def test_explain_vmaf_results(self):
print 'test on running VMAF runner with local explainer...'
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv"
asset = Asset(dataset="test", content_id=0, asset_id=0,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324})
asset_original = Asset(dataset="test", content_id=0, asset_id=1,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324})
self.runner = VmafQualityRunnerWithLocalExplainer(
[asset, asset_original],
None, fifo_mode=True,
delete_workdir=True,
result_store=None,
optional_dict2={'explainer': LocalExplainer(neighbor_samples=100)}
)
np.random.seed(0)
self.runner.run()
results = self.runner.results
self.assertAlmostEqual(results[0]['VMAF_score'], 65.4488588759, places=4)
self.assertAlmostEqual(results[1]['VMAF_score'], 99.2259317881, places=4)
expected_feature_names = ['VMAF_feature_adm2_score',
'VMAF_feature_motion_score',
'VMAF_feature_vif_scale0_score',
'VMAF_feature_vif_scale1_score',
'VMAF_feature_vif_scale2_score',
'VMAF_feature_vif_scale3_score']
weights = np.mean(results[0]['VMAF_scores_exps']['feature_weights'], axis=0)
self.assertAlmostEqual(weights[0], 0.75441663, places=4)
self.assertAlmostEqual(weights[1], 0.06816105, places=4)
self.assertAlmostEqual(weights[2], -0.10934421, places=4)
self.assertAlmostEqual(weights[3], 0.22051127, places=4)
self.assertAlmostEqual(weights[4], 0.12517884, places=4)
self.assertAlmostEqual(weights[5], 0.04639162, places=4)
self.assertEqual(results[0]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
weights = np.mean(results[1]['VMAF_scores_exps']['feature_weights'], axis=0)
self.assertAlmostEqual(weights[0], 0.77096087, places=4)
self.assertAlmostEqual(weights[1], 0.01491754, places=4)
self.assertAlmostEqual(weights[2], -0.08025557, places=4)
self.assertAlmostEqual(weights[3], 0.2511188, places=4)
self.assertAlmostEqual(weights[4], 0.14953561, places=4)
self.assertAlmostEqual(weights[5], 0.07960753, places=4)
self.assertEqual(results[1]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
class LocalExplainerTest(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
if hasattr(self, 'runner'):
self.runner.remove_results()
pass
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_vmaf_results(self):
print 'test on running VMAF runner with local explainer...'
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv"
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=dis_path,
asset_dict={
'width': 576,
'height': 324
})
asset_original = Asset(dataset="test",
content_id=0,
asset_id=1,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324
})
self.runner = VmafQualityRunnerWithLocalExplainer(
[asset, asset_original],
None,
fifo_mode=True,
delete_workdir=True,
result_store=None,
optional_dict2={'explainer': LocalExplainer(neighbor_samples=100)})
np.random.seed(0)
self.runner.run()
results = self.runner.results
self.assertAlmostEqual(results[0]['VMAF_score'],
65.4488588759,
places=4)
self.assertAlmostEqual(results[1]['VMAF_score'],
99.2259317881,
places=4)
expected_feature_names = [
'VMAF_feature_adm2_score', 'VMAF_feature_motion_score',
'VMAF_feature_vif_scale0_score', 'VMAF_feature_vif_scale1_score',
'VMAF_feature_vif_scale2_score', 'VMAF_feature_vif_scale3_score'
]
weights = np.mean(results[0]['VMAF_scores_exps']['feature_weights'],
axis=0)
self.assertAlmostEqual(weights[0], 0.75441663, places=4)
self.assertAlmostEqual(weights[1], 0.06816105, places=4)
self.assertAlmostEqual(weights[2], -0.10934421, places=4)
self.assertAlmostEqual(weights[3], 0.22051127, places=4)
self.assertAlmostEqual(weights[4], 0.12517884, places=4)
self.assertAlmostEqual(weights[5], 0.04639162, places=4)
self.assertEqual(results[0]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
weights = np.mean(results[1]['VMAF_scores_exps']['feature_weights'],
axis=0)
self.assertAlmostEqual(weights[0], 0.77096087, places=4)
self.assertAlmostEqual(weights[1], 0.01491754, places=4)
self.assertAlmostEqual(weights[2], -0.08025557, places=4)
self.assertAlmostEqual(weights[3], 0.2511188, places=4)
self.assertAlmostEqual(weights[4], 0.14953561, places=4)
self.assertAlmostEqual(weights[5], 0.07960753, places=4)
self.assertEqual(results[1]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
def test_explain_vmaf_results(self):
print 'test on running VMAF runner with local explainer...'
