Fix the error of save_quantized_model (#30587)

动态图中Conv2D保存成预测模型时，对应的Op可能是conv2d，也可能是depthwise_conv2d，但目前的save_quantized_model接口并未考虑depthwise_conv2d情况，可能会致使out_scale的值保存错误，该PR主要是修复这个问题。

Author: gfwm2013

python/paddle/fluid/contrib/slim/quantization/imperative/qat.py

@@ -36,7 +36,7 @@
 
 _op_real_in_out_name = {
     "conv2d": [["Input", "Filter"], ["Output"]],
-    "conv2d_transpose": [["Input", "Filter"], ["Output"]],
+    "depthwise_conv2d": [["Input", "Filter"], ["Output"]],
     "pool2d": [["X"], ["Out"]],
     "elementwise_add": [["X", "Y"], ["Out"]],
     "softmax": [["X"], ["Out"]],
@@ -329,9 +329,9 @@ def __init__(self, moving_rate=0.9):
         super(ImperativeCalcOutScale, self).__init__()
         self._moving_rate = moving_rate
         self._out_scale_layer_type_list = (
-            BatchNorm, BatchNorm1D, BatchNorm2D, BatchNorm3D, Conv2D,
-            Conv2DTranspose, LeakyReLU, Linear, PReLU, Pool2D, MaxPool1D,
-            MaxPool2D, ReLU, ReLU6, Sigmoid, Softmax, Tanh, Swish)
+            BatchNorm, BatchNorm1D, BatchNorm2D, BatchNorm3D, Conv2D, LeakyReLU,
+            Linear, PReLU, Pool2D, MaxPool1D, MaxPool2D, ReLU, ReLU6, Sigmoid,
+            Softmax, Tanh, Swish)
         self._register_hook_handle_list = []
         self._out_scale_dict = collections.OrderedDict()
 
@@ -415,9 +415,10 @@ def save_quantized_model(self, layer, path, input_spec=None, **config):
 
         # Traverse all ops in the program and find out the op matching
         # the Layer in the dynamic graph.
-        layer_var_dict = {}
+        layer_var_dict = collections.OrderedDict()
         ops_list = [key for key, _ in self._out_scale_dict.items()]
         op_count = 0
+        conv_count = 0
         for block in inference_program.blocks:
             for op in block.ops:
                 if op.type in _op_real_in_out_name:
@@ -472,6 +473,9 @@ def save_quantized_model(self, layer, path, input_spec=None, **config):
                 layer_name = layer_name.replace('prelu', 'p_re_lu')
             if 'relu' in layer_name:
                 layer_name = layer_name.replace('relu', 're_lu')
+            if 'conv2d' in layer_name:
+                layer_name = 'conv2d_' + str(conv_count)
+                conv_count = conv_count + 1
             if layer_name not in self._out_scale_dict:
                 continue
             var_name_op_list[1]._set_attr('out_threshold',