"""Inception model, download, and preprocessing.
"""
"""
Copyright 2017 Parag K. Mital. See also NOTICE.md.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import os
import numpy as np
from tensorflow.python.platform import gfile
import tensorflow as tf
import matplotlib.pyplot as plt
from skimage.transform import resize as imresize
from .utils import download_and_extract_tar, download_and_extract_zip
[docs]def inception_download(data_dir='inception', version='v5'):
"""Download a pretrained inception network.
Parameters
----------
data_dir : str, optional
Location of the pretrained inception network download.
version : str, optional
Version of the model: ['v3'] or 'v5'.
Returns
-------
TYPE
Description
"""
if version == 'v3':
download_and_extract_tar(
'https://s3.amazonaws.com/cadl/models/inception-2015-12-05.tgz',
data_dir)
return (os.path.join(data_dir, 'classify_image_graph_def.pb'),
os.path.join(data_dir, 'imagenet_synset_to_human_label_map.txt'))
else:
download_and_extract_zip(
'https://s3.amazonaws.com/cadl/models/inception5h.zip', data_dir)
return (os.path.join(data_dir, 'tensorflow_inception_graph.pb'),
os.path.join(data_dir, 'imagenet_comp_graph_label_strings.txt'))
[docs]def get_inception_model(data_dir='inception', version='v5'):
"""Get a pretrained inception network.
Parameters
----------
data_dir : str, optional
Location of the pretrained inception network download.
version : str, optional
Version of the model: ['v3'] or 'v5'.
Returns
-------
net : dict
{'graph_def': graph_def, 'labels': synsets}
where the graph_def is a tf.GraphDef and the synsets
map an integer label from 0-1000 to a list of names
"""
# Download the trained net
model, labels = inception_download(data_dir, version)
# Parse the ids and synsets
txt = open(labels).readlines()
synsets = [(key, val.strip()) for key, val in enumerate(txt)]
# Load the saved graph
with gfile.GFile(model, 'rb') as f:
graph_def = tf.GraphDef()
try:
graph_def.ParseFromString(f.read())
except:
print('try adding PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python' +
'to environment. e.g.:\n' +
'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python ipython\n' +
'See here for info: ' +
'https://github.com/tensorflow/tensorflow/issues/582')
return {
'graph_def': graph_def,
'labels': synsets,
'preprocess': preprocess,
'deprocess': deprocess
}
[docs]def preprocess(img, crop=True, resize=True, dsize=(299, 299)):
"""Summary
Parameters
----------
img : TYPE
Description
crop : bool, optional
Description
resize : bool, optional
Description
dsize : tuple, optional
Description
Returns
-------
TYPE
Description
"""
if img.dtype != np.uint8:
img *= 255.0
if crop:
crop = np.min(img.shape[:2])
r = (img.shape[0] - crop) // 2
c = (img.shape[1] - crop) // 2
cropped = img[r: r + crop, c: c + crop]
else:
cropped = img
if resize:
rsz = imresize(cropped, dsize, preserve_range=True)
else:
rsz = cropped
if rsz.ndim == 2:
rsz = rsz[..., np.newaxis]
rsz = rsz.astype(np.float32)
# subtract imagenet mean
return (rsz - 117)
[docs]def deprocess(img):
"""Summary
Parameters
----------
img : TYPE
Description
Returns
-------
TYPE
Description
"""
return np.clip(img + 117, 0, 255).astype(np.uint8)
[docs]def test_inception():
"""Loads the inception network and applies it to a test image.
"""
with tf.Session() as sess:
net = get_inception_model()
tf.import_graph_def(net['graph_def'], name='inception')
g = tf.get_default_graph()
names = [op.name for op in g.get_operations()]
x = g.get_tensor_by_name(names[0] + ':0')
softmax = g.get_tensor_by_name(names[-3] + ':0')
from skimage import data
img = preprocess(data.coffee())[np.newaxis]
res = np.squeeze(softmax.eval(feed_dict={x: img}))
print([(res[idx], net['labels'][idx])
for idx in res.argsort()[-5:][::-1]])
"""Let's visualize the network's gradient activation
when backpropagated to the original input image. This
is effectively telling us which pixels contribute to the
predicted class or given neuron"""
pools = [name for name in names if 'pool' in name.split('/')[-1]]
fig, axs = plt.subplots(1, len(pools))
for pool_i, poolname in enumerate(pools):
pool = g.get_tensor_by_name(poolname + ':0')
pool.get_shape()
neuron = tf.reduce_max(pool, 1)
saliency = tf.gradients(neuron, x)
neuron_idx = tf.arg_max(pool, 1)
this_res = sess.run([saliency[0], neuron_idx],
feed_dict={x: img})
grad = this_res[0][0] / np.max(np.abs(this_res[0]))
axs[pool_i].imshow((grad * 128 + 128).astype(np.uint8))
axs[pool_i].set_title(poolname)