import tensorflow as tf

import tensorlayer as tl

slim = tf.contrib.slim

from tensorflow.contrib.slim.python.slim.nets.alexnet import alexnet_v2

from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base, inception_v3, inception_v3_arg_scope

import skimage

import skimage.io

import skimage.transform

import time

from data.imagenet_classes import *

import numpy as np

def load_image(path):

return resized_img

def print_prob(prob):

return top1

x = tf.placeholder(tf.float32, shape=[None, 299, 299, 3])

net_in = tl.layers.InputLayer(x, name='input_layer')

with slim.arg_scope(inception_v3_arg_scope()):

## Alternatively, you should implement inception_v3 without TensorLayer as follow.

# logits, end_points = inception_v3(X, num_classes=1001,

# is_training=False)

network = tl.layers.SlimNetsLayer(layer=net_in, slim_layer=inception_v3,

slim_args= {

'num_classes' : 1001,

'is_training' : False,

# 'dropout_keep_prob' : 0.8, # for training

# 'min_depth' : 16,

# 'depth_multiplier' : 1.0,

# 'prediction_fn' : slim.softmax,

# 'spatial_squeeze' : True,

# 'reuse' : None,

# 'scope' : 'InceptionV3'

},

name='InceptionV3' # <-- the name should be the same with the ckpt model

)

saver = tf.train.Saver()

sess = tf.InteractiveSession()

sess.run(tf.initialize_all_variables())

saver.restore(sess, "inception_v3.ckpt") # download from https://github.com/tensorflow/models/tree/master/slim#Install

print("Model Restored")

network.print_params(False)

from scipy.misc import imread, imresize

y = network.outputs

probs = tf.nn.softmax(y)

img1 = load_image("data/puzzle.jpeg") # test data in github

img1 = img1.reshape((1, 299, 299, 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][1:]) # Note : as it have 1001 outputs, the 1st output is nothing