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)