tensorlayer/example/tutorial_vgg19.py

#! /usr/bin/python
	# -*- coding: utf-8 -*-
	"""
	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.
	"""
	import os
	import time
	import numpy as np
	import skimage
	import skimage.io
	import skimage.transform
	import tensorflow as tf
	import tensorlayer as tl
	from tensorlayer.layers import *
	tf.logging.set_verbosity(tf.logging.DEBUG)
	tl.logging.set_verbosity(tl.logging.DEBUG)
	try:
	from tensorlayer.models.imagenet_classes import *
	except Exception as e:
	raise Exception(
	"{} / download the file from: https://github.com/tensorlayer/tensorlayer/tree/master/example/data".format(e)
	)
	VGG_MEAN = [103.939, 116.779, 123.68]
	MODEL_DIR = "models"
	MODEL_NAME = "vgg19.npy"
	MODEL_PATH = os.path.join(MODEL_DIR, MODEL_NAME)
	def load_image(path):
	# load image
	img = skimage.io.imread(path)
	img = img / 255.0
	if ((0 <= img).all() and (img <= 1.0).all()) is False:
	raise Exception("image value should be [0, 1]")
	# 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), anti_aliasing=False)
	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
	red, green, blue = tf.split(rgb_scaled, 3, 3)
	if red.get_shape().as_list()[1:] != [224, 224, 1]:
	raise Exception("image size unmatch")
	if green.get_shape().as_list()[1:] != [224, 224, 1]:
	raise Exception("image size unmatch")
	if blue.get_shape().as_list()[1:] != [224, 224, 1]:
	raise Exception("image size unmatch")
	bgr = tf.concat([
	blue - VGG_MEAN[0],
	green - VGG_MEAN[1],
	red - VGG_MEAN[2],
	], axis=3)
	if bgr.get_shape().as_list()[1:] != [224, 224, 3]:
	raise Exception("image size unmatch")
	# input layer
	net_in = InputLayer(bgr, name='input')
	# conv1
	net = Conv2dLayer(net_in, act=tf.nn.relu, shape=[3, 3, 3, 64], strides=[1, 1, 1, 1], padding='SAME', name='conv1_1')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 64, 64], strides=[1, 1, 1, 1], padding='SAME', name='conv1_2')
	net = PoolLayer(net, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool1')
	# conv2
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 64, 128], strides=[1, 1, 1, 1], padding='SAME', name='conv2_1')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 128, 128], strides=[1, 1, 1, 1], padding='SAME', name='conv2_2')
	net = PoolLayer(net, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool2')
	# conv3
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 128, 256], strides=[1, 1, 1, 1], padding='SAME', name='conv3_1')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 256, 256], strides=[1, 1, 1, 1], padding='SAME', name='conv3_2')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 256, 256], strides=[1, 1, 1, 1], padding='SAME', name='conv3_3')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 256, 256], strides=[1, 1, 1, 1], padding='SAME', name='conv3_4')
	net = PoolLayer(net, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool3')
	# conv4
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 256, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv4_1')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv4_2')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv4_3')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv4_4')
	net = PoolLayer(net, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool4')
	# conv5
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv5_1')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv5_2')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv5_3')
	net = Conv2dLayer(net, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv5_4')
	net = PoolLayer(net, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool5')
	# fc 6~8
	net = FlattenLayer(net, name='flatten')
	net = DenseLayer(net, n_units=4096, act=tf.nn.relu, name='fc6')
	net = DenseLayer(net, n_units=4096, act=tf.nn.relu, name='fc7')
	net = DenseLayer(net, n_units=1000, act=None, name='fc8')
	print("build model finished: %fs" % (time.time() - start_time))
	return net
	def Vgg19_simple_api(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
	red, green, blue = tf.split(rgb_scaled, 3, 3)
	if red.get_shape().as_list()[1:] != [224, 224, 1]:
	raise Exception("image size unmatch")
	if green.get_shape().as_list()[1:] != [224, 224, 1]:
	raise Exception("image size unmatch")
	if blue.get_shape().as_list()[1:] != [224, 224, 1]:
	raise Exception("image size unmatch")
	bgr = tf.concat([
	blue - VGG_MEAN[0],
	green - VGG_MEAN[1],
	red - VGG_MEAN[2],
	], axis=3)
	if bgr.get_shape().as_list()[1:] != [224, 224, 3]:
	raise Exception("image size unmatch")
	# input layer
	net_in = InputLayer(bgr, name='input')
	# conv1
	net = Conv2d(net_in, 64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_1')
	net = Conv2d(net, n_filter=64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_2')
	net = MaxPool2d(net, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool1')
	# conv2
	net = Conv2d(net, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_1')
	net = Conv2d(net, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_2')
	net = MaxPool2d(net, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool2')
	# conv3
	net = Conv2d(net, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_1')
	net = Conv2d(net, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_2')
	net = Conv2d(net, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_3')
	net = Conv2d(net, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_4')
	net = MaxPool2d(net, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool3')
	# conv4
	net = Conv2d(net, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_1')
	net = Conv2d(net, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_2')
	net = Conv2d(net, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_3')
	net = Conv2d(net, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_4')
	net = MaxPool2d(net, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool4')
	# conv5
	net = Conv2d(net, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_1')
	net = Conv2d(net, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_2')
	net = Conv2d(net, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_3')
	net = Conv2d(net, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_4')
	net = MaxPool2d(net, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool5')
	# fc 6~8
	net = FlattenLayer(net, name='flatten')
	net = DenseLayer(net, n_units=4096, act=tf.nn.relu, name='fc6')
	net = DenseLayer(net, n_units=4096, act=tf.nn.relu, name='fc7')
	net = DenseLayer(net, n_units=1000, act=None, name='fc8')
	print("build model finished: %fs" % (time.time() - start_time))
	return net
	sess = tf.InteractiveSession()
	x = tf.placeholder("float", [None, 224, 224, 3])
	# net = Vgg19(x)
	net = Vgg19_simple_api(x)
	y = net.outputs
	probs = tf.nn.softmax(y, name="prob")
	tl.layers.initialize_global_variables(sess)
	# You need to download the pre-trained model - VGG19 NPY
	if not os.path.isfile(MODEL_PATH):
	print("Please download vgg19.npy from : https://github.com/machrisaa/tensorflow-vgg")
	exit()
	npy_file = np.load(MODEL_PATH, encoding='latin1').item()
	params = []
	for val in sorted(npy_file.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 npy file")
	tl.files.assign_params(sess, params, net)
	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])