOT + '/python/test/resource/dataset_sample.py')
model_param = import_python_file(
config.ROOT + '/python/test/resource/model_param_sample.py')
feature_param = import_python_file(
config.ROOT + '/python/test/resource/feature_param_sample.py')
train_fassembler, train_assets, train_stats, \
test_fassembler, test_assets, test_stats, _ = \
train_test_vmaf_on_dataset(
train_dataset=train_dataset, test_dataset=train_dataset,
feature_param=feature_param, model_param=model_param,
train_ax=None, test_ax=None, result_store=None,
parallelize=True,
logger=None,
fifo_mode=True,
output_model_filepath=self.output_model_filepath,
)
self.train_fassembler = train_fassembler
self.assertTrue(os.path.exists(self.output_model_filepath))
self.assertAlmostEqual(train_stats['ys_label_pred'][0],
90.753010402770798,
places=3)
self.assertAlmostEqual(test_stats['ys_label_pred'][0],
90.753010402770798,
places=3)
def test_train_test_on_dataset_with_dis1st_thr(self):
train_dataset = import_python_file(
config.ROOT + '/python/test/resource/dataset_sample.py')
model_param = import_python_file(
config.ROOT + '/python/test/resource/model_param_sample.py')
feature_param = import_python_file(
config.ROOT + '/python/test/resource/feature_param_sample.py')
train_fassembler, train_assets, train_stats, \
test_fassembler, test_assets, test_stats, _ = \
train_test_vmaf_on_dataset(
train_dataset=train_dataset, test_dataset=train_dataset,
feature_param=feature_param, model_param=model_param,
train_ax=None, test_ax=None, result_store=None,
parallelize=True,
logger=None,
fifo_mode=True,
output_model_filepath=self.output_model_filepath,
)
self.train_fassembler = train_fassembler
self.assertTrue(os.path.exists(self.output_model_filepath))
self.assertItemsEqual(train_stats['ys_label_pred'],
[90.753010402770798, 59.223801498461015,
90.753011435798058, 89.270176556597008])
self.assertItemsEqual(test_stats['ys_label_pred'],
[90.753010402770798, 59.223801498461015,
90.753011435798058, 89.270176556597008])
def run_vmaf_cv(train_dataset_filepath,
test_dataset_filepath,
param_filepath,
output_model_filepath=None,
**kwargs):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
train_dataset = import_python_file(train_dataset_filepath)
test_dataset = import_python_file(
test_dataset_filepath) if test_dataset_filepath is not None else None
param = import_python_file(param_filepath)
# === plot scatter ===
nrows = 1
ncols = 2
fig, axs = plt.subplots(figsize=(5 * ncols, 5 * nrows),
nrows=nrows,
ncols=ncols)
train_test_vmaf_on_dataset(train_dataset,
test_dataset,
param,
param,
axs[0],
axs[1],
result_store,
parallelize=True,
logger=None,
output_model_filepath=output_model_filepath,
**kwargs)
if 'xlim' in kwargs:
axs[0].set_xlim(kwargs['xlim'])
axs[1].set_xlim(kwargs['xlim'])
if 'ylim' in kwargs:
axs[0].set_ylim(kwargs['ylim'])
axs[1].set_ylim(kwargs['ylim'])
bbox = {'facecolor': 'white', 'alpha': 1, 'pad': 20}
axs[0].annotate('Training Set',
xy=(0.1, 0.85),
xycoords='axes fraction',
bbox=bbox)
axs[1].annotate('Testing Set',
xy=(0.1, 0.85),
xycoords='axes fraction',
bbox=bbox)
plt.tight_layout()
# === clean up ===
close_logger(logger)
def test_mos_subjective_model_output(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
subjective_model.run_modeling()
subjective_model.to_aggregated_dataset_file(self.output_dataset_filepath)
self.assertTrue(os.path.exists(self.output_dataset_filepath))
dataset2 = import_python_file(self.output_dataset_filepath)
dis_video = dataset2.dis_videos[0]
self.assertTrue('groundtruth' in dis_video)
self.assertTrue('os' not in dis_video)
self.assertAlmostEquals(dis_video['groundtruth'], 4.884615384615385, places=4)
def test_mos_subjective_model_output(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
subjective_model.run_modeling()
subjective_model.to_aggregated_dataset_file(self.output_dataset_filepath)
self.assertTrue(os.path.exists(self.output_dataset_filepath))
dataset2 = import_python_file(self.output_dataset_filepath)
dis_video = dataset2.dis_videos[0]
self.assertTrue('groundtruth' in dis_video)
self.assertTrue('os' not in dis_video)
self.assertAlmostEquals(dis_video['groundtruth'], 4.884615384615385, places=4)
def test_persubject_subjective_model_output(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = PerSubjectModel(dataset_reader)
subjective_model.run_modeling(transform_final={'p1':25, 'p0':-25})
