Caffe - Rewrite Accuracy layer as a Python layer

accuracy_layer.py

import caffe
import json

class AccuracyLayer(caffe.Layer):
    """
    Rewrite Accuracy layer as a Python layer

    Accepts JSON-encoded parameters through param_str

    Use like this:

    layer {
        name: "accuracy"
        type: "Python"
        bottom: "pred"
        bottom: "label"
        top: "accuracy"
        include {
            phase: TEST
        }
        python_param {
            module: "accuracy_layer"
            layer: "AccuracyLayer"
            param_str: "{\"top_k\": 2}"
        }
    }
    """
    
    def setup(self, bottom, top):
        assert len(bottom) == 2,    'requires two layer.bottoms'
        assert len(top) == 1,       'requires a single layer.top'
    
        if hasattr(self, 'param_str') and self.param_str:
            params = json.loads(self.param_str)
        else:
            params = {}

        self.top_k = params.get('top_k', 1)
    
    def reshape(self, bottom, top):
        top[0].reshape(1)
    
    def forward(self, bottom, top):
        # Renaming for clarity
        predictions = bottom[0].data
        ground_truth = bottom[1].data
    
        num_correct = 0.0
    
        # NumPy magic - get top K predictions for each datum
        top_predictions = (-predictions).argsort()[:, :self.top_k]
        for batch_index, predictions in enumerate(top_predictions):
            if ground_truth[batch_index] in predictions:
                num_correct += 1
    
        # Accuracy is averaged over the batch
        top[0].data[0] = num_correct / len(ground_truth)
    
    def backward(self, top, propagate_down, bottom):
        pass

What happens during the test phase,
Will it get averaged over all the iterations?