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv"
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=dis_path,
asset_dict={
'width': 576,
'height': 324
})
asset_original = Asset(dataset="test",
content_id=0,
asset_id=1,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324
})
self.runner = VmafQualityRunnerWithLocalExplainer(
[asset, asset_original],
None,
fifo_mode=True,
delete_workdir=True,
result_store=None,
optional_dict2={'explainer': LocalExplainer(neighbor_samples=100)})
np.random.seed(0)
self.runner.run()
results = self.runner.results
self.assertAlmostEqual(results[0]['VMAF_score'],
65.4488588759,
places=4)
self.assertAlmostEqual(results[1]['VMAF_score'],
99.2259317881,
places=4)
expected_feature_names = [
'VMAF_feature_adm2_score', 'VMAF_feature_motion_score',
'VMAF_feature_vif_scale0_score', 'VMAF_feature_vif_scale1_score',
'VMAF_feature_vif_scale2_score', 'VMAF_feature_vif_scale3_score'
]
weights = np.mean(results[0]['VMAF_scores_exps']['feature_weights'],
axis=0)
self.assertAlmostEqual(weights[0], 0.75441663, places=4)
self.assertAlmostEqual(weights[1], 0.06816105, places=4)
self.assertAlmostEqual(weights[2], -0.10934421, places=4)
self.assertAlmostEqual(weights[3], 0.22051127, places=4)
self.assertAlmostEqual(weights[4], 0.12517884, places=4)
self.assertAlmostEqual(weights[5], 0.04639162, places=4)
self.assertEqual(results[0]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
weights = np.mean(results[1]['VMAF_scores_exps']['feature_weights'],
axis=0)
self.assertAlmostEqual(weights[0], 0.77096087, places=4)
self.assertAlmostEqual(weights[1], 0.01491754, places=4)
self.assertAlmostEqual(weights[2], -0.08025557, places=4)
self.assertAlmostEqual(weights[3], 0.2511188, places=4)
self.assertAlmostEqual(weights[4], 0.14953561, places=4)
self.assertAlmostEqual(weights[5], 0.07960753, places=4)
self.assertEqual(results[1]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
class LocalExplainerTest(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
if hasattr(self, 'runner'):
self.runner.remove_results()
pass
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_vmaf_results(self):
print 'test on running VMAF runner with local explainer...'
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv"
asset = Asset(dataset="test", content_id=0, asset_id=0,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324})
asset_original = Asset(dataset="test", content_id=0, asset_id=1,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324})
self.runner = VmafQualityRunnerWithLocalExplainer(
[asset, asset_original],
None, fifo_mode=True,
delete_workdir=True,
result_store=None,
optional_dict2={'explainer': LocalExplainer(neighbor_samples=100)}
)
np.random.seed(0)
self.runner.run()
results = self.runner.results
self.assertAlmostEqual(results[0]['VMAF_score'], 65.4488588759, places=4)
self.assertAlmostEqual(results[1]['VMAF_score'], 99.2259317881, places=4)
expected_feature_names = ['VMAF_feature_adm2_score',
'VMAF_feature_motion_score',
'VMAF_feature_vif_scale0_score',
'VMAF_feature_vif_scale1_score',
'VMAF_feature_vif_scale2_score',
'VMAF_feature_vif_scale3_score']
weights = np.mean(results[0]['VMAF_scores_exps']['feature_weights'], axis=0)
self.assertAlmostEqual(weights[0], 0.75441663, places=4)
self.assertAlmostEqual(weights[1], 0.06816105, places=4)
self.assertAlmostEqual(weights[2], -0.10934421, places=4)
self.assertAlmostEqual(weights[3], 0.22051127, places=4)
self.assertAlmostEqual(weights[4], 0.12517884, places=4)
self.assertAlmostEqual(weights[5], 0.04639162, places=4)
self.assertEqual(results[0]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
weights = np.mean(results[1]['VMAF_scores_exps']['feature_weights'], axis=0)
self.assertAlmostEqual(weights[0], 0.77096087, places=4)
self.assertAlmostEqual(weights[1], 0.01491754, places=4)
self.assertAlmostEqual(weights[2], -0.08025557, places=4)
self.assertAlmostEqual(weights[3], 0.2511188, places=4)
self.assertAlmostEqual(weights[4], 0.14953561, places=4)
self.assertAlmostEqual(weights[5], 0.07960753, places=4)
self.assertEqual(results[1]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
