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Commit f25d6c4d authored by Hashim Sharif's avatar Hashim Sharif
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Adding more flag option checks to Benchmark class

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...@@ -47,14 +47,19 @@ class Benchmark: ...@@ -47,14 +47,19 @@ class Benchmark:
# Cmake ../ # Cmake ../
# make # make
def run(self, argv):
if len(argv) < 2: def printUsage(self):
print ("Usage: python ${benchmark.py} [hpvm_reload|keras_reload|train] [frontend] [compile]")
print ("Usage: python ${benchmark.py} [hpvm_reload|keras_reload|train] [frontend] [compile]")
sys.exit(0) sys.exit(0)
def run(self, argv):
if len(argv) < 2:
self.printUsage()
# Virtual method call implemented by each CNN # Virtual method call implemented by each CNN
model = self.buildModel() model = self.buildModel()
...@@ -65,12 +70,16 @@ class Benchmark: ...@@ -65,12 +70,16 @@ class Benchmark:
print ("loading weights .....\n\n") print ("loading weights .....\n\n")
model = reloadHPVMWeights(model, self.reload_dir, self.keras_model_file, X_test, Y_test) model = reloadHPVMWeights(model, self.reload_dir, self.keras_model_file, X_test, Y_test)
if argv[1] == "keras_reload": elif argv[1] == "keras_reload":
model = load_model(self.keras_model_file) model = load_model(self.keras_model_file)
if argv[1] == "train": elif argv[1] == "train":
model = self.trainModel(model) model = self.trainModel(model)
else:
self.printUsage()
score = model.evaluate(X_test, to_categorical(Y_test, self.num_classes), verbose=0) score = model.evaluate(X_test, to_categorical(Y_test, self.num_classes), verbose=0)
print('Test accuracy2:', score[1]) print('Test accuracy2:', score[1])
...@@ -86,6 +95,11 @@ class Benchmark: ...@@ -86,6 +95,11 @@ class Benchmark:
if len(argv) > 3 and argv[3] == "compile": if len(argv) > 3 and argv[3] == "compile":
self.compileSource(working_dir) self.compileSource(working_dir)
else:
self.printUsage()
elif len(argv) > 2:
self.printUsage()
...@@ -146,16 +146,11 @@ class AlexNet(Benchmark): ...@@ -146,16 +146,11 @@ class AlexNet(Benchmark):
X_train = X_train / 255.0 X_train = X_train / 255.0
X_test = X_test / 255.0 X_test = X_test / 255.0
print(X_train, X_test)
mean = np.mean(X_train,axis=(0,1,2,3)) mean = np.mean(X_train,axis=(0,1,2,3))
std = np.std(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_train = (X_train-mean)/(std+1e-7)
X_test = (X_test-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 return X_train, Y_train, X_test, Y_test
......
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