def test_unet(): data = mnist_data() backbone = ResNet() # preds = backbone(data.get_test()[0]) gen = Unet() # input_shape = gen.get_input_shape() # print(gen.get_output_shape()) rand_data_shape = (50, 28, 28, 1) random_noise_data = np.random.normal(size=rand_data_shape) # import pdb; pdb.set_trace() # breakpoint 7e7a66fc // preds = gen.predict(random_noise_data) return True
def test_unet_model(self): image = Raster \ .read \ .format("geotiff") \ .load(TEST_IMAGE_PATH) label: Raster = Raster \ .read \ .format("shp") \ .options( pixel=image.pixel, extent=image.extent ) \ .load(self.shape_path) standarize1 = ImageStand(raster=image) standarized = standarize1.standarize_image(StandardScaler()) raster_data = RasterData(standarized, label) unet_images = raster_data.prepare_unet_data(image_size=[64, 64]) callbacks = [ EarlyStopping(patience=100, verbose=1), ReduceLROnPlateau(factor=0.1, patience=100, min_lr=0, verbose=1), ModelCheckpoint('model_more_class_pixels.h5', verbose=1, save_best_only=True, save_weights_only=False) ] config = UnetConfig( input_size=[64, 64, 3], metrics=["accuracy"], optimizer=SGD(lr=0.001), callbacks=callbacks, loss="binary_crossentropy", ) # unet = Unet(config=config) unet.compile() unet.fit(unet_images, epochs=1) predicted = unet.predict(x=unet_images.x_test[0], threshold=0.4) SubPlots().extend(predicted, unet_images.x_test[0]).plot(nrows=1)