move stuff from old VGG19transformer folder in file VGG19transformer

skluc/main/data/transformation/VGG19Transformer.py

+from keras import Model
+from keras.models import load_model
+
+from skluc.main.data.mldatasets.Cifar10Dataset import Cifar10Dataset
+from skluc.main.data.transformation.KerasModelTransformer import KerasModelTransformer
+from skluc.main.utils import logger, create_directory, download_data, check_file_md5, Singleton, DownloadableModel
+
+
+class VGG19Transformer(KerasModelTransformer, metaclass=Singleton):
+    """
+    Uses the vgg19 convolution network to transform data.
+    """
+
+    MAP_DATA_MODEL = {
+        "svhn": DownloadableModel(
+            url="https://pageperso.lis-lab.fr/~luc.giffon/models/1529968150.5454917_vgg19_svhn.h5",
+            checksum="563a9ec2aad37459bd1ed0e329441b05"
+        ),
+        "cifar100": DownloadableModel(
+            url="https://pageperso.lis-lab.fr/~luc.giffon/models/1530965727.781668_vgg19_cifar100fine.h5",
+            checksum="edf43e263fec05e2c013dd5a2128fc38"
+        ),
+        "cifar10": DownloadableModel(
+            url="https://pageperso.lis-lab.fr/~luc.giffon/models/1522967518.1916964_vgg19_cifar10.h5",
+            checksum="0dbb4f02ceb1f4acb6e24831758106e5"
+        ),
+        "siamese_omniglot_28x28": DownloadableModel(
+            url="https://pageperso.lis-lab.fr/~luc.giffon/models/1536244775.6502118_siamese_vgg19_omniglot_28x28_conv.h5",
+            checksum="90aec06e688ec3248ba89544a10c9f1f"
+        ),
+        "omniglot_28x28": DownloadableModel(
+            url="https://pageperso.lis-lab.fr/~luc.giffon/models/1536764034.66037_vgg19_omniglot.h5",
+            checksum="ef1272e9c7ce070e8f70889ec58d1c33"
+        )
+    }
+
+    def __init__(self, data_name, cut_layer_name=None, cut_layer_index=None):
+        if data_name not in self.MAP_DATA_MODEL.keys():
+            raise ValueError("Unknown data name. Can't load weights")
+
+        if cut_layer_name is None and cut_layer_index is None:
+            logger.warning(
+                "Cut layer chosen automatically but it eventually will lead to an error in future: block5_pool should be specified explicitly")
+            cut_layer_name = "block5_pool"
+        if cut_layer_name is not None:
+            transformation_name = str(data_name) + "_" + self.__class__.__name__ + "_" + str(cut_layer_name)
+        elif cut_layer_index is not None:
+            transformation_name = str(data_name) + "_" + self.__class__.__name__ \
+                                  + "_" + str(cut_layer_index)
+            # todo sauvegarder index / nom dans le meme dossier si c'est les meme
+        else:
+            raise AttributeError("Cut layer name or cut_layer index must be given to init VGG19Transformer.")
+        self.__cut_layer_name = cut_layer_name
+        self.__cut_layer_index = cut_layer_index
+
+        self.keras_model = None
+
+        super().__init__(data_name=data_name,
+                         transformation_name=transformation_name)
+
+    def load(self):
+        create_directory(self.s_download_dir)
+        s_model_path = download_data(self.MAP_DATA_MODEL[self.data_name].url, self.s_download_dir)
+        check_file_md5(s_model_path, self.MAP_DATA_MODEL[self.data_name].checksum)
+        if self.keras_model is None:
+            logger.debug("Loading VGG19 model for {} transformation with {} weights".format(self.transformation_name, self.data_name))
+            self.keras_model = load_model(s_model_path)
+
+            logger.debug("Layers of model {}".format([l.name for l in self.keras_model.layers]))
+
+            if self.__cut_layer_index is not None:
+                cut_layer = self.keras_model.layers[-1]
+                self.__cut_layer_name = cut_layer.name
+                logger.debug(
+                    "Found associated layer {} to layer index {}".format(self.__cut_layer_name, self.__cut_layer_index))
+
+            self.keras_model = Model(inputs=self.keras_model.input,
+                                     outputs=self.keras_model.get_layer(name=self.__cut_layer_name).output)
+
+        else:
+            logger.debug("Skip loading model VGG19 for {} transformation with {} weights. Already there.".format(
+                self.transformation_name,
+                self.data_name))
+
+
+if __name__ == '__main__':
+    valsize = 10000
+    d = Cifar10Dataset(validation_size=valsize)
+
+    d.load()
+    d.to_image()
+    trans = VGG19Transformer(data_name="cifar10", cut_layer_name="block5_pool")
+    d.apply_transformer(transformer=trans)