## Here we are using the Resize subfunctions due to many 2D models
## want a specific shape (e.g. DenseNet for classification)
sf = [Resize(new_shape=(224, 224))]
# Initialize the Preprocessor class
pp = Preprocessor(data_io, data_aug=None, batch_size=1, subfunctions=sf,
prepare_subfunctions=True, prepare_batches=False,
analysis="fullimage")
## We are using fullimage analysis due to a 2D image can easily fit completely
## in our GPU
# Initialize the neural network model
model = Neural_Network(preprocessor=pp)
# Start the fitting on some slices
model.train(samples_list[30:50], epochs=3, iterations=10, callbacks=[])
# Predict a generic slice with direct output
pred = model.predict(["case_00002:#:42"], return_output=True)
print(np.asarray(pred).shape)
## Be aware that the direct prediction output, has a additional batch axis
# Predict a generic slice and save it as a NumPy pickle on disk
model.predict(["case_00002:#:42"], return_output=False)
# Load the slice via sample-loader and output also the new prediction, now
sample = data_io.sample_loader("case_00002:#:42", load_seg=True, load_pred=True)
print(sample.img_data.shape, sample.seg_data.shape, sample.pred_data.shape)
## Final words
def test_MODEL_multiGPU(self):
nn = Neural_Network(preprocessor=self.pp2D,
multi_gpu=True)
nn.train(self.sample_list2D, epochs=3)
def test_MODEL_training3D(self):
nn = Neural_Network(preprocessor=self.pp3D)
nn.train(self.sample_list3D, epochs=3)
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.miscnn.")
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)
#-------------------------------------------------#
# Focal Loss - Categorical #
#-------------------------------------------------#
def test_METRICS_FocalCategorical(self):
self.model.loss = categorical_focal_loss([0.9, 0.1])
self.model.metrics = [categorical_focal_loss([0.1, 0.9])]
self.model.train(self.sample_list, epochs=1)