forked from OSSInnovation/mindspore
!14330 Amend manual quant export and add resnet50_quant export.py
From: @zhang__sss Reviewed-by: @zhoufeng54 Signed-off-by:
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f47767b361
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@ -228,10 +228,17 @@ class ExportManualQuantNetwork(ExportToQuantInferNetwork):
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__quant_op_name__ = ["Add", "Sub", "Mul", "RealDiv"]
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def __init__(self, network, mean, std_dev, *inputs, is_mindir=False):
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super(ExportManualQuantNetwork, self).__init__(network, mean, std_dev, *inputs, is_mindir)
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super(ExportManualQuantNetwork, self).__init__(network, mean, std_dev, *inputs, is_mindir=is_mindir)
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self.upcell = None
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self.upname = None
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def _add_output_min_max_for_op(self, origin_op, fake_quant_cell):
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if self.is_mindir:
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np_type = mstype.dtype_to_nptype(self.data_type)
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_, _, maxq, minq = quant_utils.scale_zp_max_min_from_fake_quant_cell(fake_quant_cell, np_type)
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origin_op.add_prim_attr('output_maxq', Tensor(maxq))
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origin_op.add_prim_attr('output_minq', Tensor(minq))
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def _convert_quant2deploy(self, network):
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"""Convert network's all quant subcell to deploy subcell."""
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cells = network.name_cells()
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@ -247,18 +254,31 @@ class ExportManualQuantNetwork(ExportToQuantInferNetwork):
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elif isinstance(subcell, (quant.Conv2dBnFoldQuant, quant.Conv2dBnWithoutFoldQuant,
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quant.Conv2dQuant, quant.DenseQuant)):
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network, change = self._convert_subcell(network, change, name, subcell, core=False)
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elif isinstance(subcell, quant.FakeQuantWithMinMaxObserver) and self.upcell:
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np_type = mstype.dtype_to_nptype(self.data_type)
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_, _, maxq, minq = quant_utils.scale_zp_max_min_from_fake_quant_cell(subcell, np_type)
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self.upcell.core_op.add_prim_attr('output_maxq', Tensor(maxq))
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self.upcell.core_op.add_prim_attr('output_minq', Tensor(minq))
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network.insert_child_to_cell(self.upname, self.upcell)
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elif isinstance(subcell, nn.ActQuant) and hasattr(subcell, "get_origin"):
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if self.upcell:
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self._add_output_min_max_for_op(self.upcell.core_op, subcell.fake_quant_act)
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activation = subcell.get_origin()
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network.insert_child_to_cell(name, activation)
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change = True
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elif isinstance(subcell, nn.TensorAddQuant):
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if isinstance(subcell.add, _AddFakeQuantAfterSubCell):
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add_op = subcell.add.subcell
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subcell.__delattr__("add")
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subcell.__setattr__("add", add_op)
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add_op = subcell.add
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if add_op:
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self._add_output_min_max_for_op(add_op, subcell.fake_quant_act)
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subcell.__delattr__("fake_quant_act")
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subcell.__setattr__("fake_quant_act", P.identity())
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elif isinstance(subcell, quant.FakeQuantWithMinMaxObserver):
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if self.upcell:
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self._add_output_min_max_for_op(self.upcell.core_op, subcell)
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network.__delattr__(name)
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network.__setattr__(name, P.identity())
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elif isinstance(subcell, _AddFakeQuantAfterSubCell):
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op = subcell.subcell
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if op.name in QuantizationAwareTraining.__quant_op_name__ and isinstance(op, ops.Primitive):
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if self.is_mindir:
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op.add_prim_attr('output_maxq', Tensor(subcell.fake_quant_act.maxq.data.asnumpy()))
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op.add_prim_attr('output_minq', Tensor(subcell.fake_quant_act.minq.data.asnumpy()))
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self._add_output_min_max_for_op(op, subcell.fake_quant_act)
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network.__delattr__(name)
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network.__setattr__(name, op)
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change = True
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@ -271,15 +291,18 @@ class ExportManualQuantNetwork(ExportToQuantInferNetwork):
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def _convert_subcell(self, network, change, name, subcell, core=True, conv=True):
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"""Convert subcell to ant subcell."""
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new_subcell = None
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if core:
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cell_core = subcell.conv if conv else subcell.dense
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activation = subcell.activation
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fake_quant_act = activation.fake_quant_act
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if hasattr(activation, 'fake_quant_act'):
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fake_quant_act = activation.fake_quant_act
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new_subcell = self._get_quant_block(cell_core, activation, fake_quant_act)
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else:
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cell_core = subcell
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activation = None
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fake_quant_act = None
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new_subcell = self._get_quant_block(cell_core, activation, fake_quant_act)
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new_subcell = self._get_quant_block(cell_core, activation, fake_quant_act)
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if new_subcell:
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prefix = subcell.param_prefix
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new_subcell.update_parameters_name(prefix + '.')
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@ -87,6 +87,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
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│ ├──crossentropy.py # define the crossentropy of resnet50-quant
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├── train.py # training script
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├── eval.py # evaluation script
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├── export.py # export script
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```
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@ -95,6 +95,7 @@ ResNet-50总体网络架构如下：
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│ ├──crossentropy.py # 定义ResNet-50-Quant的交叉熵
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├── train.py # 训练脚本
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├── eval.py # 评估脚本
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├── export.py # 导出脚本
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```
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@ -0,0 +1,53 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""Export Resnet50 on ImageNet"""
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import argparse
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import numpy as np
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import mindspore
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from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
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from mindspore.compression.quant import QuantizationAwareTraining
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from models.resnet_quant_manual import resnet50_quant
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from src.config import config_quant
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
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parser.add_argument('--device_target', type=str, default=None, help='Run device target')
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args_opt = parser.parse_args()
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if __name__ == '__main__':
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
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# define fusion network
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network = resnet50_quant(class_num=config_quant.class_num)
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# convert fusion network to quantization aware network
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quantizer = QuantizationAwareTraining(bn_fold=True,
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per_channel=[True, False],
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symmetric=[True, False])
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network = quantizer.quantize(network)
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# load checkpoint
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if args_opt.checkpoint_path:
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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not_load_param = load_param_into_net(network, param_dict)
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if not_load_param:
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raise ValueError("Load param into network fail!")
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# export network
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print("============== Starting export ==============")
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inputs = Tensor(np.ones([1, 3, 224, 224]), mindspore.float32)
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export(network, inputs, file_name="resnet50_quant", file_format=args_opt.file_format,
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quant_mode='MANUAL', mean=0., std_dev=48.106)
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print("============== End export ==============")
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