9943f36fd3cf218775b735ddd41e2939487c0e0f,niftynet/application/autoencoder_application.py,AutoencoderApplication,initialise_dataset_loader,#AutoencoderApplication#Any#Any#Any#,38

Before Change

        // read each line of csv files into an instance of Subject
        if self.is_training:
            self.readers = [ImageReader(["image"])]
            if  self.action_param.validate_every_n:
                self.readers.append(ImageReader(["image"]))
        if self._infer_type in ("encode", "encode-decode"):
            self.readers = [ImageReader(["image"])]
        elif self._infer_type == "sample":
            self.readers = []
        elif self._infer_type == "linear_interpolation":
            self.readers = [ImageReader(["feature"])]

        file_list = data_partitioner.get_file_list()
        for reader in self.readers:
            reader.initialise(data_param, task_param, file_list)
        //if self.is_training or self._infer_type in ("encode", "encode-decode"):
        //    mean_var_normaliser = MeanVarNormalisationLayer(image_name="image")
        //    self.reader.add_preprocessing_layers([mean_var_normaliser])

    def initialise_sampler(self):
        self.sampler = []
        if self.is_training:
            self.sampler.append([ResizeSampler(

After Change

        // read each line of csv files into an instance of Subject
        if self.is_training:
            file_lists = []
            if self.action_param.validation_every_n > 0:
                file_lists.append(data_partitioner.train_files)
                file_lists.append(data_partitioner.validation_files)
            else:
                file_lists.append(data_partitioner.all_files)

            self.readers = []
            for file_list in file_lists:
                reader = ImageReader(["image"])
                reader.initialise(data_param, task_param, file_list)
                self.readers.append(reader)
        if self._infer_type in ("encode", "encode-decode"):
            self.readers = [ImageReader(["image"])]
            self.readers[0].initialise(data_param,
                                       task_param,
                                       data_partitioner.inference_files)
        elif self._infer_type == "sample":
            self.readers = []
        elif self._infer_type == "linear_interpolation":
            self.readers = [ImageReader(["feature"])]
            self.readers[0].initialise(data_param,
                                       task_param,
                                       data_partitioner.inference_files)


        //if self.is_training or self._infer_type in ("encode", "encode-decode"):

Instances