Revert "remove op-wise cfgs for testing. (#1521)" (#202)

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

Author: intel-zhangyi

examples/tensorflow/image_recognition/tensorflow_models/quantization/ptq/densenet121.yaml

@@ -38,6 +38,14 @@ quantization:                                        # optional. tuning constrai
       algorithm: minmax
     weight:
       granularity: per_channel
+  op_wise: {
+             'densenet121/MaxPool2D/MaxPool': {
+               'activation':  {'dtype': ['fp32']}
+             },
+             'densenet121/transition_block[1-3]/AvgPool2D/AvgPool': {
+               'activation':  {'dtype': ['fp32']},
+             }
+           }
 
 evaluation:                                          # optional. required if user doesn't provide eval_func in neural_compressor.Quantization.
   accuracy:                                          # optional. required if user doesn't provide eval_func in neural_compressor.Quantization.

examples/tensorflow/image_recognition/tensorflow_models/quantization/ptq/densenet161.yaml

@@ -38,6 +38,14 @@ quantization:                                        # optional. tuning constrai
       algorithm: minmax
     weight:
       granularity: per_channel
+  op_wise: {
+             'densenet161/MaxPool2D/MaxPool': {
+               'activation':  {'dtype': ['fp32']}
+             },
+             'densenet161/transition_block[1-3]/AvgPool2D/AvgPool': {
+               'activation':  {'dtype': ['fp32']},
+             }
+           }
 
 evaluation:                                          # optional. required if user doesn't provide eval_func in neural_compressor.Quantization.
   accuracy:                                          # optional. required if user doesn't provide eval_func in neural_compressor.Quantization.

examples/tensorflow/image_recognition/tensorflow_models/quantization/ptq/densenet169.yaml

@@ -38,6 +38,14 @@ quantization:                                        # optional. tuning constrai
       algorithm: minmax
     weight:
       granularity: per_channel
+  op_wise: {
+             'densenet169/MaxPool2D/MaxPool': {
+               'activation':  {'dtype': ['fp32']}
+             },
+             'densenet169/transition_block[1-3]/AvgPool2D/AvgPool': {
+               'activation':  {'dtype': ['fp32']},
+             }
+           }
 
 evaluation:                                          # optional. required if user doesn't provide eval_func in neural_compressor.Quantization.
   accuracy:                                          # optional. required if user doesn't provide eval_func in neural_compressor.Quantization.