model: {name: model_name, framework: model_framework, inputs: input_tensor, outputs: softmax_tensor}
quantization:
    calibration:
        sampling_size: '100'
        dataloader:
            batch_size: 1
            dataset:
                ImageRecord: {root: /path/to/calib/dataset}
            transform:
                ResizeCropImagenet:
                    height: 224
                    width: 224
                    mean_value: [123.68, 116.78, 103.94]
    model_wise:
        activation: {algorithm: minmax}
    approach: post_training_static_quant
tuning:
    strategy: {name: basic}
    accuracy_criterion: {relative: 0.01}
    objective: performance
    exit_policy: {timeout: 0, max_trials: 100}
    random_seed: 9527
    workspace: {path: /path/to/workspace/workloads/resnet50_v1_33a86499061d6f355f1f158e70f60c7829d138e24b4eff41ffeef300524dd029}
evaluation:
    accuracy:
        metric: {topk: 1}
        dataloader:
            batch_size: 32
            dataset:
                ImageRecord: {root: /path/to/eval/dataset}
            transform:
                ResizeCropImagenet:
                    height: 224
                    width: 224
                    mean_value: [123.68, 116.78, 103.94]
    performance:
        warmup: 10
        iteration: -1
        configs: {cores_per_instance: 4, num_of_instance: 7}
        dataloader:
            batch_size: 1
            dataset:
                ImageRecord: {root: /path/to/eval/dataset}
            transform:
                ResizeCropImagenet:
                    height: 224
                    width: 224
                    mean_value: [123.68, 116.78, 103.94]
