From 504ad4e9ffaab82a1bd0171ee87912c954361526 Mon Sep 17 00:00:00 2001
From: Nathan Zhao <nz11@tyler.cs.illinois.edu>
Date: Mon, 1 Feb 2021 20:39:59 -0600
Subject: [PATCH] fix imagenet benchmark bugs
---
hpvm/projects/keras/src/alexnet_imagenet.py | 23 ++++++-------------
hpvm/projects/keras/src/lenet.py | 1 -
hpvm/projects/keras/src/resnet50_imagenet.py | 24 ++++++--------------
hpvm/projects/keras/src/vgg16_imagenet.py | 23 ++++++-------------
4 files changed, 21 insertions(+), 50 deletions(-)
diff --git a/hpvm/projects/keras/src/alexnet_imagenet.py b/hpvm/projects/keras/src/alexnet_imagenet.py
index 5fceb31b31..e3ab937e9b 100644
--- a/hpvm/projects/keras/src/alexnet_imagenet.py
+++ b/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
diff --git a/hpvm/projects/keras/src/lenet.py b/hpvm/projects/keras/src/lenet.py
index 83f4d3cf52..4b58e65314 100644
--- a/hpvm/projects/keras/src/lenet.py
+++ b/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
diff --git a/hpvm/projects/keras/src/resnet50_imagenet.py b/hpvm/projects/keras/src/resnet50_imagenet.py
index bca4799b75..0c3006213d 100644
--- a/hpvm/projects/keras/src/resnet50_imagenet.py
+++ b/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
diff --git a/hpvm/projects/keras/src/vgg16_imagenet.py b/hpvm/projects/keras/src/vgg16_imagenet.py
index 5e2bef9c34..35ab92479e 100644
--- a/hpvm/projects/keras/src/vgg16_imagenet.py
+++ b/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
--
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