diff --git a/llvm/projects/keras/frontend/weight_utils.py b/llvm/projects/keras/frontend/weight_utils.py
index fefd48e7781712fde47d0e5a918a8c0afbecb2db..9d5bf75d6de9ccdb3d264542a231668944d668d7 100644
--- a/llvm/projects/keras/frontend/weight_utils.py
+++ b/llvm/projects/keras/frontend/weight_utils.py
@@ -2,6 +2,7 @@
import numpy as np
import struct
import random
+from keras.optimizers import Adam
def dumpLabels(file_name, Y_test):
@@ -148,9 +149,53 @@ def dumpCalibrationData(file_name, X_train, labels_fname, train_labels):
def dumpCalibrationData2(file_name, test_data, labels_fname, test_labels):
-
-
+
dumpData(file_name, test_data)
dumpLabels(labels_fname, test_labels)
+
+
+# Loads Existing HPVM FP32 weights
+def dumpHPVMToKerasModel(model, reload_dir, output_model, X_test, Y_test):
+
+ print ("***** Reloading pre-trained HPVM weights ****")
+
+ for i in range(len(model.layers)):
+ layer = model.layers[i]
+ layer_name = layer.name
+ print ("*layer_name = ", layer_name)
+ if "conv" not in layer_name and "dense" not in layer_name:
+ continue
+
+ w_path = reload_dir + layer_name + "_w.bin"
+ print ("** w_path = ", w_path)
+ w_arr = np.fromfile(w_path, dtype='float32')
+
+ b_path = reload_dir + layer_name + "_b.bin"
+ b_arr = np.fromfile(b_path, dtype='float32')
+
+ w_shape = layer.get_weights()[0].shape
+ if "conv" in layer_name:
+ w_nchw_shape = (w_shape[3], w_shape[2], w_shape[0], w_shape[1])
+ w_arr = np.reshape(w_arr, w_nchw_shape)
+ w_arr = np.transpose(w_arr, (2,3,1,0))
+
+ if "dense" in layer_name:
+ w_arr = np.reshape(w_arr, w_shape)
+
+ weights = []
+ weights.append(w_arr)
+ weights.append(b_arr)
+
+ # Overriding model weights
+ layer.set_weights(weights)
+
+ # Model recompilation needed after resetting weights
+ model.compile(loss='categorical_crossentropy',
+ optimizer=Adam(lr=0.0001, decay=1e-6),
+ metrics=['accuracy'])
+
+ model.save("alexnet.h5")
+
+ return model
diff --git a/llvm/projects/keras/src/alexnet.py b/llvm/projects/keras/src/alexnet.py
index f1337f432944b6159b6be54f11d1fc3384933e38..fa9abcfdd9a5324af948d18af791fc21524b842f 100644
--- a/llvm/projects/keras/src/alexnet.py
+++ b/llvm/projects/keras/src/alexnet.py
@@ -3,6 +3,7 @@ import numpy as np
from keras.datasets import cifar10
from keras.models import Sequential
+from keras.models import load_model
from keras.layers.core import Dense, Dropout, Flatten, Activation
from keras.layers.convolutional import Conv2D
from keras.optimizers import Adam
@@ -19,7 +20,7 @@ import numpy as np
import os
from frontend.approxhpvm_translator import translate_to_approxhpvm
from frontend.weight_utils import dumpCalibrationData
-
+from frontend.weight_utils import dumpHPVMToKerasModel
def lr_schedule(epoch):
@@ -161,61 +162,22 @@ def trainModel(model):
-# Loads Existing HPVM FP32 weights
-def reloadFP32HPVMModel(model, reload_dir):
-
- print ("\n\n*****NOTE: Reloading pre-trained weights \n")
-
- for i in range(len(model.layers)):
- layer = model.layers[i]
- layer_name = layer.name
- print ("*layer_name = ", layer_name)
- if "conv" not in layer_name and "dense" not in layer_name:
- continue
-
- w_path = reload_dir + layer_name + "_w.bin"
- print ("** w_path = ", w_path)
- b_path = reload_dir + layer_name + "_b.bin"
-
- w_arr = np.fromfile(w_path, dtype='float32')
- print ("w_arr = ", w_arr)
-
- b_arr = np.fromfile(b_path, dtype='float32')
-
- w_shape = layer.get_weights()[0].shape
- if "conv" in layer_name:
- w_nchw_shape = (w_shape[3], w_shape[2], w_shape[0], w_shape[1])
- w_arr = np.reshape(w_arr, w_nchw_shape)
- w_arr = np.transpose(w_arr, (2,3,1,0))
- print ("old_shape = ", w_shape, " new_shape = ", w_arr.shape)
+
- if "dense" in layer_name:
- w_arr = np.reshape(w_arr, w_shape)
+def reloadKerasModel(model_path):
- weights = []
- weights.append(w_arr)
- weights.append(b_arr)
+ model = load_model(model_path)
- # NOTE: overriding weights
- layer.set_weights(weights)
-
+ score = model.evaluate(X_test, to_categorical(Y_test, 10), verbose=0)
+ print('Test loss2:', score[0])
+ print('Test accuracy2:', score[1])
- model.compile(loss='categorical_crossentropy',
- optimizer=Adam(lr=0.0001, decay=1e-6),
- #optimizer = opt_rms,
- metrics=['accuracy'])
- #input_path = reload_dir + "input.bin"
- #input_arr = np.fromfile(input_path, dtype='float32')
- #input_arr = np.reshape(input_arr, (10000, 3, 32, 32))
+def data_preprocess():
(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
- test_labels = Y_test
- train_labels = Y_train
- #X_train = X_train.astype('float32')
- #X_test = X_test.astype('float32')
X_train = X_train / 255.0
X_test = X_test / 255.0
@@ -223,13 +185,11 @@ def reloadFP32HPVMModel(model, reload_dir):
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)
-
- score = model.evaluate(X_test, to_categorical(Y_test, 10), verbose=0)
- print('Test loss2:', score[0])
- print('Test accuracy2:', score[1])
+ return X_train, Y_train, X_test, Y_test
+
if __name__ == "__main__":
@@ -240,7 +200,18 @@ if __name__ == "__main__":
model = buildModel2()
- reloadFP32HPVMModel(model, "/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/")
+ X_train, Y_train, X_test, Y_test = data_preprocess()
+ ###reloadFP32HPVMModel(model, "/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/")
+ reload_dir = "/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/"
+ keras_model_file = "alexnet.h5"
+ model = dumpHPVMToKerasModel(model, reload_dir, keras_model_file, X_test, Y_test)
+
+
+ num_classes = 10
+ score = model.evaluate(X_test, to_categorical(Y_test, num_classes), verbose=0)
+ print('Test accuracy2:', score[1])
+
+ reloadKerasModel(keras_model_file)
### trainModel(model)