def _unet(n_classes,
encoder,
l1_skip_conn=True,
input_height=512,
input_width=704):
img_input, levels = encoder(input_height=input_height,
input_width=input_width)
[f1, f2, f3, f4, f5] = levels
o = f4
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o)
o = (BatchNormalization())(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f3], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o)
o = (BatchNormalization())(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f2], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o)
o = (BatchNormalization())(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
if l1_skip_conn:
o = (concatenate([o, f1], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o)
o = (BatchNormalization())(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
if l1_skip_conn:
o = (concatenate([o, img_input], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o)
o = (BatchNormalization())(o)
o = Conv2D(n_classes, (3, 3), padding='same',
data_format=IMAGE_ORDERING)(o)
model = get_segmentation_model(img_input, o)
return model
up1 = np.concatenate([UpSampling2D((2, 2))(conv3), conv2], axis=-1)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
conv4 = Dropout(0.2)(conv4)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv4)
up2 = np.concatenate([UpSampling2D((2, 2))(conv4), conv1], axis=-1)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(up2)
conv5 = Dropout(0.2)(conv5)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv5)
out = Conv2D(n_classes, (1, 1), padding='same')(conv5)
from keras_segmentation.models.model_utils import get_segmentation_model
model = get_segmentation_model(img_input,
out) # this would build the segmentation model
#%% from https://segmentation-models.readthedocs.io/en/latest/tutorial.html
from segmentation_models import Unet
from segmentation_models import get_preprocessing
from segmentation_models.losses import bce_jaccard_loss
from segmentation_models.metrics import iou_score
BACKBONE = 'resnet34'
preprocess_input = get_preprocessing(BACKBONE)
# load your data
#x_train, y_train, x_val, y_val = load_data(...)
# preprocess input
conv2 = Dropout(0.2)(conv2)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D((2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Dropout(0.2)(conv3)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
up1 = concatenate([UpSampling2D((2, 2))(conv3), conv2], axis=-1)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
conv4 = Dropout(0.2)(conv4)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv4)
up2 = concatenate([UpSampling2D((2, 2))(conv4), conv1], axis=-1)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(up2)
conv5 = Dropout(0.2)(conv5)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv5)
out = Conv2D(256, (1, 1), padding='same')(conv5)
CNN_model = get_segmentation_model(img_input, out)
CNN_model.train(
train_images = train_x_filepath,
train_annotations = train_y_filepath,
val_images = test_x_filepath,
val_annotations = test_y_filepath,
checkpoints_path = checkpoints_filepath,
auto_resume_checkpoint=True,
epochs=5,
steps_per_epoch=128,
verify_dataset=False
)
out1 = CNN_model.predict_segmentation(
inp=example1,
out_fname="drive/Shared drives/Data Mining Project/Data/output1.png"
)