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#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import tensorlayer as tl
from data.imagenet_classes import *
import os
import numpy as np
import time
import inspect
import skimage
import skimage.io
import skimage.transform
"""
VGG-19 for ImageNet
--------------------
Pre-trained model in this example - VGG19 NPZ and
trainable examples of VGG16/19 in TensorFlow can be found here:
https://github.com/machrisaa/tensorflow-vgg
For simplified CNN layer see "Convolutional layer (Simplified)"
in read the docs website.
"""
VGG_MEAN = [103.939, 116.779, 123.68]
def load_image(path):
# load image
img = skimage.io.imread(path)
img = img / 255.0
assert (0 <= img).all() and (img <= 1.0).all()
# print "Original Image Shape: ", img.shape
# we crop image from center
short_edge = min(img.shape[:2])
yy = int((img.shape[0] - short_edge) / 2)
xx = int((img.shape[1] - short_edge) / 2)
crop_img = img[yy: yy + short_edge, xx: xx + short_edge]
# resize to 224, 224
resized_img = skimage.transform.resize(crop_img, (224, 224))
return resized_img
def print_prob(prob):
synset = class_names
# print prob
pred = np.argsort(prob)[::-1]
# Get top1 label
top1 = synset[pred[0]]
print("Top1: ", top1, prob[pred[0]])
# Get top5 label
top5 = [(synset[pred[i]], prob[pred[i]]) for i in range(5)]
print("Top5: ", top5)
return top1
def Vgg19(rgb):
"""
Build the VGG 19 Model
Parameters
-----------
rgb : rgb image placeholder [batch, height, width, 3] values scaled [0, 1]
"""
start_time = time.time()
print("build model started")
rgb_scaled = rgb * 255.0
# Convert RGB to BGR
if tf.__version__ <= '0.11':
red, green, blue = tf.split(3, 3, rgb_scaled)
else: # TF 1.0
print(rgb_scaled)
red, green, blue = tf.split(rgb_scaled, 3, 3)
assert red.get_shape().as_list()[1:] == [224, 224, 1]
assert green.get_shape().as_list()[1:] == [224, 224, 1]
assert blue.get_shape().as_list()[1:] == [224, 224, 1]
if tf.__version__ <= '0.11':
bgr = tf.concat(3, [
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
else:
bgr = tf.concat([
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
], axis=3)
assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
""" input layer """
net_in = tl.layers.InputLayer(bgr, name='input_layer')
""" conv1 """
network = tl.layers.Conv2dLayer(net_in,
act = tf.nn.relu,
shape = [3, 3, 3, 64],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv1_1')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 64, 64],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv1_2')
network = tl.layers.PoolLayer(network,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool = tf.nn.max_pool,
name ='pool1')
""" conv2 """
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 64, 128],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv2_1')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 128, 128],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv2_2')
network = tl.layers.PoolLayer(network,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool = tf.nn.max_pool,
name ='pool2')
""" conv3 """
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 128, 256],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv3_1')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 256, 256],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv3_2')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 256, 256],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv3_3')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 256, 256],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv3_4')
network = tl.layers.PoolLayer(network,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool = tf.nn.max_pool,
name ='pool3')
""" conv4 """
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 256, 512],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv4_1')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 512, 512],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv4_2')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 512, 512],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv4_3')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 512, 512],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv4_4')
network = tl.layers.PoolLayer(network,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool = tf.nn.max_pool,
name ='pool4')
""" conv5 """
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 512, 512],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv5_1')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 512, 512],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv5_2')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 512, 512],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv5_3')
network = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [3, 3, 512, 512],
strides = [1, 1, 1, 1],
padding='SAME',
name ='conv5_4')
network = tl.layers.PoolLayer(network,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool = tf.nn.max_pool,
name ='pool5')
""" fc 6~8 """
network = tl.layers.FlattenLayer(network, name='flatten')
network = tl.layers.DenseLayer(network, n_units=4096,
act = tf.nn.relu, name = 'fc6')
network = tl.layers.DenseLayer(network, n_units=4096,
act = tf.nn.relu, name = 'fc7')
network = tl.layers.DenseLayer(network, n_units=1000,
act = tf.identity, name = 'fc8')
print("build model finished: %fs" % (time.time() - start_time))
return network
sess = tf.InteractiveSession()
x = tf.placeholder("float", [None, 224, 224, 3])
network = Vgg19(x)
y = network.outputs
probs = tf.nn.softmax(y, name="prob")
# sess.run(tf.initialize_all_variables())
tl.layers.initialize_global_variables(sess)
# You need to download the pre-trained model - VGG19 NPZ
vgg19_npy_path = "vgg19.npy"
if not os.path.isfile(vgg19_npy_path):
print("Please download vgg19.npz from : https://github.com/machrisaa/tensorflow-vgg")
exit()
npz = np.load(vgg19_npy_path, encoding='latin1').item()
params = []
for val in sorted( npz.items() ):
W = np.asarray(val[1][0])
b = np.asarray(val[1][1])
print(" Loading %s: %s, %s" % (val[0], W.shape, b.shape))
params.extend([W, b])
print("Restoring model from npz file")
tl.files.assign_params(sess, params, network)
img1 = load_image("data/tiger.jpeg") # test data in github
img1 = img1.reshape((1, 224, 224, 3))
start_time = time.time()
prob = sess.run(probs, feed_dict= {x : img1})
print("End time : %.5ss" % (time.time() - start_time))
print_prob(prob[0])
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