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 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)
model = ToddNoiseClassifierTrainTestModel(
param_dict={
'seed': seed,
'n_epochs': n_epochs,
},
logger=None,
optional_dict2={ # for options that won't impact the result
# 'checkpoints_dir': config.ROOT + '/workspace/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.'