subjective_model.to_aggregated_dataset_file(self.output_dataset_filepath)
self.assertTrue(os.path.exists(self.output_dataset_filepath))
dataset2 = import_python_file(self.output_dataset_filepath)
dis_video = dataset2.dis_videos[0]
self.assertTrue('groundtruth' in dis_video)
self.assertTrue('os' not in dis_video)
self.assertAlmostEquals(dis_video['groundtruth'], 100.0, places=4)
def test_mos_subjective_model_output_custom_resampling(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
subjective_model.run_modeling()
subjective_model.to_aggregated_dataset_file(self.output_dataset_filepath, resampling_type='lanczos')
self.assertTrue(os.path.exists(self.output_dataset_filepath))
dataset2 = import_python_file(self.output_dataset_filepath)
self.assertFalse(hasattr(dataset2, 'quality_height'))
self.assertFalse(hasattr(dataset2, 'quality_width'))
self.assertEquals(dataset2.resampling_type, 'lanczos')
dis_video = dataset2.dis_videos[0]
self.assertTrue('groundtruth' in dis_video)
self.assertTrue('os' not in dis_video)
self.assertAlmostEquals(dis_video['groundtruth'], 4.884615384615385, places=4)
def test_mos_subjective_model_output_custom_resampling(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
subjective_model.run_modeling()
subjective_model.to_aggregated_dataset_file(self.output_dataset_filepath, resampling_type='lanczos')
self.assertTrue(os.path.exists(self.output_dataset_filepath))
dataset2 = import_python_file(self.output_dataset_filepath)
self.assertFalse(hasattr(dataset2, 'quality_height'))
self.assertFalse(hasattr(dataset2, 'quality_width'))
self.assertEquals(dataset2.resampling_type, 'lanczos')
dis_video = dataset2.dis_videos[0]
self.assertTrue('groundtruth' in dis_video)
self.assertTrue('os' not in dis_video)
self.assertAlmostEquals(dis_video['groundtruth'], 4.884615384615385, places=4)
def setUp(self):
train_dataset_path = config.ROOT + '/python/test/resource/test_image_dataset_noisy.py'
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.raw_video_h5py_filepath = config.ROOT + '/workspace/workdir/rawvideo.hdf5'
self.raw_video_h5py_file = DisYUVRawVideoExtractor.open_h5py_file(
self.raw_video_h5py_filepath)
optional_dict2 = {'h5py_file': self.raw_video_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,
)
np.random.seed(0)
np.random.shuffle(self.features)
self.patch_h5py_filepath = config.ROOT + '/workspace/workdir/patch.hdf5'
self.model_filename = config.ROOT + "/workspace/model/test_save_load.pkl"
NeuralNetTrainTestModel.reset()
def test_test_on_dataset_mle(self):
test_dataset = import_python_file(
config.ROOT + '/python/test/resource/raw_dataset_sample.py')
test_assets, results = test_on_dataset(test_dataset,
VmafQualityRunner,
None,
None,
None,
parallelize=True,
aggregate_method=None,
subj_model_class=MosModel)
self.assertAlmostEqual(results[0]['VMAF_score'],
99.620284242680668,
places=4)
self.assertAlmostEqual(results[1]['VMAF_score'],
28.98459923031756,
places=4)
self.assertAlmostEqual(results[2]['VMAF_score'],
98.860794831997097,
places=4)
self.assertAlmostEqual(results[3]['VMAF_score'],
98.860790186334995,
places=4)
self.assertAlmostEqual(test_assets[0].groundtruth, 100, places=4)
self.assertAlmostEqual(test_assets[1].groundtruth, 50, places=4)
self.assertAlmostEqual(test_assets[2].groundtruth, 90, places=4)
self.assertAlmostEqual(test_assets[3].groundtruth, 80, places=4)
def main():
if len(sys.argv) < 3:
print_usage()
return 2
try:
quality_type = sys.argv[1]
dataset_filepath = sys.argv[2]
except ValueError:
print_usage()
return 2
try:
dataset = import_python_file(dataset_filepath)
except Exception as e:
print "Error: " + str(e)
return 1
try:
runner_class = QualityRunner.find_subclass(quality_type)
except:
print_usage()
return 2
result_store = FileSystemResultStore()
run_remove_results_for_dataset(result_store, dataset, runner_class)
return 0
def main():
if len(sys.argv) < 3:
print_usage()
return 2
try:
quality_type = sys.argv[1]
dataset_filepath = sys.argv[2]
except ValueError:
print_usage()
return 2
try:
dataset = import_python_file(dataset_filepath)
except Exception as e:
print "Error: " + str(e)
return 1
try:
runner_class = QualityRunner.find_subclass(quality_type)
except:
print_usage()
return 2
result_store = FileSystemResultStore()
run_remove_results_for_dataset(result_store, dataset, runner_class)
