fix imagenet benchmark bugs

hpvm/projects/keras/src/alexnet_imagenet.py

@@ -21,28 +21,19 @@ from Config import MODEL_PARAMS_DIR
 
 
 
-IMAGENET_DIR = '/home/nz11/ILSVRC2012/'
-NUM_TUNE_CLASSES = 200
-IMAGES_PER_CLASS = 50
-
-
-
 class AlexNet(Benchmark):
 
     def data_preprocess(self):
-
-        X_val = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/test_input.bin', dtype=np.float32)
-        y_val = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/test_labels.bin', dtype=np.uint32)
-        
-        X_val = X_val.reshape((-1, 3, 224, 224)) 
         X_train, y_train = None, None
         
-            
-        X_test = X_val[0:5000]
-        y_test = y_val[0:5000]
-        X_tuner = X_val[5000:]
-        y_tuner = y_val[5000:]
+        X_test = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/test_input.bin', dtype=np.float32)
+        X_test = X_test.reshape((-1, 3, 224, 224)) 
+        y_test = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/test_labels.bin', dtype=np.uint32)
         
+        X_tuner = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/tune_input.bin', dtype=np.float32)
+        X_tuner = X_tuner.reshape((-1, 3, 224, 224)) 
+        y_tuner = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/tune_labels.bin', dtype=np.uint32)
+ 
         return X_train, y_train, X_test, y_test, X_tuner, y_tuner
     
     

hpvm/projects/keras/src/lenet.py

@@ -51,7 +51,6 @@ class LeNet_MNIST(Benchmark):
 
 
     def data_preprocess(self):
-
         (X_train, y_train), (X_val, y_val) = mnist.load_data()
         test_labels = y_val
 

hpvm/projects/keras/src/resnet50_imagenet.py

@@ -16,18 +16,11 @@ from keras.utils import to_categorical
 from keras.preprocessing.image import ImageDataGenerator
 from keras.callbacks import LearningRateScheduler
 
-from keras.applications.resnet50 import preprocess_input
 from Benchmark import Benchmark
 from Config import MODEL_PARAMS_DIR
 
 
 
-IMAGENET_DIR = '/home/nz11/ILSVRC2012/'
-NUM_TUNE_CLASSES = 200
-IMAGES_PER_CLASS = 50
-
-
-
 class ResNet50(Benchmark):
     
     def buildModel(self):
@@ -120,19 +113,16 @@ class ResNet50(Benchmark):
 
     
     def data_preprocess(self):
-
-        X_val = np.fromfile(MODEL_PARAMS_DIR + '/resnet50_imagenet/test_input.bin', dtype=np.float32)
-        y_val = np.fromfile(MODEL_PARAMS_DIR + '/resnet50_imagenet/test_labels.bin', dtype=np.uint32)
-        
-        X_val = X_val.reshape((-1, 3, 224, 224)) 
         X_train, y_train = None, None
         
-            
-        X_test = X_val[0:5000]
-        y_test = y_val[0:5000]
-        X_tuner = X_val[5000:]
-        y_tuner = y_val[5000:]
+        X_test = np.fromfile(MODEL_PARAMS_DIR + '/resnet50_imagenet/test_input.bin', dtype=np.float32)
+        X_test = X_test.reshape((-1, 3, 224, 224)) 
+        y_test = np.fromfile(MODEL_PARAMS_DIR + '/resnet50_imagenet/test_labels.bin', dtype=np.uint32)
         
+        X_tuner = np.fromfile(MODEL_PARAMS_DIR + '/resnet50_imagenet/tune_input.bin', dtype=np.float32)
+        X_tuner = X_tuner.reshape((-1, 3, 224, 224)) 
+        y_tuner = np.fromfile(MODEL_PARAMS_DIR + '/resnet50_imagenet/tune_labels.bin', dtype=np.uint32)
+ 
         return X_train, y_train, X_test, y_test, X_tuner, y_tuner
     
 

hpvm/projects/keras/src/vgg16_imagenet.py

@@ -21,12 +21,6 @@ from Config import MODEL_PARAMS_DIR
 
 
 
-IMAGENET_DIR = '/home/nz11/ILSVRC2012/'
-NUM_TUNE_CLASSES = 200
-IMAGES_PER_CLASS = 50
-
-
-
 class VGG16(Benchmark):
 
     def buildModel(self):
@@ -104,19 +98,16 @@ class VGG16(Benchmark):
 
 
     def data_preprocess(self):
-
-        X_val = np.fromfile(MODEL_PARAMS_DIR + '/vgg16_imagenet/test_input.bin', dtype=np.float32)
-        y_val = np.fromfile(MODEL_PARAMS_DIR + '/vgg16_imagenet/test_labels.bin', dtype=np.uint32)
-        
-        X_val = X_val.reshape((-1, 3, 224, 224)) 
         X_train, y_train = None, None
         
-            
-        X_test = X_val[0:5000]
-        y_test = y_val[0:5000]
-        X_tuner = X_val[5000:]
-        y_tuner = y_val[5000:]
+        X_test = np.fromfile(MODEL_PARAMS_DIR + '/vgg16_imagenet/test_input.bin', dtype=np.float32)
+        X_test = X_test.reshape((-1, 3, 224, 224)) 
+        y_test = np.fromfile(MODEL_PARAMS_DIR + '/vgg16_imagenet/test_labels.bin', dtype=np.uint32)
         
+        X_tuner = np.fromfile(MODEL_PARAMS_DIR + '/vgg16_imagenet/tune_input.bin', dtype=np.float32)
+        X_tuner = X_tuner.reshape((-1, 3, 224, 224)) 
+        y_tuner = np.fromfile(MODEL_PARAMS_DIR + '/vgg16_imagenet/tune_labels.bin', dtype=np.uint32)
+ 
         return X_train, y_train, X_test, y_test, X_tuner, y_tuner