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Generate VGG16 model for CoreML
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# Modified from https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3#file-vgg-16_keras-py | |
from keras.models import Sequential | |
from keras.layers.core import Flatten, Dense, Dropout | |
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D | |
from keras.optimizers import SGD | |
import cv2, numpy as np | |
from keras import backend as K | |
import csv | |
import coremltools | |
from keras import models | |
import sys | |
import imghdr | |
import os.path | |
def VGG_16(weights_path=None): | |
model = Sequential() | |
model.add(ZeroPadding2D((1,1),input_shape=(3,224,224))) | |
model.add(Convolution2D(64, 3, 3, activation='relu')) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(64, 3, 3, activation='relu')) | |
model.add(MaxPooling2D((2,2), strides=(2,2))) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(128, 3, 3, activation='relu')) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(128, 3, 3, activation='relu')) | |
model.add(MaxPooling2D((2,2), strides=(2,2))) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(256, 3, 3, activation='relu')) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(256, 3, 3, activation='relu')) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(256, 3, 3, activation='relu')) | |
model.add(MaxPooling2D((2,2), strides=(2,2))) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu')) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu')) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu')) | |
model.add(MaxPooling2D((2,2), strides=(2,2))) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu')) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu')) | |
model.add(ZeroPadding2D((1,1))) | |
model.add(Convolution2D(512, 3, 3, activation='relu')) | |
model.add(MaxPooling2D((2,2), strides=(2,2))) | |
model.add(Flatten()) | |
model.add(Dense(4096, activation='relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(4096, activation='relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(1000, activation='softmax')) | |
if weights_path: | |
model.load_weights(weights_path) | |
return model | |
if __name__ == "__main__": | |
K.set_image_dim_ordering('th') | |
## Check if there input filename exists | |
if os.path.exists(sys.argv[1]) == False: | |
print("Please enter a valid filename") | |
exit() | |
input_image = cv2.imread(sys.argv[1]) | |
img = cv2.resize(input_image, (224, 224)).astype(np.float32) | |
mean_pixel = [103.939, 116.779, 123.68] | |
img = img.astype(np.float32, copy=False) | |
for c in range(3): | |
img[:, :, c] = img[:, :, c] - mean_pixel[c] | |
img = img.transpose((2,0,1)) | |
img = np.expand_dims(img, axis=0) | |
# Test pretrained model | |
model = VGG_16('vgg16_weights.h5') | |
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) | |
model.compile(optimizer=sgd, loss='categorical_crossentropy') | |
out = model.predict(img) | |
line = np.argmax(out) | |
the_file = open('synset_words.txt', 'r') | |
reader = csv.reader(the_file) | |
for i, row in enumerate(reader): | |
if i == line: | |
print(row) | |
break | |
models.save_model(model,'vgg16.h5') | |
### Uncomment if you want to generate the .mlmodel file for Xcode | |
# coreml_model = coremltools.converters.keras.convert('vgg16.h5') | |
# coreml_model.save('vgg16_keras.mlmodel') |
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