image_folder,
mask_folder,
aug_dict_image,
aug_dict_mask,
image_color_mode="grayscale",
mask_color_mode="grayscale",
image_save_prefix="image",
mask_save_prefix="mask",
flag_multi_class=False,
num_class=num_class,
save_to_dir=None,
target_size=(256, 256),
seed=1)
#Instantiate the model
model = unet()
#Save the model after each epoch
model_checkpoint = ModelCheckpoint(
'/media/hd1/unet_model_training_data/unet-master/data_' + channel +
'/06082020_unet_little-delta_' + little_delta + '_total-time_' +
total_time + '.hdf5',
monitor='loss',
verbose=1,
save_best_only=True)
#Fit the model with 5 epochs
try:
H1 = model.fit_generator(myData,
steps_per_epoch=steps_per_epoch,
epochs=2,
callbacks=[model_checkpoint])
num_class = 3
#Augment the data with some defined alterations
#These are defined by U-Net package
#The mask must have the same augmentation applied to it excepting brightness
aug_dict_image = dict()
aug_dict_mask = dict()
cnt = 0
for i1 in np.arange(1, 7):
for i2 in np.arange(1, 7):
if cnt == input_cnt:
weights_file = '/media/hd1/unet_model_training_data_cm/unet-master/data_' + ch + '/round_' + str(
i1) + '_06302020_unet_little-delta_' + str(
little_delta) + '_total-time_' + str(total_time) + '.hdf5'
model = unet(pretrained_weights=weights_file)
test_path = '/media/hd1/unet_model_training_data_cm/unet-master/data_' + ch + '/unet_data/round_' + str(
i2) + '_test_little-delta_' + str(
little_delta) + '_total-time_' + str(total_time)
#Test the model
myDataTest = trainGenerator(batch_size,
test_path,
image_folder,
mask_folder,
aug_dict_image,
aug_dict_mask,
image_color_mode="grayscale",
mask_color_mode="grayscale",
image_save_prefix="image",
mask_save_prefix="mask",