Skip to content
Snippets Groups Projects
Commit 33ee6eb4 authored by nz11's avatar nz11
Browse files

Update vgg16_imagenet.py

parent c9fe8348
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
llvm/projects/keras/src/vgg16_imagenet.py
+ 98
− 106
View file @ 33ee6eb4
......@@ -4,102 +4,105 @@ import glob
import scipy
import scipy.io
import cv2
import numpy as np
import keras
from keras.models import Model
from keras.models import Sequential, Model
from keras.layers import *
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.utils import to_categorical
from keras import backend as K
from frontend.approxhpvm_translator import translate_to_approxhpvm
from frontend.weight_utils import dumpCalibrationData
np.random.seed(0)
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
K.set_image_data_format('channels_first')
num_images = 5000
val_size = 100
data_format = 'channels_first'
def get_vgg16_nchw_keras():
img_input = Input(shape=(3, 224, 224))
# Block 1
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(img_input)
x = Conv2D(64, (3, 3),
padding='valid',
data_format=data_format)(x)
padding='same',
data_format=data_format)(img_input)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(64, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
# Block 2
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(128, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(128, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
# Block 3
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(256, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(256, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(256, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
# Block 4
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(512, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(512, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(512, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
# Block 5
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(512, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(512, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1), data_format=data_format)(x)
x = Conv2D(512, (3, 3),
padding='valid',
padding='same',
data_format=data_format)(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), data_format=data_format)(x)
x = Flatten(data_format=data_format)(x)
x = Flatten(data_format='channels_last')(x)
x = Dense(4096)(x)
x = Activation('relu')(x)
......@@ -114,11 +117,6 @@ def get_vgg16_nchw_keras():
def load_image(x):
try:
x = x.decode('utf-8')
except:
pass
image = cv2.imread(x)
height, width, _ = image.shape
......@@ -132,88 +130,82 @@ def load_image(x):
image = image[starty:starty + 224, startx:startx + 224]
image = image[:, :, ::-1]
image = preprocess_input(image.astype(np.float32))
image = np.transpose(image, (2, 0, 1))
image = preprocess_input(image.astype(np.float32), data_format=data_format)
return image.astype(np.float32)
if __name__ == '__main__':
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
num_images = 5000
val_size = 100
model = VGG16()
model_nchw = get_vgg16_nchw_keras()
j = 0
for i in range(len(model_nchw.layers)):
if 'padding' in model_nchw.layers[i].name or 'activation' in model_nchw.layers[i].name:
continue
try:
model_nchw.layers[i].set_weights(model.layers[j].get_weights())
except:
print (i, model_nchw.layers[i], 'skipped')
j += 1
classes = os.listdir('/home/nz11/ILSVRC2012/train')
train_images = glob.glob('/home/nz11/ILSVRC2012/train/*/*')
val_images = glob.glob('/home/nz11/ILSVRC2012/val/*/*')
val_images = sorted(val_images, key=lambda x: x.split('/')[-1].split('_')[-1].split('.')[0])
model = VGG16()
model_nchw = get_vgg16_nchw_keras()
idx = np.random.permutation(len(val_images))[:num_images]
val_images = np.array(val_images)[idx]
j = 0
for i in range(len(model_nchw.layers)):
if 'padding' in model_nchw.layers[i].name or 'activation' in model_nchw.layers[i].name:
continue
try:
model_nchw.layers[i].set_weights(model.layers[j].get_weights())
except:
print (i, model_nchw.layers[i], 'skipped')
j += 1
d = {k:v for v, k in enumerate(classes)}
X_test = []
for x in val_images:
X_test.append(load_image(x))
X_test = np.array(X_test)
meta = scipy.io.loadmat("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/meta.mat")
original_idx_to_synset = {}
synset_to_name = {}
for i in range(1000):
ilsvrc2012_id = int(meta["synsets"][i,0][0][0][0])
synset = meta["synsets"][i,0][1][0]
name = meta["synsets"][i,0][2][0]
original_idx_to_synset[ilsvrc2012_id] = synset
synset_to_name[synset] = name
synset_to_keras_idx = {}
keras_idx_to_name = {}
f = open("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/synset_words.txt","r")
c = 0
for line in f:
parts = line.split(" ")
synset_to_keras_idx[parts[0]] = c
keras_idx_to_name[c] = " ".join(parts[1:])
c += 1
f.close()
def convert_original_idx_to_keras_idx(idx):
return synset_to_keras_idx[original_idx_to_synset[idx]]
f = open("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt","r")
classes = os.listdir('/home/nz11/ILSVRC2012/train')
train_images = glob.glob('/home/nz11/ILSVRC2012/train/*/*')
val_images = glob.glob('/home/nz11/ILSVRC2012/val/*/*')
val_images = sorted(val_images, key=lambda x: x.split('/')[-1].split('_')[-1].split('.')[0])
idx = np.random.permutation(len(val_images))[:num_images]
val_images = np.array(val_images)[idx]
d = {k:v for v, k in enumerate(classes)}
X_test = []
for x in val_images:
X_test.append(load_image(x))
X_test = np.array(X_test)
meta = scipy.io.loadmat("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/meta.mat")
original_idx_to_synset = {}
synset_to_name = {}
for i in range(1000):
ilsvrc2012_id = int(meta["synsets"][i,0][0][0][0])
synset = meta["synsets"][i,0][1][0]
name = meta["synsets"][i,0][2][0]
original_idx_to_synset[ilsvrc2012_id] = synset
synset_to_name[synset] = name
synset_to_keras_idx = {}
keras_idx_to_name = {}
f = open("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/synset_words.txt","r")
c = 0
for line in f:
parts = line.split(" ")
synset_to_keras_idx[parts[0]] = c
keras_idx_to_name[c] = " ".join(parts[1:])
c += 1
f.close()
def convert_original_idx_to_keras_idx(idx):
return synset_to_keras_idx[original_idx_to_synset[idx]]
with open("/home/nz11/ILSVRC2012/ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt","r") as f:
y_true = f.read().strip().split("\n")
y_true = list(map(int, y_true))
y_true = np.array([convert_original_idx_to_keras_idx(idx) for idx in y_true])[idx]
y_true = y_true.astype(np.uint32)
f.close()
translate_to_approxhpvm(model_nchw, "data/vgg16_imagenet/", X_test[:val_size], y_true[:val_size], 1000)
dumpCalibrationData("data/vgg16_imagenet/test_input.bin", X_test, "data/vgg16_imagenet/test_labels.bin", y_true)
\ No newline at end of file
y_true = np.expand_dims(y_true, axis=-1)
translate_to_approxhpvm(model_nchw, "data/vgg16_imagenet/", X_test[:val_size], y_true[:val_size], 1000)
dumpCalibrationData("data/vgg16_imagenet/test_input.bin", X_test, "data/vgg16_imagenet/test_labels.bin", y_true)
pred = np.argmax(model_nchw.predict(X_test), axis=1)
print ('val accuracy', np.sum(pred == y_true.ravel()) / val_size)
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment