| import gdown | |
| import logging as log | |
| from .unet3d import UNet3D | |
| from .fetch import download | |
| import os | |
| from tensorflow.python.keras.models import load_model | |
| def get_model(output): | |
| url = "https://github.com/andreped/livermask/releases/download/trained-models-v1/model.h5" | |
| if os.path.exists(output): | |
| try: | |
| model = load_model(output, compile=False) | |
| del model | |
| except OSError as e: | |
| print(e) | |
| print("Model failed to load. Trying to delete old model and redownload. This might take a while...") | |
| os.remove(output) | |
| download(url, output) | |
| else: | |
| download(url, output) | |
| def get_vessel_model(output): | |
| url = "https://drive.google.com/uc?id=1-8VNoRmIeiF1uIuWBqmZXz_6dIQFSAxN" | |
| gdown.cached_download(url, output) | |
| def load_vessel_model(path, cpu): | |
| import chainer | |
| unet = UNet3D(num_of_label=2) | |
| chainer.serializers.load_npz(path, unet) | |
| if not cpu: | |
| chainer.cuda.get_device_from_id(0).use() | |
| unet.to_gpu() | |
| xp = unet.xp | |
| return unet, xp | |
| def verboseHandler(verbose): | |
| if verbose: | |
| log.basicConfig(format="%(levelname)s: %(message)s", level=log.DEBUG) | |
| log.info("Verbose output.") | |
| else: | |
| log.basicConfig(format="%(levelname)s: %(message)s") | |
| return log | |