return 0
def run_vmaf_kfold_cv(dataset_filepath, contentid_groups, param_filepath, aggregate_method):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
dataset = import_python_file(dataset_filepath)
param = import_python_file(param_filepath)
fig, ax = plt.subplots(figsize=(5, 5), nrows=1, ncols=1)
cv_on_dataset(dataset, param, param, ax, result_store, contentid_groups, logger, aggregate_method)
ax.set_xlim([0, 120])
ax.set_ylim([0, 120])
plt.tight_layout()
# === clean up ===
close_logger(logger)
def run_vmaf_cv(train_dataset_filepath, test_dataset_filepath, param_filepath, output_model_filepath=None, **kwargs):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
train_dataset = import_python_file(train_dataset_filepath)
test_dataset = import_python_file(test_dataset_filepath) if test_dataset_filepath is not None else None
param = import_python_file(param_filepath)
# === plot scatter ===
nrows = 1
ncols = 2
fig, axs = plt.subplots(figsize=(5 * ncols, 5 * nrows), nrows=nrows, ncols=ncols)
train_test_vmaf_on_dataset(
train_dataset,
test_dataset,
param,
param,
axs[0],
axs[1],
result_store,
parallelize=True,
logger=None,
output_model_filepath=output_model_filepath,
**kwargs
)
if "xlim" in kwargs:
axs[0].set_xlim(kwargs["xlim"])
axs[1].set_xlim(kwargs["xlim"])
if "ylim" in kwargs:
axs[0].set_ylim(kwargs["ylim"])
axs[1].set_ylim(kwargs["ylim"])
bbox = {"facecolor": "white", "alpha": 1, "pad": 20}
axs[0].annotate("Training Set", xy=(0.1, 0.85), xycoords="axes fraction", bbox=bbox)
axs[1].annotate("Testing Set", xy=(0.1, 0.85), xycoords="axes fraction", bbox=bbox)
plt.tight_layout()
# === clean up ===
close_logger(logger)
def test_mos_subjective_model_normalize_final(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling(normalize_final=True)
scores = result['quality_scores']
self.assertAlmostEquals(scores[0], 1.1318646945818083, places=4)
self.assertAlmostEquals(scores[10], -1.2400334499143002, places=4)
self.assertAlmostEquals(np.mean(scores), 0.0, places=4)
def test_mos_subjective_model_transform_final(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling(transform_final={'p1': 10, 'p0': 1})
scores = result['quality_scores']
self.assertAlmostEquals(scores[0], 49.84615384615385, places=4)
self.assertAlmostEquals(scores[10], 21.769230769230771, places=4)
self.assertAlmostEquals(np.mean(scores), 36.44790652385589, places=4)
def test_mos_subjective_model_normalize_final(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling(normalize_final=True)
scores = result['quality_scores']
self.assertAlmostEquals(scores[0], 1.1318646945818083, places=4)
self.assertAlmostEquals(scores[10], -1.2400334499143002, places=4)
self.assertAlmostEquals(np.mean(scores), 0.0, places=4)
def test_mos_subjective_model(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(scores[0], 4.884615384615385, places=4)
self.assertAlmostEquals(scores[10], 2.0769230769230771, places=4)
self.assertAlmostEquals(np.mean(scores), 3.544790652385589, places=4)
def test_mos_subjective_model(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(scores[0], 4.884615384615385, places=4)
self.assertAlmostEquals(scores[10], 2.0769230769230771, places=4)
self.assertAlmostEquals(np.mean(scores), 3.544790652385589, places=4)
def test_mos_subjective_model_transform_final(self):
dataset = import_python_file(self.dataset_filepath)
dataset_reader = RawDatasetReader(dataset)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling(transform_final={'p1': 10, 'p0': 1})
scores = result['quality_scores']
self.assertAlmostEquals(scores[0], 49.84615384615385, places=4)
self.assertAlmostEquals(scores[10], 21.769230769230771, places=4)
self.assertAlmostEquals(np.mean(scores), 36.44790652385589, places=4)
def test_observer_content_aware_subjective_model_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModel(dataset_reader)
with self.assertRaises(AssertionError):
result = subjective_model.run_modeling(subject_rejection=True)
def test_observer_content_aware_subjective_model_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModel(dataset_reader)
with self.assertRaises(AssertionError):
result = subjective_model.run_modeling(subject_rejection=True)
def setUp(self):
dataset_filepath = config.ROOT + '/python/test/resource/NFLX_dataset_public_raw.py'
dataset = import_python_file(dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
