add manual quant network of resnet

mindspore/train/quant/quant_utils.py

@@ -252,13 +252,14 @@ def without_fold_batchnorm(weight, cell_quant):
     return weight, bias
 
 
-def load_nonquant_param_into_quant_net(quant_model, params_dict):
+def load_nonquant_param_into_quant_net(quant_model, params_dict, quant_new_params=None):
     """
     load fp32 model parameters to quantization model.
 
     Args:
-        quant_model: quantization model
-        params_dict: f32 param
+        quant_model: quantization model.
+        params_dict: f32 param.
+        quant_new_params:parameters that exist in quantative network but not in unquantative network.
 
     Returns:
         None
@@ -277,6 +278,8 @@ def load_nonquant_param_into_quant_net(quant_model, params_dict):
     for name, param in quant_model.parameters_and_names():
         key_name = name.split(".")[-1]
         if key_name not in iterable_dict.keys():
+            if quant_new_params is not None and key_name in quant_new_params:
+                continue
             raise ValueError(f"Can't find match parameter in ckpt,param name = {name}")
         value_param = next(iterable_dict[key_name], None)
         if value_param is not None:

model_zoo/official/cv/resnet50_quant/eval.py

@@ -20,7 +20,8 @@ import argparse
 from src.config import config_quant
 from src.dataset import create_dataset
 from src.crossentropy import CrossEntropy
-from models.resnet_quant import resnet50_quant
+#from models.resnet_quant import resnet50_quant #auto construct quantative network of resnet50
+from models.resnet_quant_manual import resnet50_quant #manually construct quantative network of resnet50
 
 from mindspore import context
 from mindspore.train.model import Model

model_zoo/official/cv/resnet50_quant/models/resnet_quant.py

@@ -209,7 +209,7 @@ class ResNet(nn.Cell):
         return out
 
 
-def resnet50_quant(class_num=10001):
+def resnet50_quant(class_num=10):
     """
     Get ResNet50 neural network.
 

model_zoo/official/cv/resnet50_quant/train.py

@@ -31,7 +31,8 @@ from mindspore.communication.management import init
 import mindspore.nn as nn
 import mindspore.common.initializer as weight_init
 
-from models.resnet_quant import resnet50_quant
+#from models.resnet_quant import resnet50_quant #auto construct quantative network of resnet50
+from models.resnet_quant_manual import resnet50_quant #manually construct quantative network of resnet50
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
 from src.config import config_quant
@@ -85,7 +86,7 @@ if __name__ == '__main__':
     # weight init and load checkpoint file
     if args_opt.pre_trained:
         param_dict = load_checkpoint(args_opt.pre_trained)
-        load_nonquant_param_into_quant_net(net, param_dict)
+        load_nonquant_param_into_quant_net(net, param_dict, ['step'])
         epoch_size = config.epoch_size - config.pretrained_epoch_size
     else:
         for _, cell in net.cells_and_names():