From 78d20827fa0d0a36d0827a1476260c2b6f7d6e84 Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@miranda.cs.illinois.edu>
Date: Sun, 31 Jan 2021 01:23:28 -0600
Subject: [PATCH] Adding mising Benchmark.py (Keras frontend
---
hpvm/projects/keras/src/Benchmark.py | 122 +++++++++++++++++++++++++++
1 file changed, 122 insertions(+)
create mode 100644 hpvm/projects/keras/src/Benchmark.py
diff --git a/hpvm/projects/keras/src/Benchmark.py b/hpvm/projects/keras/src/Benchmark.py
new file mode 100644
index 0000000000..3610b2e9a5
--- /dev/null
+++ b/hpvm/projects/keras/src/Benchmark.py
@@ -0,0 +1,122 @@
+
+
+import sys
+import os
+import shutil
+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 reloadHPVMWeights
+
+
+# Every CNN Benchmark must inherit from Benchmark class
+# Defines common interfaces and virtual methods to be overridden by child classes
+class Benchmark:
+
+ def __init__(self, name, reload_dir, keras_model_file, data_dir, src_dir, num_classes, batch_size=500):
+ self.name = name
+ self.reload_dir = reload_dir
+ self.keras_model_file = keras_model_file
+ self.data_dir = data_dir
+ self.src_dir = src_dir
+ self.num_classes = num_classes
+ self.batch_size = batch_size
+
+
+ def buildModel(self):
+ return
+
+ def data_preprocess(self):
+ return
+
+ def trainModel(self, X_train, y_train, X_test, y_test):
+ return
+
+ def inference(self):
+ return
+
+
+ # Compiles frontend generated sources
+ def compileSource(self, working_dir):
+
+ # set LLVM_SRC_ROOT
+ os.environ["CFLAGS"] = ""
+ os.environ["CXXFLAGS"] = ""
+
+ dest_file = working_dir + "CMakeLists.txt"
+ shutil.copy("cmake_template/CMakeLists.txt", dest_file)
+
+ # Cmake ../
+ # make
+
+
+ def printUsage(self):
+
+ print ("Usage: python ${benchmark.py} [hpvm_reload|train] [frontend] [compile]")
+ sys.exit(0)
+
+
+ def run(self, argv):
+
+ if len(argv) < 2:
+ self.printUsage()
+
+ print ("Build Model ...")
+ # Virtual method call implemented by each CNN
+ model = self.buildModel()
+
+ print ("Data Preprocess... \n")
+ # Virtual method call to preprocess test and train data
+ X_train, y_train, X_test, y_test, X_tuner, y_tuner = self.data_preprocess()
+
+ if argv[1] == "hpvm_reload":
+ print ("loading weights .....\n\n")
+ model = reloadHPVMWeights(model, self.reload_dir, self.keras_model_file)
+
+ elif argv[1] == "keras_reload":
+ model.load_weights(self.keras_model_file)
+ model.compile(loss='categorical_crossentropy',
+ optimizer='adam',
+ metrics=['accuracy'])
+
+ elif argv[1] == "train":
+ print ("Train Model ...")
+ model = self.trainModel(model, X_train, y_train, X_test, y_test)
+ else:
+ self.printUsage()
+
+
+ score = model.evaluate(X_test, to_categorical(y_test, self.num_classes), verbose=0)
+ print('Test accuracy2:', score[1])
+
+
+ if len(argv) > 2:
+ if argv[2] == "frontend":
+
+ # Main call to ApproxHPVM-Keras Frontend
+ working_dir = translate_to_approxhpvm(model,
+ self.data_dir, self.src_dir, ## "data/test_src/",
+ X_test, y_test,
+ X_tuner, y_tuner,
+ self.batch_size, # FIXIT
+ self.num_classes,
+ (argv[1] == "hpvm_reload")) # Do not redump HPVM weights if `hpvm_reload` used
+
+ if len(argv) > 3 and argv[3] == "compile":
+ self.compileSource(working_dir)
+ else:
+ self.printUsage()
+
+
+ if argv[2] == "keras_dump":
+ model.save_weights(self.keras_model_file)
+
+
+ elif len(argv) > 2:
+ self.printUsage()
+
+
+
+
+
--
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