renaming HPVM reload weights routine

llvm/projects/keras/frontend/weight_utils.py

@@ -159,7 +159,7 @@ def dumpCalibrationData2(file_name, test_data, labels_fname, test_labels):
 
 
 # Loads Existing HPVM FP32 weights
-def dumpHPVMToKerasModel(model, reload_dir, output_model, X_test, Y_test):
+def reloadHPVMWeights(model, reload_dir, output_model, X_test, Y_test):
 
   print ("***** Reloading pre-trained HPVM weights ****")
   

llvm/projects/keras/src/Benchmark.py

@@ -7,7 +7,7 @@ from keras.utils.np_utils import to_categorical
 from keras.models import load_model
 from frontend.approxhpvm_translator import translate_to_approxhpvm
 from frontend.weight_utils import dumpCalibrationData
-from frontend.weight_utils import dumpHPVMToKerasModel
+from frontend.weight_utils import reloadHPVMWeights
 
 
 # Every CNN Benchmark must inherit from Benchmark class
@@ -62,7 +62,8 @@ class Benchmark:
       X_train, Y_train, X_test, Y_test = self.data_preprocess()   
 
       if argv[1] == "hpvm_reload":
-        model = dumpHPVMToKerasModel(model, self.reload_dir, self.keras_model_file, X_test, Y_test)
+        print ("loading weights .....\n\n")  
+        model = reloadHPVMWeights(model, self.reload_dir, self.keras_model_file, X_test, Y_test)
 
       if argv[1] == "keras_reload":
         model = load_model(self.keras_model_file)

llvm/projects/keras/src/alexnet.py

@@ -19,9 +19,7 @@ import keras
 import numpy as np
 import os
 from Benchmark import Benchmark
-from frontend.approxhpvm_translator import translate_to_approxhpvm
-from frontend.weight_utils import dumpCalibrationData
-from frontend.weight_utils import dumpHPVMToKerasModel
+
 
 
 
@@ -46,6 +44,8 @@ class AlexNet(Benchmark):
 
   def buildModel(self):
 
+      print ("BuildModel ...")
+      
       activation_type = "tanh"
       weight_decay = 1e-4
 
@@ -138,16 +138,25 @@ class AlexNet(Benchmark):
 
   def data_preprocess(self):
 
+    print ("Data Preprocess... \n")
+    
     (X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
 
+    print ("Data Loaded... \n")    
+    
     X_train = X_train / 255.0
     X_test = X_test / 255.0
 
+    print(X_train, X_test)
+      
     mean = np.mean(X_train,axis=(0,1,2,3))
     std = np.std(X_train,axis=(0,1,2,3))   
     X_train = (X_train-mean)/(std+1e-7)
     X_test = (X_test-mean)/(std+1e-7)  
 
+    print(X_train, X_test)
+  
+    
     return X_train, Y_train, X_test, Y_test