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#! /usr/bin/python

# -*- coding: utf-8 -*-

import json

import os

import time

import numpy as np

import tensorflow as tf

import tensorlayer as tl

from tensorlayer.layers import (ConcatLayer, Conv2d, DropoutLayer, GlobalMeanPool2d, InputLayer, MaxPool2d)

tf.logging.set_verbosity(tf.logging.DEBUG)

tl.logging.set_verbosity(tl.logging.DEBUG)

MODEL_PATH = os.path.join("models", "squeezenet.npz")

def decode_predictions(preds, top=5): # keras.applications.resnet50

fpath = os.path.join("data", "imagenet_class_index.json")

if tl.files.file_exists(fpath) is False:

raise Exception(

"{} / download imagenet_class_index.json from: https://github.com/tensorlayer/tensorlayer/tree/master/example/data"

)

if isinstance(preds, np.ndarray) is False:

preds = np.asarray(preds)

if len(preds.shape) != 2 or preds.shape[1] != 1000:

raise ValueError(

'`decode_predictions` expects '

'a batch of predictions '

'(i.e. a 2D array of shape (samples, 1000)). '

'Found array with shape: ' + str(preds.shape)

)

with open(fpath) as f:

CLASS_INDEX = json.load(f)

results = []

for pred in preds:

top_indices = pred.argsort()[-top:][::-1]

result = [tuple(CLASS_INDEX[str(i)]) + (pred[i], ) for i in top_indices]

result.sort(key=lambda x: x[2], reverse=True)

results.append(result)

return results

def squeezenet(x, is_train=True, reuse=False):

# model from: https://github.com/wohlert/keras-squeezenet

# https://github.com/DT42/squeezenet_demo/blob/master/model.py

with tf.variable_scope("squeezenet", reuse=reuse):

with tf.variable_scope("input"):

n = InputLayer(x)

# n = Conv2d(n, 96, (7,7),(2,2),tf.nn.relu,'SAME',name='conv1')

n = Conv2d(n, 64, (3, 3), (2, 2), tf.nn.relu, 'SAME', name='conv1')

n = MaxPool2d(n, (3, 3), (2, 2), 'VALID', name='max')

with tf.variable_scope("fire2"):

n = Conv2d(n, 16, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='squeeze1x1')

n1 = Conv2d(n, 64, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='expand1x1')

n2 = Conv2d(n, 64, (3, 3), (1, 1), tf.nn.relu, 'SAME', name='expand3x3')

n = ConcatLayer([n1, n2], -1, name='concat')

with tf.variable_scope("fire3"):

n = Conv2d(n, 16, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='squeeze1x1')

n1 = Conv2d(n, 64, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='expand1x1')

n2 = Conv2d(n, 64, (3, 3), (1, 1), tf.nn.relu, 'SAME', name='expand3x3')

n = ConcatLayer([n1, n2], -1, name='concat')

n = MaxPool2d(n, (3, 3), (2, 2), 'VALID', name='max')

with tf.variable_scope("fire4"):

n = Conv2d(n, 32, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='squeeze1x1')

n1 = Conv2d(n, 128, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='expand1x1')

n2 = Conv2d(n, 128, (3, 3), (1, 1), tf.nn.relu, 'SAME', name='expand3x3')

n = ConcatLayer([n1, n2], -1, name='concat')

with tf.variable_scope("fire5"):

n = Conv2d(n, 32, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='squeeze1x1')

n1 = Conv2d(n, 128, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='expand1x1')

n2 = Conv2d(n, 128, (3, 3), (1, 1), tf.nn.relu, 'SAME', name='expand3x3')

n = ConcatLayer([n1, n2], -1, name='concat')

n = MaxPool2d(n, (3, 3), (2, 2), 'VALID', name='max')

with tf.variable_scope("fire6"):

n = Conv2d(n, 48, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='squeeze1x1')

n1 = Conv2d(n, 192, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='expand1x1')

n2 = Conv2d(n, 192, (3, 3), (1, 1), tf.nn.relu, 'SAME', name='expand3x3')

n = ConcatLayer([n1, n2], -1, name='concat')

with tf.variable_scope("fire7"):

n = Conv2d(n, 48, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='squeeze1x1')

n1 = Conv2d(n, 192, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='expand1x1')

n2 = Conv2d(n, 192, (3, 3), (1, 1), tf.nn.relu, 'SAME', name='expand3x3')

n = ConcatLayer([n1, n2], -1, name='concat')

with tf.variable_scope("fire8"):

n = Conv2d(n, 64, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='squeeze1x1')

n1 = Conv2d(n, 256, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='expand1x1')

n2 = Conv2d(n, 256, (3, 3), (1, 1), tf.nn.relu, 'SAME', name='expand3x3')

n = ConcatLayer([n1, n2], -1, name='concat')

with tf.variable_scope("fire9"):

n = Conv2d(n, 64, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='squeeze1x1')

n1 = Conv2d(n, 256, (1, 1), (1, 1), tf.nn.relu, 'SAME', name='expand1x1')

n2 = Conv2d(n, 256, (3, 3), (1, 1), tf.nn.relu, 'SAME', name='expand3x3')

n = ConcatLayer([n1, n2], -1, name='concat')

with tf.variable_scope("output"):

n = DropoutLayer(n, keep=0.5, is_fix=True, is_train=is_train, name='drop1')

n = Conv2d(n, 1000, (1, 1), (1, 1), padding='VALID', name='conv10') # 13, 13, 1000

n = GlobalMeanPool2d(n)

return n

x = tf.placeholder(tf.float32, (None, 224, 224, 3))

n = squeezenet(x, False, False)

softmax = tf.nn.softmax(n.outputs)

n.print_layers()

n.print_params(False)

sess = tf.InteractiveSession()

tl.layers.initialize_global_variables(sess)

if tl.files.file_exists(MODEL_PATH):

tl.files.load_and_assign_npz(sess=sess, name=MODEL_PATH, network=n)

else:

raise Exception(

"please download the pre-trained squeezenet.npz from https://github.com/tensorlayer/pretrained-models"

)

img = tl.vis.read_image('data/tiger.jpeg', '')

img = tl.prepro.imresize(img, (224, 224))

prob = sess.run(softmax, feed_dict={x: [img]})[0] # the 1st time need time to compile

start_time = time.time()

prob = sess.run(softmax, feed_dict={x: [img]})[0]

print(" End time : %.5ss" % (time.time() - start_time))

print('Predicted:', decode_predictions([prob], top=3)[0])

tl.files.save_npz(n.all_params, name=MODEL_PATH, sess=sess)