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216 lines
9.6 KiB
216 lines
9.6 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import sys
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import numpy as np
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from .... import Executor
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from .... import io
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from .... import core
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from ....compiler import CompiledProgram
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from ....compiler import BuildStrategy
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from ....framework import IrGraph
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from ..core.strategy import Strategy
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from .quantization_pass import *
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__all__ = ['QuantizationStrategy']
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logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
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_logger = logging.getLogger(__name__)
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_logger.setLevel(logging.INFO)
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class QuantizationStrategy(Strategy):
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"""
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The strategy for Quantization.
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"""
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def __init__(self,
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start_epoch=0,
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end_epoch=0,
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float_model_save_path=None,
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mobile_model_save_path=None,
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int8_model_save_path=None,
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activation_bits=8,
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weight_bits=8,
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activation_quantize_type='abs_max',
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weight_quantize_type='abs_max',
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save_in_nodes=None,
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save_out_nodes=None):
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"""
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Args:
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start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
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end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
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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. defalut: None.
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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. defalut: None.
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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. defalut: None.
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activation_bits(int): quantization bit number for activation. default: 8.
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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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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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weight_quantize_type (str): quantization type for weights, support 'abs_max' and 'channel_wise_abs_max'.
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The 'range_abs_max' usually is not used for weight, since weights are fixed once the model is well trained.
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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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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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"""
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super(QuantizationStrategy, self).__init__(start_epoch, end_epoch)
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self.start_epoch = start_epoch
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self.end_epoch = end_epoch
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self.float_model_save_path = float_model_save_path
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self.mobile_model_save_path = mobile_model_save_path
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self.int8_model_save_path = int8_model_save_path
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self.activation_bits = activation_bits
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self.weight_bits = weight_bits
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self.activation_quantize_type = activation_quantize_type
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self.weight_quantize_type = weight_quantize_type
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self.save_out_nodes = save_out_nodes
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self.save_in_nodes = save_in_nodes
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def on_epoch_begin(self, context):
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"""
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Insert fake_quantize_op and fake_dequantize_op before trainging and testing.
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"""
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super(QuantizationStrategy, self).on_compression_begin(context)
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if self.start_epoch == context.epoch_id:
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_logger.info('QuantizationStrategy::on_epoch_begin')
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train_ir_graph = IrGraph(
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core.Graph(context.optimize_graph.program.desc), for_test=False)
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test_ir_graph = IrGraph(
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core.Graph(context.eval_graph.program.desc), for_test=True)
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transform_pass = QuantizationTransformPass(
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scope=context.scope,
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place=context.place,
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weight_bits=self.weight_bits,
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activation_bits=self.activation_bits,
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activation_quantize_type=self.activation_quantize_type,
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weight_quantize_type=self.weight_quantize_type)
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transform_pass.apply(train_ir_graph)
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transform_pass.apply(test_ir_graph)
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build_strategy = BuildStrategy()
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build_strategy.enable_inplace = False
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build_strategy.memory_optimize = False
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# for quantization training
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context.optimize_graph.compiled_graph = CompiledProgram(
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train_ir_graph.graph).with_data_parallel(
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loss_name=context.optimize_graph.out_nodes['loss'],
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build_strategy=build_strategy)
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# for evaluation. And program compiled from ir graph must be with data parallel.
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context.eval_graph.compiled_graph = CompiledProgram(
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test_ir_graph.graph).with_data_parallel(
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build_strategy=build_strategy)
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# for saving inference model after training
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context.put('quantization_test_ir_graph_backup', test_ir_graph)
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_logger.info('Finish QuantizationStrategy::on_epoch_begin')
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def on_epoch_end(self, context):
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"""
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Free and save inference model.
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"""
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super(QuantizationStrategy, self).on_compression_end(context)
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if context.epoch_id == self.end_epoch:
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_logger.info('QuantizationStrategy::on_epoch_end')
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test_ir_graph = context.get('quantization_test_ir_graph_backup')
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# freeze the graph after training
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freeze_pass = QuantizationFreezePass(
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scope=context.scope,
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place=context.place,
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weight_bits=self.weight_bits,
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activation_bits=self.activation_bits,
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weight_quantize_type=self.weight_quantize_type)
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freeze_pass.apply(test_ir_graph)
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# for other strategies
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context.eval_graph.program = test_ir_graph.to_program()
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if self.save_out_nodes == None:
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out_vars = [
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context.eval_graph.var(var_name)._var
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for var_name in context.eval_graph.out_nodes.values()
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]
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else:
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out_vars = [
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context.eval_graph.var(var_name)._var
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for var_name in self.save_out_nodes
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]
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if self.save_in_nodes == None:
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in_vars = list(context.eval_graph.out_nodes.values())
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else:
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in_vars = self.save_in_nodes
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# save float model
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if self.float_model_save_path:
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executor = Executor(context.place)
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io.save_inference_model(
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self.float_model_save_path,
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in_vars,
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out_vars,
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executor,
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main_program=test_ir_graph.to_program(),
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model_filename='model',
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params_filename='weights',
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export_for_deployment=True)
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# save int8 model
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if self.int8_model_save_path:
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convert_int8_pass = ConvertToInt8Pass(
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scope=context.scope, place=context.place)
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convert_int8_pass.apply(test_ir_graph)
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executor = Executor(context.place)
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io.save_inference_model(
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self.int8_model_save_path,
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in_vars,
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out_vars,
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executor,
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main_program=test_ir_graph.to_program(),
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model_filename='model',
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params_filename='weights',
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export_for_deployment=True)
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# save mobile model
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if self.mobile_model_save_path:
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if not self.int8_model_save_path:
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# convert the weights as int8_t type
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convert_int8_pass = ConvertToInt8Pass(
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scope=context.scope, place=context.place)
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convert_int8_pass.apply(test_ir_graph)
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# make some changes on the graph for the mobile inference
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mobile_pass = TransformForMobilePass()
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mobile_pass.apply(test_ir_graph)
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executor = Executor(context.place)
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io.save_inference_model(
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self.mobile_model_save_path,
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in_vars,
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out_vars,
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executor,
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main_program=test_ir_graph.to_program(),
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model_filename='model',
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params_filename='weights',
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export_for_deployment=True)
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_logger.info('Finish QuantizationStrategy::on_epoch_end')
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