self.dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
def setUp(self):
dataset_filepath = config.ROOT + '/python/test/resource/NFLX_dataset_public_raw.py'
dataset = import_python_file(dataset_filepath)
np.random.seed(0)
info_dict = {
'missing_probability': 0.1,
}
self.dataset_reader = MissingDataRawDatasetReader(dataset, input_dict=info_dict)
def test_explain_train_test_model(self):
model_class = SklearnRandomForestTrainTestModel
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.features = run_executors_in_parallel(
MomentNorefFeatureExtractor,
train_assets,
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None,
optional_dict=None,
optional_dict2=None,
)
xys = model_class.get_xys_from_results(self.features[:7])
model = model_class({'norm_type':'normalize', 'random_state':0}, None)
model.train(xys)
np.random.seed(0)
xs = model_class.get_xs_from_results(self.features[7:])
explainer = LocalExplainer(neighbor_samples=1000)
exps = explainer.explain(model, xs)
self.assertAlmostEqual(exps['feature_weights'][0, 0], -0.12416, places=4)
self.assertAlmostEqual(exps['feature_weights'][1, 0], 0.00076, places=4)
self.assertAlmostEqual(exps['feature_weights'][0, 1], -0.20931, places=4)
self.assertAlmostEqual(exps['feature_weights'][1, 1], -0.01245, places=4)
self.assertAlmostEqual(exps['feature_weights'][0, 2], 0.02322, places=4)
self.assertAlmostEqual(exps['feature_weights'][1, 2], 0.03673, places=4)
self.assertAlmostEqual(exps['features'][0, 0], 107.73501, places=4)
self.assertAlmostEqual(exps['features'][1, 0], 35.81638, places=4)
self.assertAlmostEqual(exps['features'][0, 1], 13691.23881, places=4)
self.assertAlmostEqual(exps['features'][1, 1], 1611.56764, places=4)
self.assertAlmostEqual(exps['features'][0, 2], 2084.40542, places=4)
self.assertAlmostEqual(exps['features'][1, 2], 328.75389, places=4)
self.assertAlmostEqual(exps['features_normalized'][0, 0], -0.65527, places=4)
self.assertAlmostEqual(exps['features_normalized'][1, 0], -3.74922, places=4)
self.assertAlmostEqual(exps['features_normalized'][0, 1], -0.68872, places=4)
self.assertAlmostEqual(exps['features_normalized'][1, 1], -2.79586, places=4)
self.assertAlmostEqual(exps['features_normalized'][0, 2], 0.08524, places=4)
self.assertAlmostEqual(exps['features_normalized'][1, 2], -1.32625, places=4)
self.assertEqual(exps['feature_names'],
['Moment_noref_feature_1st_score',
'Moment_noref_feature_2nd_score',
'Moment_noref_feature_var_score']
)
def test_explain_train_test_model(self):
model_class = SklearnRandomForestTrainTestModel
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.features = run_executors_in_parallel(
MomentNorefFeatureExtractor,
train_assets,
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None,
optional_dict=None,
optional_dict2=None,
)
xys = model_class.get_xys_from_results(self.features[:7])
model = model_class({'norm_type':'normalize', 'random_state':0}, None)
model.train(xys)
np.random.seed(0)
xs = model_class.get_xs_from_results(self.features[7:])
explainer = LocalExplainer(neighbor_samples=1000)
exps = explainer.explain(model, xs)
self.assertAlmostEqual(exps['feature_weights'][0, 0], -0.12416, places=4)
self.assertAlmostEqual(exps['feature_weights'][1, 0], 0.00076, places=4)
self.assertAlmostEqual(exps['feature_weights'][0, 1], -0.20931, places=4)
self.assertAlmostEqual(exps['feature_weights'][1, 1], -0.01245, places=4)
self.assertAlmostEqual(exps['feature_weights'][0, 2], 0.02322, places=4)
self.assertAlmostEqual(exps['feature_weights'][1, 2], 0.03673, places=4)
self.assertAlmostEqual(exps['features'][0, 0], 107.73501, places=4)
self.assertAlmostEqual(exps['features'][1, 0], 35.81638, places=4)
self.assertAlmostEqual(exps['features'][0, 1], 13691.23881, places=4)
self.assertAlmostEqual(exps['features'][1, 1], 1611.56764, places=4)
self.assertAlmostEqual(exps['features'][0, 2], 2084.40542, places=4)
self.assertAlmostEqual(exps['features'][1, 2], 328.75389, places=4)
self.assertAlmostEqual(exps['features_normalized'][0, 0], -0.65527, places=4)
self.assertAlmostEqual(exps['features_normalized'][1, 0], -3.74922, places=4)
self.assertAlmostEqual(exps['features_normalized'][0, 1], -0.68872, places=4)
self.assertAlmostEqual(exps['features_normalized'][1, 1], -2.79586, places=4)
self.assertAlmostEqual(exps['features_normalized'][0, 2], 0.08524, places=4)
