fix keras

Signed-off-by: zehao-intel <[email protected]>

examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md

@@ -27,7 +27,7 @@ pip install -r requirements.txt
 
 The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
  ```
-python prepare_model.py --output_model=/path/to/model
+python prepare_model.py --output_model=./inception_v3_keras
  ```
 `--output_model ` the model should be saved as SavedModel format or H5 format.
 
@@ -40,9 +40,9 @@ python prepare_model.py --output_model=/path/to/model
   ```shell
   cd examples/3.x_api/tensorflow/keras/cv/
   # convert validation subset
-  bash prepare_dataset.sh --output_dir=/inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
-  bash prepare_dataset.sh --output_dir=/inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
   cd inception_v3/quantization/ptq
   ```
 > **Note**: 
@@ -55,7 +55,7 @@ python prepare_model.py --output_model=/path/to/model
 
 ## Quantization
   ```shell
-  bash run_quant.sh --input_model=./inception_v3_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
+  bash run_quant.sh --input_model=./inception_v3_keras --output_model=./result --dataset_location=/path/to/evaluation/dataset
   ```
 
 ## Benchmark

examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md

@@ -27,7 +27,7 @@ pip install -r requirements.txt
 
 The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
  ```
-python prepare_model.py --output_model=/path/to/model
+python prepare_model.py --output_model=./mobilenet_v2_keras
  ```
 `--output_model ` the model should be saved as SavedModel format or H5 format.
 
@@ -39,9 +39,9 @@ python prepare_model.py --output_model=/path/to/model
   ```shell
   cd examples/3.x_api/tensorflow/keras/cv/
   # convert validation subset
-  bash prepare_dataset.sh --output_dir=/mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  bash prepare_dataset.sh --output_dir=./mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
-  bash prepare_dataset.sh --output_dir=/mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  bash prepare_dataset.sh --output_dir=./mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
   cd mobilenet_v2/quantization/ptq
   ```
 > **Note**: 
@@ -54,7 +54,7 @@ python prepare_model.py --output_model=/path/to/model
 
 ## Quantization
   ```shell
-  bash run_quant.sh --input_model=./mobilenet_v2_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
+  bash run_quant.sh --input_model=./mobilenet_v2_keras --output_model=./result --dataset_location=/path/to/evaluation/dataset
   ```
 
 ## Benchmark

examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md

@@ -27,7 +27,7 @@ pip install -r requirements.txt
 
 The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
  ```
-python prepare_model.py --output_model=/path/to/model
+python prepare_model.py --output_model=./resnetv2_50_keras
  ```
 `--output_model ` the model should be saved as SavedModel format or H5 format.
 
@@ -39,9 +39,9 @@ python prepare_model.py --output_model=/path/to/model
   ```shell
   cd examples/3.x_api/tensorflow/keras/cv/
   # convert validation subset
-  bash prepare_dataset.sh --output_dir=/resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  bash prepare_dataset.sh --output_dir=./resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
-  bash prepare_dataset.sh --output_dir=/resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  bash prepare_dataset.sh --output_dir=./resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
   cd resnetv2_50/quantization/ptq
   ```
 > **Note**: 
@@ -54,7 +54,7 @@ python prepare_model.py --output_model=/path/to/model
 
 ## Quantization
   ```shell
-  bash run_quant.sh --input_model=./resnetv2_50_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
+  bash run_quant.sh --input_model=./resnetv2_50_keras --output_model=./result --dataset_location=/path/to/evaluation/dataset
   ```
 
 ## Benchmark

neural_compressor/tensorflow/algorithms/static_quant/keras.py

@@ -203,19 +203,20 @@ def _fuse_bn_keras2(self, fuse_conv_bn, fp32_layers):  # pragma: no cover
                         fuse_layers.append(layer)
                     else:
                         for bound_node in layer._inbound_nodes:
-                            inbound_layer = bound_node.inbound_layers
-                            if isinstance(inbound_layer, list) and len(inbound_layer) == 0:
-                                continue
-                            if inbound_layer in self.bn_weights.keys():
-                                for bn_inbound_node in inbound_layer._inbound_nodes:
-                                    bn_inbound_layer = bn_inbound_node.inbound_layers
-                                    if bn_inbound_layer.name in self.conv_weights.keys():
-                                        new_bound_nodes.append(bn_inbound_node)
-                                    else:
-                                        if bound_node not in new_bound_nodes:
-                                            new_bound_nodes.append(bound_node)
-                            else:
-                                new_bound_nodes.append(bound_node)
+                            inbound_layers = bound_node.inbound_layers
+                            if not isinstance(inbound_layers, list):
+                                inbound_layers = [inbound_layers]
+                            for inbound_layer in inbound_layers:
+                                if inbound_layer in self.bn_weights.keys():
+                                    for bn_inbound_node in inbound_layer._inbound_nodes:
+                                        bn_inbound_layer = bn_inbound_node.inbound_layers
+                                        if bn_inbound_layer.name in self.conv_weights.keys():
+                                            new_bound_nodes.append(bn_inbound_node)
+                                        else:
+                                            if bound_node not in new_bound_nodes:
+                                                new_bound_nodes.append(bound_node)
+                                else:
+                                    new_bound_nodes.append(bound_node)
 
                         layer._inbound_nodes.clear()
                         for bound_node in new_bound_nodes: