def eval_classif()

in src/evaluator.py [0:0]

• 14 lines of code
• 8 McCabe index (conditional complexity)

    def eval_classif(self, data_type, scores, data_loader):
        """
        Evaluate classification.
        """
        params = self.params
        self.model.eval()

        # stats
        accuracies = []

        # memories
        topk = [1, 5, 10, 20, 50, 100, 200, 500]
        topk = [k for k in topk if k <= params.num_classes]

        for images, targets in data_loader:
            images = images.cuda().half() if params.fp16 else images.cuda()
            if self.ftmodel is not None:
                images = self.ftmodel(images)

            output = self.model(images)
            accuracies.append(accuracy(output.cpu(), targets, topk=tuple(topk)))

        # accuracy
        for i_k, k in enumerate(topk):
            scores['top%d_acc' % k] = np.mean([x[i_k] for x in accuracies])