self.assertAlmostEqual(exps['features_normalized'][1, 2], -1.32625, places=4)
self.assertEqual(exps['feature_names'],
['Moment_noref_feature_1st_score',
'Moment_noref_feature_2nd_score',
'Moment_noref_feature_var_score']
)
def test_train_test_on_dataset_with_dis1st_thr(self):
model_param = import_python_file(
config.ROOT + '/python/test/resource/model_param_sample.py')
feature_param = import_python_file(
config.ROOT + '/python/test/resource/feature_param_sample.py')
train_fassembler, train_assets, train_stats, \
test_fassembler, test_assets, test_stats, _ = \
train_test_vmaf_on_dataset(
train_dataset=self.train_dataset, test_dataset=None,
feature_param=feature_param, model_param=model_param,
train_ax=None, test_ax=None, result_store=None,
parallelize=True,
logger=None,
fifo_mode=True,
output_model_filepath=self.output_model_filepath,
)
self.train_fassembler = train_fassembler
self.assertTrue(os.path.exists(self.output_model_filepath))
self.assertItemsEqual(train_stats['ys_label_pred'],
[90.753010402770798, 59.223801498461015,
90.753011435798058, 89.270176556597008])
def test_read_dataset(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.assertEquals(len(train_assets), 9)
self.assertTrue('groundtruth' in train_assets[0].asset_dict.keys())
self.assertTrue('os' not in train_assets[0].asset_dict.keys())
self.assertTrue('width' in train_assets[0].asset_dict.keys())
self.assertTrue('height' in train_assets[0].asset_dict.keys())
self.assertTrue('quality_width' not in train_assets[0].asset_dict.keys())
self.assertTrue('quality_height' not in train_assets[0].asset_dict.keys())
def test_read_dataset(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.assertEquals(len(train_assets), 9)
self.assertTrue('groundtruth' in train_assets[0].asset_dict.keys())
self.assertTrue('os' not in train_assets[0].asset_dict.keys())
self.assertTrue('width' in train_assets[0].asset_dict.keys())
self.assertTrue('height' in train_assets[0].asset_dict.keys())
self.assertTrue('quality_width' not in train_assets[0].asset_dict.keys())
self.assertTrue('quality_height' not in train_assets[0].asset_dict.keys())
def test_train_test_on_dataset_with_dis1st_thr(self):
model_param = import_python_file(
config.ROOT + '/python/test/resource/model_param_sample.py')
feature_param = import_python_file(
config.ROOT + '/python/test/resource/feature_param_sample.py')
train_fassembler, train_assets, train_stats, \
test_fassembler, test_assets, test_stats = \
train_test_on_dataset(
train_dataset=self.train_dataset, test_dataset=None,
feature_param=feature_param, model_param=model_param,
train_ax=None, test_ax=None, result_store=None,
parallelize=True,
logger=None,
fifo_mode=True,
output_model_filepath=self.output_model_filepath,
)
self.train_fassembler = train_fassembler
self.assertTrue(os.path.exists(self.output_model_filepath))
self.assertItemsEqual(train_stats['ys_label_pred'],
[91.707522376672316, 58.277822562766268,
91.707521620497104, 88.307134410232536])
def test_observer_aware_subjective_model_corruptdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.mean(result['quality_scores']), 3.5573073781669944, places=4) # 3.5482845335713469
self.assertAlmostEquals(np.var(result['quality_scores']), 1.3559834438740614, places=4) # 1.4355485462027884
def test_observer_aware_subjective_model_corruptdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.mean(result['quality_scores']), 3.5573073781669944, places=4) # 3.5482845335713469
self.assertAlmostEquals(np.var(result['quality_scores']), 1.3559834438740614, places=4) # 1.4355485462027884
def test_subjrejdmos_subjective_model_corruptdata_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = SubjrejDmosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 4.0246673158065542, places=4)
self.assertAlmostEquals(np.var(scores), 1.0932580358187849, places=4) # 1.4012220200639218
def test_mos_subjective_model_corruptdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 3.5447906523855899, places=4)
self.assertAlmostEquals(np.var(scores), 0.95893305294535369, places=4) # 1.4012220200639218
def test_zscore_mos_subjective_model_corruptdata_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling(zscore_mode=True, subject_rejection=True)
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 0.0, places=4)
