!14330 Amend manual quant export and add resnet50_quant export.py

From: @zhang__sss
Reviewed-by: @zhoufeng54
Signed-off-by:

mindspore/compression/export/quant_export.py

@@ -228,10 +228,17 @@ class ExportManualQuantNetwork(ExportToQuantInferNetwork):
     __quant_op_name__ = ["Add", "Sub", "Mul", "RealDiv"]
 
     def __init__(self, network, mean, std_dev, *inputs, is_mindir=False):
-        super(ExportManualQuantNetwork, self).__init__(network, mean, std_dev, *inputs, is_mindir)
+        super(ExportManualQuantNetwork, self).__init__(network, mean, std_dev, *inputs, is_mindir=is_mindir)
         self.upcell = None
         self.upname = None
 
+    def _add_output_min_max_for_op(self, origin_op, fake_quant_cell):
+        if self.is_mindir:
+            np_type = mstype.dtype_to_nptype(self.data_type)
+            _, _, maxq, minq = quant_utils.scale_zp_max_min_from_fake_quant_cell(fake_quant_cell, np_type)
+            origin_op.add_prim_attr('output_maxq', Tensor(maxq))
+            origin_op.add_prim_attr('output_minq', Tensor(minq))
+
     def _convert_quant2deploy(self, network):
         """Convert network's all quant subcell to deploy subcell."""
         cells = network.name_cells()
@@ -247,18 +254,31 @@ class ExportManualQuantNetwork(ExportToQuantInferNetwork):
             elif isinstance(subcell, (quant.Conv2dBnFoldQuant, quant.Conv2dBnWithoutFoldQuant,
                                       quant.Conv2dQuant, quant.DenseQuant)):
                 network, change = self._convert_subcell(network, change, name, subcell, core=False)
-            elif isinstance(subcell, quant.FakeQuantWithMinMaxObserver) and self.upcell:
-                np_type = mstype.dtype_to_nptype(self.data_type)
-                _, _, maxq, minq = quant_utils.scale_zp_max_min_from_fake_quant_cell(subcell, np_type)
-                self.upcell.core_op.add_prim_attr('output_maxq', Tensor(maxq))
-                self.upcell.core_op.add_prim_attr('output_minq', Tensor(minq))
-                network.insert_child_to_cell(self.upname, self.upcell)
+            elif isinstance(subcell, nn.ActQuant) and hasattr(subcell, "get_origin"):
+                if self.upcell:
+                    self._add_output_min_max_for_op(self.upcell.core_op, subcell.fake_quant_act)
+                activation = subcell.get_origin()
+                network.insert_child_to_cell(name, activation)
+                change = True
+            elif isinstance(subcell, nn.TensorAddQuant):
+                if isinstance(subcell.add, _AddFakeQuantAfterSubCell):
+                    add_op = subcell.add.subcell
+                    subcell.__delattr__("add")
+                    subcell.__setattr__("add", add_op)
+                add_op = subcell.add
+                if add_op:
+                    self._add_output_min_max_for_op(add_op, subcell.fake_quant_act)
+                subcell.__delattr__("fake_quant_act")
+                subcell.__setattr__("fake_quant_act", P.identity())
+            elif isinstance(subcell, quant.FakeQuantWithMinMaxObserver):
+                if self.upcell:
+                    self._add_output_min_max_for_op(self.upcell.core_op, subcell)
+                network.__delattr__(name)
+                network.__setattr__(name, P.identity())
             elif isinstance(subcell, _AddFakeQuantAfterSubCell):
                 op = subcell.subcell
                 if op.name in QuantizationAwareTraining.__quant_op_name__ and isinstance(op, ops.Primitive):
-                    if self.is_mindir:
-                        op.add_prim_attr('output_maxq', Tensor(subcell.fake_quant_act.maxq.data.asnumpy()))
-                        op.add_prim_attr('output_minq', Tensor(subcell.fake_quant_act.minq.data.asnumpy()))
+                    self._add_output_min_max_for_op(op, subcell.fake_quant_act)
                     network.__delattr__(name)
                     network.__setattr__(name, op)
                     change = True
@@ -271,10 +291,13 @@ class ExportManualQuantNetwork(ExportToQuantInferNetwork):
 
     def _convert_subcell(self, network, change, name, subcell, core=True, conv=True):
         """Convert subcell to ant subcell."""
+        new_subcell = None
         if core:
             cell_core = subcell.conv if conv else subcell.dense
             activation = subcell.activation
+            if hasattr(activation, 'fake_quant_act'):
                 fake_quant_act = activation.fake_quant_act
+                new_subcell = self._get_quant_block(cell_core, activation, fake_quant_act)
         else:
             cell_core = subcell
             activation = None

model_zoo/official/cv/resnet50_quant/README.md

@@ -87,6 +87,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
   │   ├──crossentropy.py     # define the crossentropy of resnet50-quant
   ├── train.py      # training script
   ├── eval.py       # evaluation script
+  ├── export.py     # export script
 
 ```
 

model_zoo/official/cv/resnet50_quant/README_CN.md

@@ -95,6 +95,7 @@ ResNet-50总体网络架构如下：
   │   ├──crossentropy.py # 定义ResNet-50-Quant的交叉熵
   ├── train.py # 训练脚本
   ├── eval.py # 评估脚本
+  ├── export.py # 导出脚本
 
 ```
 

model_zoo/official/cv/resnet50_quant/export.py

@@ -0,0 +1,53 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Export Resnet50 on ImageNet"""
+
+import argparse
+import numpy as np
+
+import mindspore
+from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
+from mindspore.compression.quant import QuantizationAwareTraining
+
+from models.resnet_quant_manual import resnet50_quant
+from src.config import config_quant
+
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
+parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
+parser.add_argument('--device_target', type=str, default=None, help='Run device target')
+args_opt = parser.parse_args()
+
+if __name__ == '__main__':
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
+    # define fusion network
+    network = resnet50_quant(class_num=config_quant.class_num)
+    # convert fusion network to quantization aware network
+    quantizer = QuantizationAwareTraining(bn_fold=True,
+                                          per_channel=[True, False],
+                                          symmetric=[True, False])
+    network = quantizer.quantize(network)
+    # load checkpoint
+    if args_opt.checkpoint_path:
+        param_dict = load_checkpoint(args_opt.checkpoint_path)
+        not_load_param = load_param_into_net(network, param_dict)
+        if not_load_param:
+            raise ValueError("Load param into network fail!")
+    # export network
+    print("============== Starting export ==============")
+    inputs = Tensor(np.ones([1, 3, 224, 224]), mindspore.float32)
+    export(network, inputs, file_name="resnet50_quant", file_format=args_opt.file_format,
+           quant_mode='MANUAL', mean=0., std_dev=48.106)
+    print("============== End export ==============")