Merge branch 'master' of http://62.234.201.16/hao/Model-Transfer-Adaptability

mzh/new_mzh/ResNet18-50-152/model.py

@@ -72,8 +72,9 @@ class ResNet(nn.Module):
         self.layer2.quantize(quant_type=quant_type,num_bits=num_bits, e_bits=e_bits)
         self.layer3.quantize(quant_type=quant_type,num_bits=num_bits, e_bits=e_bits)
         self.layer4.quantize(quant_type=quant_type,num_bits=num_bits, e_bits=e_bits)
-        self.qavgpool1 = QAdaptiveAvgPool2d(quant_type,qi=False,qo=True,num_bits=num_bits,e_bits=e_bits)
-        self.qfc1 = QLinear(quant_type, self.fc,qi=False,qo=True,num_bits=num_bits,e_bits=e_bits)
+        self.qavgpool1 = QAdaptiveAvgPool2d(quant_type,qi=False,num_bits=num_bits,e_bits=e_bits)
+        # self.qfc1 = QLinear(quant_type, self.fc,qi=False,qo=True,num_bits=num_bits,e_bits=e_bits)
+        self.qfc1 = QLinear(quant_type, self.fc,qi=True,qo=True,num_bits=num_bits,e_bits=e_bits)
 
     def quantize_forward(self, x):
         # for _, layer in self.quantize_layers.items():
@@ -101,7 +102,8 @@ class ResNet(nn.Module):
         qo = self.layer3.freeze(qinput = qo)
         qo = self.layer4.freeze(qinput = qo)
         self.qavgpool1.freeze(qi=qo)
-        self.qfc1.freeze(qi=self.qavgpool1.qo)
+        # self.qfc1.freeze(qi=self.qavgpool1.qo)
+        self.qfc1.freeze()
 
     def fakefreeze(self):
         pass
@@ -204,7 +206,7 @@ class BasicBlock(nn.Module):
         else:
             self.qelementadd.freeze(qi0 = self.qconvbn1.qo, qi1 = qinput)
         # 这里或许需要补充个层来处理elementwise add
-        self.qrelu1.freeze(qi = self.qelementadd.qo)  # 需要自己统计qi
+        self.qrelu1.freeze(qi = self.qelementadd.qo) 
         return self.qrelu1.qi  # relu后的qo可用relu统计的qi 
 
     def quantize_inference(self, x):

mzh/new_mzh/ResNet18-50-152/module.py

@@ -64,7 +64,7 @@ def bias_qmax(quant_type):
     elif quant_type == 'POT':
         return get_qmax(quant_type)
     else:
-        return get_qmax(quant_type, 16, 5)
+        return get_qmax(quant_type, 16, 7)  # e7 m9  (e5时不够大，导致数据溢出到两侧)
         
 
 # 转化为FP32，不需再做限制
@@ -574,17 +574,23 @@ class QConvBN(QModule):
 
 # 待修改  需要有qo吧
 class QAdaptiveAvgPool2d(QModule):
-    def __init__(self, quant_type, qi=False, qo=True, num_bits=8, e_bits=3):
+    def __init__(self, quant_type, qi=False, qo=True,num_bits=8, e_bits=3):
         super(QAdaptiveAvgPool2d, self).__init__(quant_type,qi,qo,num_bits,e_bits)
 
 
-    def freeze(self, qi=None):
+    def freeze(self, qi=None, qo=None):
         if hasattr(self, 'qi') and qi is not None:
             raise ValueError('qi has been provided in init function.')
         if not hasattr(self, 'qi') and qi is None:
             raise ValueError('qi is not existed, should be provided.')
+        # if hasattr(self, 'qo') and qo is not None:
+        #     raise ValueError('qo has been provided in init function.')
+        # if not hasattr(self, 'qo') and qo is None:
+        #     raise ValueError('qo is not existed, should be provided.')
         if qi is not None:
             self.qi = qi
+        # if qo is not None:
+        #     self.qo = qo
 
     # def fakefreeze(self, qi=None):
     #     if hasattr(self, 'qi') and qi is not None:
@@ -603,15 +609,16 @@ class QAdaptiveAvgPool2d(QModule):
         
         x = F.adaptive_avg_pool2d(x,(1, 1))   # 对输入输出都量化一下就算是量化了
 
-        if hasattr(self, 'qo'):
-            self.qo.update(x)
-            x = FakeQuantize.apply(x, self.qo)
+        # if hasattr(self, 'qo'):
+        #     self.qo.update(x)
+        #     x = FakeQuantize.apply(x, self.qo)
 
         return x
 
     def quantize_inference(self, x):
         x = F.adaptive_avg_pool2d(x,(1,1))
-        x = FakeQuantize.apply(x, self.qo)
+        # x = FakeQuantize.apply(x, self.qo
+        # x = get_nearest_val(self.quant_type,x)  # 这里可能并不适配于PoT的情况 缺少一个放缩?
         return x
     
 
@@ -625,7 +632,12 @@ class QModule_2(nn.Module):
         if qi1:
             self.qi1 = QParam(quant_type,num_bits, e_bits)  # qi在此处就已经被num_bits和mode赋值了
         if qo:
-            self.qo = QParam(quant_type,num_bits, e_bits)  # qo在此处就已经被num_bits和mode赋值了
+            # if num_bits <=9 :
+            #     self.qo = QParam(quant_type,num_bits, e_bits)
+            # if num_bits > 9 and num_bits<13:
+            #     self.qo = QParam(quant_type,8, e_bits)  # qo在此处就已经被num_bits和mode赋值了
+            # else:
+            self.qo = QParam(quant_type,num_bits, e_bits)
 
         self.quant_type = quant_type
         self.num_bits = num_bits
@@ -694,11 +706,20 @@ class QElementwiseAdd(QModule_2):
         return x
     
     def quantize_inference(self, x0, x1):  # 此处input为已经量化的qx
+        # x0_d = self.qi0.dequantize_tensor(x0)
+        # x1_d = self.qi1.dequantize_tensor(x1)
+        # print(f"x0={x0_d.reshape(-1)[:10]}")
+        # print(f"x1={x1_d.reshape(-1)[:10]}")
         x0 = x0 - self.qi0.zero_point
         x1 = x1 - self.qi1.zero_point
         
         x = self.M0 * (x0 + x1*self.M1)
 
-        # x = get_nearest_val(self.quant_type,x)
+        x = get_nearest_val(self.quant_type,x)
+
         x = x + self.qo.zero_point
-        return x
\ No newline at end of file
+        # x_d = self.qo.dequantize_tensor(x)
+        # print(f"x={x_d.reshape(-1)[:10]}")
+        # print(f"loss={x_d.reshape(-1)[:10]-(x0_d.reshape(-1)[:10]+x1_d.reshape(-1)[:10])}")
+        # print('=============')
+        return x