Moving to int32 labels - previously int8

llvm/projects/keras/frontend/approxhpvm_translator.py

@@ -404,7 +404,7 @@ class TensorRtTranslator:
 
       
 
-    elif layer_type == "Dense":
+    if layer_type == "Dense":
       input_var_name = self.getSingleInputName(cur_node)
 
       weights = cur_node.weights
@@ -416,7 +416,7 @@ class TensorRtTranslator:
       self.program_str += inst_str
 
       
-    elif self.hasBiasAdd(cur_node):
+    if self.hasBiasAdd(cur_node):
       out_var_name2 = self.getVariableName(cur_node)    
 
       inst_str = "void* " + out_var_name2 + " = "
@@ -430,14 +430,14 @@ class TensorRtTranslator:
       out_var_name1 = out_var_name2
 
 
-    elif layer_type == "Activation":
+    if layer_type == "Activation":
       input_var_name = self.getSingleInputName(cur_node)
       
       inst_str = genActivationCallStr(input_var_name, out_var_name1, cur_node.activation_type)
       self.program_str += inst_str
       
     
-    elif self.hasActivation(cur_node) and layer_type != "Activation":
+    if self.hasActivation(cur_node) and layer_type != "Activation":
       activation_type = cur_node.activation_type
       out_var_name3 = self.getVariableName(cur_node)    
 
@@ -449,7 +449,7 @@ class TensorRtTranslator:
         sys.exit(0)
         
 
-    elif layer_type == "BatchNormalization":
+    if layer_type == "BatchNormalization":
       input_var_name = self.getSingleInputName(cur_node)
 
       inst_str = "void* " + out_var_name1 + " = "
@@ -464,7 +464,7 @@ class TensorRtTranslator:
       self.program_str += inst_str
       
       
-    elif layer_type == "Add":  
+    if layer_type == "Add":  
       input_vars = self.getMultipleInputNames(cur_node)
       
       inst_str = "void* " + out_var_name1 + " = "
@@ -472,7 +472,7 @@ class TensorRtTranslator:
       self.program_str += inst_str
 
       
-    elif layer_type == "MaxPooling2D" or layer_type == "AveragePooling2D":  
+    if layer_type == "MaxPooling2D" or layer_type == "AveragePooling2D":  
       input_var_name = self.getSingleInputName(cur_node)
 
       pool_size = cur_node.pool_size
@@ -491,8 +491,6 @@ class TensorRtTranslator:
       inst_str +=  "," + str(padding) + "," + str(padding) + "," + str(strides[0]) + "," + str(strides[1])
       inst_str += "); \n"
       self.program_str += inst_str
-    #else:
-    #  print ("ERROR: Operator = ", layer_type, " is NOT currently supported")
 
       
     
@@ -771,7 +769,8 @@ class TensorRtTranslator:
     file_path_str += "labels.bin\"); \n"
     self.weight_str += file_path_str
 
-    self.input_str += "uint8_t* labels = readLabels("
+    #self.input_str += "uint8_t* labels = readLabels("
+    self.input_str += "uint32_t* labels = readLabels2("
     self.input_str += file_path + ".c_str()," + str(test_labels.shape[0]) + "); \n"
 
 
@@ -805,11 +804,13 @@ class TensorRtTranslator:
   def endBatchLoop(self):
 
     end_loop_str = ""
-    end_loop_str += "\nuint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); \n"
+    #end_loop_str += "\nuint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); \n"
+    end_loop_str += "\nuint32_t* labels = readLabelsBatch2(labels_path.c_str(),start,end); \n"
 
     last_node = self.dfg.last_node
     output_var = self.output_map[last_node.layer_name]
-    accuracy_call = "\nfloat accuracy = computeAccuracy2(labels, batch_size, " + output_var + "); \n"
+    #accuracy_call = "\nfloat accuracy = computeAccuracy2(labels, batch_size, " + output_var + "); \n"
+    accuracy_call = "\nfloat accuracy = computeAccuracy3(labels, batch_size, " + output_var + "); \n"
     end_loop_str += accuracy_call
  
     #end_loop_str += "float accuracy = computeAccuracy2(labels, batch_size, var_60); "

llvm/projects/keras/frontend/hpvm_dfg_translator.py

@@ -637,7 +637,8 @@ class HPVMTranslator:
     main_func_str += "hpvm_request_tensor(result, 0); \n\n"
     main_func_str += "__visc__cleanup(); \n "
    
-    main_func_str += "computeAccuracy2(labels, " + str(len(test_data)) + ", result); \n"    
+    #main_func_str += "computeAccuracy2(labels, " + str(len(test_data)) + ", result); \n"
+    main_func_str += "computeAccuracy3(labels, " + str(len(test_data)) + ", result); \n"    
     main_func_str += "return 0; \n\n"
     main_func_str += "} \n"    
     

llvm/projects/keras/frontend/promise_translator.py

@@ -1131,11 +1131,14 @@ class PromiseRtTranslator:
   def endBatchLoop(self):
 
     end_loop_str = ""
-    end_loop_str += "\nuint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); \n"
+    #end_loop_str += "\nuint8_t* labels = readLabelsBatch2(labels_path.c_str(),start,end); \n"
+    end_loop_str += "\nuint32_t* labels = readLabelsBatch2(labels_path.c_str(),start,end); \n"
 
+    
     last_node = self.dfg.last_node
     output_var = self.output_map[last_node.layer_name]
-    accuracy_call = "\nfloat accuracy = computeAccuracy2(labels, batch_size, " + output_var + "); \n"
+    #accuracy_call = "\nfloat accuracy = computeAccuracy2(labels, batch_size, " + output_var + "); \n"
+    accuracy_call = "\nfloat accuracy = computeAccuracy3(labels, batch_size, " + output_var + "); \n"
     end_loop_str += accuracy_call
  
     #end_loop_str += "float accuracy = computeAccuracy2(labels, batch_size, var_60); "

llvm/projects/keras/frontend/utils.py

@@ -5,16 +5,27 @@
 def nodeHasBias(cur_node):
     
   if cur_node.layer_type == "Conv2D" or cur_node.layer_type == "DepthwiseConv2D" or cur_node.layer_type == "Dense":
+    #return True
+    return cur_node.use_bias
+  else:
+    return False
+
+  
+def layerHasActivationAttr(cur_node):
+    
+  if cur_node.layer_type == "Conv2D" or cur_node.layer_type == "DepthwiseConv2D" \
+     or cur_node.layer_type == "Dense" or cur_node.layer_type == "Activation":
     return True
   else:
     return False
-      
+
 
 def nodeHasActivation(cur_node):
     
   if cur_node.layer_type == "Conv2D" or cur_node.layer_type == "DepthwiseConv2D" \
      or cur_node.layer_type == "Dense" or cur_node.layer_type == "Activation":
-    return True
+    #return True
+    return cur_node.activation_type != "linear"
   else:
     return False
 

llvm/projects/keras/frontend/weight_utils.py

@@ -12,9 +12,11 @@ def dumpLabels(file_name, Y_test):
     for label in Y_test:
         label_val = 0
         if len(Y_test.shape) > 1:        
-          label_val = np.int8(label[0])
+          #label_val = np.int8(label[0])
+          label_val = np.int32(label[0])
         else:
-          label_val = np.int8(label)
+          #label_val = np.int8(label)
+          label_val = np.int32(label)
          
         if label_val not in labels_map:
             labels_map[label_val] = 0