def tearDown(self): if hasattr(self, 'h5py_file'): DisYUVRawVideoExtractor.close_h5py_file(self.h5py_file) if os.path.exists(self.h5py_filepath): os.remove(self.h5py_filepath) if os.path.exists(self.model_filename): os.remove(self.model_filename)
def tearDown(self): if hasattr(self, 'h5py_file'): DisYUVRawVideoExtractor.close_h5py_file(self.h5py_file) if os.path.exists(self.h5py_filepath): os.remove(self.h5py_filepath) if os.path.exists(self.model_filename): os.remove(self.model_filename)
def tearDown(self): if hasattr(self, 'raw_video_h5py_file'): DisYUVRawVideoExtractor.close_h5py_file(self.raw_video_h5py_file) if os.path.exists(self.raw_video_h5py_filepath): os.remove(self.raw_video_h5py_filepath) if os.path.exists(self.patch_h5py_filepath): os.remove(self.patch_h5py_filepath) if hasattr(self, 'model'): self.model.delete(self.model_filename)
def test_run_dis_yuv_raw_video_extractor(self): print 'test on running dis YUV raw video extractor...' 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=1, 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=2, workdir_root=config.ROOT + "/workspace/workdir", 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.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_parallel_dis_y_fextractor(self): print 'test on running dis YUV raw video extractor in parallel (disabled)...' 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=1, 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=2, workdir_root=config.ROOT + "/workspace/workdir", 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} self.fextractors, results = run_executors_in_parallel( DisYUVRawVideoExtractor, [asset, asset_original], fifo_mode=True, delete_workdir=True, parallelize=False, # Can't run parallel: can't pickle FileID objects result_store=None, optional_dict={'channels': 'yu'}, optional_dict2=optional_dict2) 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_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()
class DisYUVRawVideoExtractorTest(unittest.TestCase): def setUp(self): self.h5py_filepath = config.ROOT + '/workspace/workdir/test.hdf5' def tearDown(self): if os.path.exists(self.h5py_filepath): os.remove(self.h5py_filepath) def test_run_dis_yuv_raw_video_extractor(self): print 'test on running dis YUV raw video extractor...' 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=1, 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=2, workdir_root=config.ROOT + "/workspace/workdir", 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.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_parallel_dis_y_fextractor(self): print 'test on running dis YUV raw video extractor in parallel (disabled)...' 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=1, 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=2, workdir_root=config.ROOT + "/workspace/workdir", 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} self.fextractors, results = run_executors_in_parallel( DisYUVRawVideoExtractor, [asset, asset_original], fifo_mode=True, delete_workdir=True, parallelize=False, # Can't run parallel: can't pickle FileID objects result_store=None, optional_dict={'channels': 'yu'}, optional_dict2=optional_dict2 ) 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 = 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=1, 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=2, workdir_root=config.ROOT + "/workspace/workdir", 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 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, )
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)