e21ef336207b0f9ae378c77430d298945827830a,art/classifiers/pytorch.py,PyTorchClassifier,get_activations,#PyTorchClassifier#Any#Any#Any#,338

Before Change

        else:
            raise TypeError("Layer must be of type str or int")

        return model_outputs[layer_index].detach().cpu().numpy()

    def set_learning_phase(self, train):
        
        Set the learning phase for the backend framework.

After Change

        num_batch = int(np.ceil(len(x_) / float(batch_size)))
        for m in range(num_batch):
            // Batch indexes
            begin, end = m * batch_size, min((m + 1) * batch_size, x_.shape[0])

            // Run prediction for the current batch
            layer_output = self._model(torch.from_numpy(x_[begin:end]).to(self._device).float())[layer_index]
            results.append(layer_output.detach().cpu().numpy())

Instances