string_to_model.py 文件源码

def create_top_layer(self, phase=caffe.TRAIN, input_file="", train=True):
        if self.hp.GCN_APPROX:
            transform_param = {'scale': 0.0078125,'mean_value': 128}
        else:
            transform_param = {}
        if train:
            transform_param['mirror'] = self.hp.MIRROR
            if self.hp.CROP:
                # Adds random crops.
                transform_param['crop_size'] = self.hp.IMAGE_HEIGHT

        data, label = cl.Data(
            batch_size=self.hp.TRAIN_BATCH_SIZE if train else self.hp.EVAL_BATCH_SIZE, backend=P.Data.LMDB, name="data",
            source=input_file, ntop=2, include={'phase': phase}, transform_param=transform_param)
        return data, label

    # MAIN FUNCTION: Converts a net string to a caffe netspec.
    # Adds the data layer and accuracy layer for test/train.