504d63d25cfd42e650834d8db6d976103e5f4baa,autokeras/preprocessor.py,DataTransformer,transform_test,#DataTransformer#Any#Any#Any#,76

Before Change

        common_list = [Normalize(torch.Tensor(self.mean), torch.Tensor(self.std))]
        compose_list = common_list

        return self._transform(batch_size, compose_list, data, targets)

    def _transform(self, batch_size, compose_list, data, targets):
        if batch_size is None:
            batch_size = Constant.MAX_BATCH_SIZE

After Change

        common_list = [Normalize(torch.Tensor(self.mean), torch.Tensor(self.std))]
        compose_list = common_list

        dataset = self._transform(compose_list, data, targets)

        if batch_size is None:
            batch_size = Constant.MAX_BATCH_SIZE
        batch_size = min(len(data), batch_size)

        return DataLoader(dataset, batch_size=batch_size, shuffle=False)

    def _transform(self, compose_list, data, targets):
        data = data / self.max_val
        data = torch.Tensor(data.transpose(0, 3, 1, 2))

Instances