self.assertAlmostEquals(np.var(scores), 0.66670826882879042, places=4)
def test_subjrejmos_subjective_model_corruptdata_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = SubjrejMosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 3.5611814345991566, places=4)
self.assertAlmostEquals(np.var(scores), 1.1049505732699529, places=4) # 1.4012220200639218
def test_zscoresubjrejdmos_subjective_model_corruptdata_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = ZscoringSubjrejDmosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 0, places=4)
self.assertAlmostEquals(np.var(scores), 0.66405245792414114, places=4) # 1.4012220200639218
def test_mos_subjective_model_corruptdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 3.5447906523855899, places=4)
self.assertAlmostEquals(np.var(scores), 0.95893305294535369, places=4) # 1.4012220200639218
def test_zscoresubjrejdmos_subjective_model_corruptdata_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = ZscoringSubjrejDmosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 0, places=4)
self.assertAlmostEquals(np.var(scores), 0.66405245792414114, places=4) # 1.4012220200639218
def test_subjrejdmos_subjective_model_corruptdata_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = SubjrejDmosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 4.0246673158065542, places=4)
self.assertAlmostEquals(np.var(scores), 1.0932580358187849, places=4) # 1.4012220200639218
def test_zscore_mos_subjective_model_corruptdata_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MosModel(dataset_reader)
result = subjective_model.run_modeling(zscore_mode=True, subject_rejection=True)
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 0.0, places=4)
self.assertAlmostEquals(np.var(scores), 0.66670826882879042, places=4)
def test_subjrejmos_subjective_model_corruptdata_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = SubjrejMosModel(dataset_reader)
result = subjective_model.run_modeling()
scores = result['quality_scores']
self.assertAlmostEquals(np.mean(scores), 3.5611814345991566, places=4)
self.assertAlmostEquals(np.var(scores), 1.1049505732699529, places=4) # 1.4012220200639218
def run_vmaf_kfold_cv(dataset_filepath,
contentid_groups,
param_filepath,
aggregate_method,
):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
dataset = import_python_file(dataset_filepath)
param = import_python_file(param_filepath)
fig, ax = plt.subplots(figsize=(5, 5), nrows=1, ncols=1)
cv_on_dataset(dataset, param, param, ax, result_store, contentid_groups,
logger, aggregate_method)
ax.set_xlim([0, 120])
ax.set_ylim([0, 120])
plt.tight_layout()
# === clean up ===
close_logger(logger)
def test_train_test_on_dataset_with_dis1st_thr(self):
model_param = import_python_file(
config.ROOT + '/python/test/resource/model_param_sample.py')
feature_param = import_python_file(
config.ROOT + '/python/test/resource/feature_param_sample.py')
train_fassembler, train_assets, train_stats, \
test_fassembler, test_assets, test_stats = \
train_test_on_dataset(
train_dataset=self.train_dataset, test_dataset=None,
feature_param=feature_param, model_param=model_param,
train_ax=None, test_ax=None, result_store=None,
parallelize=True,
logger=None,
fifo_mode=True,
output_model_filepath=self.output_model_filepath,
)
self.train_fassembler = train_fassembler
self.assertTrue(os.path.exists(self.output_model_filepath))
self.assertItemsEqual(train_stats['ys_label_pred'], [
91.707522376672316, 58.277822562766268, 91.707521620497104,
88.307134410232536
])
def test_observer_content_aware_subjective_model_missingdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'missing_probability': 0.1,
}
dataset_reader = MissingDataRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModel(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.var(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity']), 3.9104244772977128, places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']), 0.0037713583509767193, places=4)
self.assertAlmostEquals(np.sum(result['observer_bias']), -0.21903272050455846, places=4)
self.assertAlmostEquals(np.var(result['observer_bias']), 0.084353684687185043, places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']), 9.8168943054654481, places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']), 0.028159236075789944, places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']), 280.05548186797336, places=4)
