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_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_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_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_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 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 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_read_dataset_crop_and_pad(self):
train_dataset_path = config.ROOT + '/python/test/resource/example_dataset_crop_pad.py'
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
self.assertEquals(len(train_assets), 3)
self.assertEquals(
str(train_assets[0]),
"example_0_1_src01_hrc00_576x324_576x324_vs_src01_hrc01_576x324_576x324_q_576x324_crop288:162:144:81"
)
self.assertEquals(
str(train_assets[1]),
"example_0_2_src01_hrc00_576x324_576x324_vs_src01_hrc01_576x324_576x324_q_576x324_padiw+100:ih+100:50:50"
)
self.assertEquals(
str(train_assets[2]),
"example_0_3_src01_hrc00_576x324_576x324_vs_src01_hrc01_576x324_576x324_q_576x324_crop288:162:144:81_padiw+288:ih+162:144:81"
)
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,
)
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,
)
from core.raw_extractor import DisYUVRawVideoExtractor
from core.nn_train_test_model import ToddNoiseClassifierTrainTestModel
from routine import read_dataset
from tools.misc import import_python_file
# parameters
num_train = 500
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,