def __get_unet(self):
inputs = Input((self.img_rows, self.img_cols, 4)) # (b, 224, 224, 1)
filters = 16 # 64
stem = inputs
conv224, p112 = self.__pool_layer(stem, filters=filters, block_num=1) # (b, 112, 112, 64)
conv112, p56 = self.__pool_layer(p112, filters=filters * 2, block_num=2) # (b, 56, 56, 128)
conv56, p28 = self.__pool_layer(p56, filters=filters * 4, block_num=3) # (b, 28, 28, 256)
conv28, p14 = self.__pool_layer(p28, filters=filters * 8, block_num=4) # (b, 14, 14, 512)
conv14, _ = self.__pool_layer(p14, filters=filters * 16, block_num=5) # (b, 14, 14, 512)
# conv14 = Dropout(0.5)(conv14)
u28 = self.__unpool_block(pooled=conv14, prepooled=conv28, block_num=1)
u56 = self.__unpool_block(pooled=u28, prepooled=conv56, block_num=2)
u112 = self.__unpool_block(pooled=u56, prepooled=conv112, block_num=3)
u224 = self.__unpool_block(pooled=u112, prepooled=conv224, block_num=4)
predictions = Conv2D(filters=4, kernel_size=1, activation='softmax', name='predictions')(u224)
mask = Lambda(metrics.compute_mask)(inputs)
masked_predictions = predictions # Lambda(lambda mask_n_preds: mask_n_preds[0] * mask_n_preds[1])(
# [mask, predictions]) # multiply([mask, predictions])
model = Model(inputs=inputs, outputs=masked_predictions)
model.compile(optimizer=Adam(lr=1e-3), loss=metrics.keras_dice_coef_loss(),
metrics=[metrics.wt_dice,
metrics.tc_dice,
metrics.et_dice])
model.summary()
plot_model(model, self.config.results_path + '/model.png', show_shapes=True, show_layer_names=True)
return model
def __get_unet(self):
inputs = Input((self.img_rows, self.img_cols, self.img_depth, 4)) # (b, 224, 224, 1)
filters = 8 # 64
conv224, p112 = self.__pool_layer(inputs, filters=filters, block_num=1) # (b, 112, 112, 16)
conv112, p56 = self.__pool_layer(p112, filters=filters*2, block_num=2) # (b, 56, 56, 32)
conv56, p28 = self.__pool_layer(p56, filters=filters*4, block_num=3) # (b, 28, 28, 64)
conv28, p14 = self.__pool_layer(p28, filters=filters*8, block_num=4) # (b, 14, 14, 128)
conv14, _ = self.__pool_layer(p14, filters=filters*16, block_num=5) # (b, 14, 14, 256)
# conv14 = Dropout(0.5)(conv14)
u28 = self.__unpool_block(pooled=conv14, prepooled=conv28, block_num=1)
u56 = self.__unpool_block(pooled=u28, prepooled=conv56, block_num=2)
u112 = self.__unpool_block(pooled=u56, prepooled=conv112, block_num=3)
u224 = self.__unpool_block(pooled=u112, prepooled=conv224, block_num=4)
predictions = Conv3D(filters=4, kernel_size=1, activation='softmax', name='predictions')(u224)
mask = Lambda(metrics.compute_mask)(inputs)
masked_predictions = Lambda(lambda mask_n_preds: mask_n_preds[0] * mask_n_preds[1])([mask, predictions]) # multiply([mask, predictions])
model = Model(inputs=inputs, outputs=masked_predictions)
model.compile(optimizer=Adam(lr=1e-3), loss=metrics.keras_dice_coef_loss(), metrics=[metrics.category_dice_score(1), metrics.category_dice_score(2), metrics.category_dice_score(3)])
return model
(scores_crf[0], scores_crf[1], scores_crf[2]))
# For testing
if __name__ == '__main__':
import config as configuration
from read_data import BRATSReader
from evaluation_generator import EvalGenerator
from keras.models import load_model
import metrics
import keras
config = configuration.Config()
# Super hacky way to load weights and architecture. Absolutely not ok to run training or keras metrics on this.
keras.losses.keras_dice_coef_loss_fn = metrics.keras_dice_coef_loss()
keras.metrics.hard_dice = metrics.wt_dice
keras.metrics.wt_dice = metrics.wt_dice
keras.metrics.et_dice = metrics.et_dice
keras.metrics.tc_dice = metrics.tc_dice
axial_modals = [load_model("unet.hdf5")]
multi_modals = []
brats = BRATSReader(use_hgg=True, use_lgg=True)
# print(brats.get_mean_dev(.15, 't1ce'))
train_ids, val_ids, test_ids = brats.get_case_ids(config.brats_val_split)
height, width, slices = brats.get_dims()
#train_datagen = EvalGenerator(brats, train_ids, dim=(height, width, 4))
val_datagen = EvalGenerator(brats, val_ids, dim=(height, width, 4))