self.assertAlmostEquals(np.var(result['quality_scores']), 1.4339487982797514, places=4)
np.random.seed(0)
info_dict = {
'missing_probability': 0.5,
}
dataset_reader = MissingDataRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModel(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.var(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity']), 2.63184284168883, places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']), 0.019164097909450246, places=4)
self.assertAlmostEquals(np.sum(result['observer_bias']), 0.2263148440748638, places=4)
self.assertAlmostEquals(np.var(result['observer_bias']), 0.070613033112114504, places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']), 12.317917502439435, places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']), 0.029455722248727296, places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']), 280.29962156788139, places=4)
self.assertAlmostEquals(np.var(result['quality_scores']), 1.4717366222424826, places=4)
def test_observer_content_aware_subjective_model_missingdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'missing_probability': 0.1,
}
dataset_reader = MissingDataRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModel(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.var(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity']), 3.9104244772977128, places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']), 0.0037713583509767193, places=4)
self.assertAlmostEquals(np.sum(result['observer_bias']), -0.21903272050455846, places=4)
self.assertAlmostEquals(np.var(result['observer_bias']), 0.084353684687185043, places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']), 9.8168943054654481, places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']), 0.028159236075789944, places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']), 280.05548186797336, places=4)
self.assertAlmostEquals(np.var(result['quality_scores']), 1.4339487982797514, places=4)
np.random.seed(0)
info_dict = {
'missing_probability': 0.5,
}
dataset_reader = MissingDataRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModel(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.var(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity']), 2.63184284168883, places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']), 0.019164097909450246, places=4)
self.assertAlmostEquals(np.sum(result['observer_bias']), 0.2263148440748638, places=4)
self.assertAlmostEquals(np.var(result['observer_bias']), 0.070613033112114504, places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']), 12.317917502439435, places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']), 0.029455722248727296, places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']), 280.29962156788139, places=4)
self.assertAlmostEquals(np.var(result['quality_scores']), 1.4717366222424826, places=4)
def test_read_dataset_diffyuv(self):
train_dataset_path = config.ROOT + '/python/test/resource/test_dataset_diffyuv.py'
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.assertEquals(len(train_assets), 4)
self.assertEquals(train_assets[0].ref_width_height, (1920, 1080))
self.assertEquals(train_assets[0].dis_width_height, (1920, 1080))
self.assertEquals(train_assets[0].quality_width_height, (1920, 1080))
self.assertEquals(train_assets[0].yuv_type, 'yuv420p')
self.assertEquals(train_assets[2].ref_width_height, (1280, 720))
self.assertEquals(train_assets[2].dis_width_height, (1280, 720))
self.assertEquals(train_assets[2].quality_width_height, (1280, 720))
self.assertEquals(train_assets[2].yuv_type, 'yuv420p10le')
def test_read_dataset_qualitywh2(self):
train_dataset_path = config.ROOT + '/python/test/resource/test_image_dataset_diffdim_qualitywh2.py'
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.assertTrue('quality_width' in train_assets[0].asset_dict.keys())
self.assertTrue('quality_height' in train_assets[0].asset_dict.keys())
self.assertTrue('resampling_type' in train_assets[0].asset_dict.keys())
self.assertTrue('quality_width' not in train_assets[1].asset_dict.keys())
self.assertTrue('quality_height' not in train_assets[1].asset_dict.keys())
self.assertTrue('resampling_type' not in train_assets[1].asset_dict.keys())
self.assertEqual(train_assets[0].asset_dict['quality_width'], 200)
