add config file to avoid load checkpoint test=develop (#21373)
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#start_epoch(int): The epoch to insert quantization operators. default: 0
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#
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#end_epoch(int): The epoch to save inference model. default: 0
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#
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#float_model_save_path(str): The path to save model with float weights.
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# None means it doesn't save float model. default: None.
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#
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#mobile_model_save_path(str): The path to save model for paddle-mobile execution.
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# None means it doesn't save mobile model. default: None.
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#
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#int8_model_save_path(str): The path to save model with int8_t weight.
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# None means it doesn't save int8 model. default: None.
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#
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#activation_bits(int): quantization bit number for activation. default: 8.
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#
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#weight_bits(int): quantization bit number for weights. The bias is not quantized.
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# default: 8.
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#
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#activation_quantize_type(str): quantization type for activation,
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# now support 'abs_max', 'range_abs_max' and 'moving_average_abs_max'.
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# If use 'abs_max' mode, the quantization scale will be calculated
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# dynamically each step in both training and testing period. If use
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# 'range_abs_max', a static quantization scale will be calculated
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# during training and used in inference.
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#
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#save_in_nodes(list<str>): A list of variable names used to prune graph
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# for saving inference model.
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#
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#save_out_nodes(list<str>): A list of variable names used to prune graph
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# for saving inference model.
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version: 1.0
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strategies:
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quantization_strategy:
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class: 'QuantizationStrategy'
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start_epoch: 0
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end_epoch: 0
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float_model_save_path: './output/float'
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mobile_model_save_path: './output/mobile'
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int8_model_save_path: './output/int8'
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weight_bits: 8
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activation_bits: 8
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weight_quantize_type: 'abs_max'
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activation_quantize_type: 'abs_max'
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save_in_nodes: ['image']
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save_out_nodes: ['quan.tmp_2']
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compressor:
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epoch: 2
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checkpoint_path: './checkpoints_quan_2/'
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strategies:
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- quantization_strategy
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