class metricTEST(unittest.TestCase):
# Create random imaging and segmentation data
@classmethod
def setUpClass(self):
np.random.seed(1234)
# Create 2D imgaging and segmentation data set
self.dataset = dict()
for i in range(0, 2):
img = np.random.rand(16, 16) * 255
self.img = img.astype(int)
seg = np.random.rand(16, 16) * 2
self.seg = seg.astype(int)
self.dataset["TEST.sample_" + str(i)] = (self.img, self.seg)
# Initialize Dictionary IO Interface
io_interface = Dictionary_interface(self.dataset, classes=3,
three_dim=False)
# Initialize temporary directory
self.tmp_dir = tempfile.TemporaryDirectory(prefix="tmp.CESP.")
tmp_batches = os.path.join(self.tmp_dir.name, "batches")
# Initialize Data IO
self.data_io = Data_IO(io_interface,
input_path=os.path.join(self.tmp_dir.name),
output_path=os.path.join(self.tmp_dir.name),
batch_path=tmp_batches, delete_batchDir=False)
# Initialize Preprocessor
self.pp = Preprocessor(self.data_io, batch_size=1,
data_aug=None, analysis="fullimage")
# Initialize Neural Network
self.model = Neural_Network(self.pp)
# Get sample list
self.sample_list = self.data_io.get_indiceslist()
# Delete all temporary files
@classmethod
def tearDownClass(self):
self.tmp_dir.cleanup()
#-------------------------------------------------#
# Standard DSC Metric #
#-------------------------------------------------#
def test_METRICS_DSC_standard(self):
self.model.loss = dice_coefficient
self.model.metrics = [dice_coefficient]
self.model.train(self.sample_list, epochs=1)
#-------------------------------------------------#
# Standard DSC Loss #
#-------------------------------------------------#
def test_METRICS_DSC_standardLOSS(self):
self.model.loss = dice_coefficient_loss
self.model.metrics = [dice_coefficient_loss]
self.model.train(self.sample_list, epochs=1)
#-------------------------------------------------#
# Soft DSC Metric #
#-------------------------------------------------#
def test_METRICS_DSC_soft(self):
self.model.loss = dice_soft
self.model.metrics = [dice_soft]
self.model.train(self.sample_list, epochs=1)
#-------------------------------------------------#
# Soft DSC Loss #
#-------------------------------------------------#
def test_METRICS_DSC_softLOSS(self):
self.model.loss = dice_soft_loss
self.model.metrics = [dice_soft_loss]
self.model.train(self.sample_list, epochs=1)
#-------------------------------------------------#
# Weighted DSC #
#-------------------------------------------------#
def test_METRICS_DSC_weighted(self):
self.model.loss = dice_weighted([1,1,4])
self.model.metrics = [dice_weighted([1,1,4])]
self.model.train(self.sample_list, epochs=1)
#-------------------------------------------------#
# Dice & Crossentropy loss #
#-------------------------------------------------#
def test_METRICS_DSC_CrossEntropy(self):
self.model.loss = dice_crossentropy
self.model.metrics = [dice_crossentropy]
self.model.train(self.sample_list, epochs=1)
#-------------------------------------------------#
# Tversky loss #
#-------------------------------------------------#
def test_METRICS_Tversky(self):
self.model.loss = tversky_loss
self.model.metrics = [tversky_loss]
self.model.train(self.sample_list, epochs=1)
#-------------------------------------------------#
# Tversky & Crossentropy loss #
#-------------------------------------------------#
def test_METRICS_Tversky_CrossEntropy(self):
self.model.loss = tversky_crossentropy
self.model.metrics = [tversky_crossentropy]
self.model.train(self.sample_list, epochs=1)
def test_MODEL_training3D(self):
nn = Neural_Network(preprocessor=self.pp3D)
nn.train(self.sample_list3D, epochs=3)