self.assertEqual(train_assets[0].asset_dict['quality_height'], 100)
self.assertEqual(train_assets[0].asset_dict['resampling_type'], 'bicubic')
def test_read_dataset_qualitywh2(self):
train_dataset_path = config.ROOT + '/python/test/resource/test_image_dataset_diffdim_qualitywh2.py'
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.assertTrue('quality_width' in train_assets[0].asset_dict.keys())
self.assertTrue('quality_height' in train_assets[0].asset_dict.keys())
self.assertTrue('resampling_type' in train_assets[0].asset_dict.keys())
self.assertTrue('quality_width' not in train_assets[1].asset_dict.keys())
self.assertTrue('quality_height' not in train_assets[1].asset_dict.keys())
self.assertTrue('resampling_type' not in train_assets[1].asset_dict.keys())
self.assertEqual(train_assets[0].asset_dict['quality_width'], 200)
self.assertEqual(train_assets[0].asset_dict['quality_height'], 100)
self.assertEqual(train_assets[0].asset_dict['resampling_type'], 'bicubic')
def setUp(self):
dataset_filepath = config.ROOT + '/python/test/resource/NFLX_dataset_public_raw.py'
dataset = import_python_file(dataset_filepath)
np.random.seed(0)
info_dict = {
'quality_scores': np.random.randint(1, 6, 79),
'observer_bias': np.random.normal(0, 1, 26),
'observer_inconsistency': np.abs(np.random.normal(0, 0.1, 26)),
'content_bias': np.zeros(9),
'content_ambiguity': np.zeros(9),
}
self.dataset_reader = SyntheticRawDatasetReader(dataset, input_dict=info_dict)
def test_read_dataset_diffyuv(self):
train_dataset_path = config.ROOT + '/python/test/resource/test_dataset_diffyuv.py'
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.assertEquals(len(train_assets), 4)
self.assertEquals(train_assets[0].ref_width_height, (1920, 1080))
self.assertEquals(train_assets[0].dis_width_height, (1920, 1080))
self.assertEquals(train_assets[0].quality_width_height, (1920, 1080))
self.assertEquals(train_assets[0].yuv_type, 'yuv420p')
self.assertEquals(train_assets[2].ref_width_height, (1280, 720))
self.assertEquals(train_assets[2].dis_width_height, (1280, 720))
self.assertEquals(train_assets[2].quality_width_height, (1280, 720))
self.assertEquals(train_assets[2].yuv_type, 'yuv420p10le')
def run_vmaf_cv(train_dataset_filepath,
test_dataset_filepath,
param_filepath,
output_model_filepath=None):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
train_dataset = import_python_file(train_dataset_filepath)
test_dataset = import_python_file(test_dataset_filepath)
param = import_python_file(param_filepath)
# === plot scatter ===
nrows = 1
ncols = 2
fig, axs = plt.subplots(figsize=(5*ncols, 5*nrows), nrows=nrows, ncols=ncols)
train_test_on_dataset(train_dataset, test_dataset, param, param, axs[0], axs[1],
result_store, parallelize=False, logger=None,
output_model_filepath=output_model_filepath)
# axs[0].set_xlim([0, 120])
# axs[0].set_ylim([0, 120])
# axs[1].set_xlim([0, 120])
# axs[1].set_ylim([0, 120])
bbox = {'facecolor':'white', 'alpha':1, 'pad':20}
axs[0].annotate('Training Set', xy=(0.1, 0.85), xycoords='axes fraction', bbox=bbox)
axs[1].annotate('Testing Set', xy=(0.1, 0.85), xycoords='axes fraction', bbox=bbox)
plt.tight_layout()
# === clean up ===
close_logger(logger)
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.features = run_executors_in_parallel(
MomentNorefFeatureExtractor,
train_assets,
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None,
optional_dict=None,
optional_dict2=None,
)
def test_test_on_dataset_raw(self):
test_dataset = import_python_file(
config.ROOT + '/python/test/resource/raw_dataset_sample.py')
test_assets, results = test_on_dataset(test_dataset, VmafQualityRunner, None,
None, None,
parallelize=True,
aggregate_method=None)
self.assertAlmostEqual(results[0]['VMAF_score'], 99.620284242680668, places=4)
self.assertAlmostEqual(results[1]['VMAF_score'], 28.98459923031756, places=4)
self.assertAlmostEqual(results[2]['VMAF_score'], 98.860794831997097, places=4)
self.assertAlmostEqual(results[3]['VMAF_score'], 98.860790186334995, places=4)
self.assertAlmostEqual(test_assets[0].groundtruth, 100, places=4)
self.assertAlmostEqual(test_assets[1].groundtruth, 50, places=4)
self.assertAlmostEqual(test_assets[2].groundtruth, 100, places=4)
self.assertAlmostEqual(test_assets[3].groundtruth, 90, places=4)
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,
)