# step1: Pruning at epoch-0
# step2: Fine-tune from epoch-0 to epoch-120
# step3: Quantization training from epoch-121 to epoch-141
version: 1.0
pruners:
    pruner_1:
        class: 'StructurePruner'
        pruning_axis:
            '*': 0
        criterions:
            '*': 'l1_norm'
strategies:
    uniform_pruning_strategy:
        class: 'UniformPruneStrategy'
        pruner: 'pruner_1'
        start_epoch: 0
        target_ratio: 0.5
        pruned_params: '.*_sep_weights'
        metric_name: 'acc_top1'
    quantization_strategy:
        class: 'QuantizationStrategy'
        start_epoch: 121
        end_epoch: 141
        float_model_save_path: './output/float'
        mobile_model_save_path: './output/mobile'
        int8_model_save_path: './output/int8'
        weight_bits: 8
        activation_bits: 8
        weight_quantize_type: 'abs_max'
        activation_quantize_type: 'abs_max'
compressor:
    epoch: 142
    #init_model: './checkpoints/0' # Please enable this option for loading checkpoint.
    checkpoint_path: './checkpoints/'
    strategies:
        - uniform_pruning_strategy
        - quantization_strategy