Tensorflow Inception resnet v2 input tensor

Question

I'm trying to run this code

import os
import tensorflow as tf
from datasets import imagenet
from nets import inception_resnet_v2
from preprocessing import inception_preprocessing

checkpoints_dir = 'model'

slim = tf.contrib.slim

batch_size = 3
image_size = 299

with tf.Graph().as_default():

with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
    logits, _ = inception_resnet_v2.inception_resnet_v2([1, 299, 299, 3], num_classes=1001, is_training=False)
    probabilities = tf.nn.softmax(logits)

    init_fn = slim.assign_from_checkpoint_fn(
    os.path.join(checkpoints_dir, 'inception_resnet_v2_2016_08_30.ckpt'),
    slim.get_model_variables('InceptionResnetV2'))

    with tf.Session() as sess:
        init_fn(sess)

        imgPath = '.../image_3.jpeg'
        testImage_string = tf.gfile.FastGFile(imgPath, 'rb').read()
        testImage = tf.image.decode_jpeg(testImage_string, channels=3)

        np_image, probabilities = sess.run([testImage, probabilities])
        probabilities = probabilities[0, 0:]
        sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x:x[1])]

        names = imagenet.create_readable_names_for_imagenet_labels()
        for i in range(15):
            index = sorted_inds[i]
            print((probabilities[index], names[index]))

But TF displays an error: ValueError: rank of shape must be at least 4 not: 1

I believe that problem is in input tensor shape [1, 299, 299, 3]. How to input tensor for 3 channel JPEG image???

there is an also one similar question (Using pre-trained inception_resnet_v2 with Tensorflow). I saw in code input_tensor - unfortunatelly there is explanation what is input_tensor. Maybe I'm asking something self-evident but i stuck! Thanks a lot in advance for any advice!

can you verify that your testImage vector is an array of 4 dimensions — Jules G.M., Sep 28 '16 at 21:55
yes, testimage is 4D tensor. If I write `imgPath, testImage_string and test_image` just after `with tf.Graph().as_default()` and instead of `[1, 299, 299, 3]` write `test_image` everything works just fine. My intention is to manually put 4D input tensor and then in session part I want to test model on many different images. TF — Saddam, Sep 28 '16 at 22:58

score 3 · Answer 1 · answered Jan 14 '17 at 10:40

You have to preprocess your image. Here is a code:

import os
import tensorflow as tf
from datasets import imagenet
from nets import inception_resnet_v2
from preprocessing import inception_preprocessing

checkpoints_dir = 'model'

slim = tf.contrib.slim

batch_size = 3
image_size = 299

with tf.Graph().as_default():
    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):

        imgPath = '.../cat.jpg'
        testImage_string = tf.gfile.FastGFile(imgPath, 'rb').read()
        testImage = tf.image.decode_jpeg(testImage_string, channels=3)
        processed_image = inception_preprocessing.preprocess_image(testImage, image_size, image_size, is_training=False)
        processed_images = tf.expand_dims(processed_image, 0)

        logits, _ = inception_resnet_v2.inception_resnet_v2(processed_images, num_classes=1001, is_training=False)
        probabilities = tf.nn.softmax(logits)

        init_fn = slim.assign_from_checkpoint_fn(
        os.path.join(checkpoints_dir, 'inception_resnet_v2_2016_08_30.ckpt'), slim.get_model_variables('InceptionResnetV2'))

        with tf.Session() as sess:
            init_fn(sess)

            np_image, probabilities = sess.run([processed_images, probabilities])
            probabilities = probabilities[0, 0:]
            sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x: x[1])]

            names = imagenet.create_readable_names_for_imagenet_labels()
            for i in range(15):
                index = sorted_inds[i]
                print((probabilities[index], names[index]))

The answer is:

(0.1131034, 'tiger cat')
(0.079478227, 'tabby, tabby cat')
(0.052777905, 'Cardigan, Cardigan Welsh corgi')
(0.030195976, 'laptop, laptop computer')
(0.027841948, 'bathtub, bathing tub, bath, tub')
(0.026694898, 'television, television system')
(0.024981709, 'carton')
(0.024039172, 'Egyptian cat')
(0.018425584, 'tub, vat')
(0.018221909, 'Pembroke, Pembroke Welsh corgi')
(0.015066789, 'skunk, polecat, wood pussy')
(0.01377619, 'screen, CRT screen')
(0.012509955, 'monitor')
(0.012224807, 'mouse, computer mouse')
(0.012188354, 'refrigerator, icebox')

score -1 · Answer 2 · edited May 05 '17 at 02:14

-1

You could use tf.expand_dims(your_tensor_3channel, axis=0) to expand it into batch format.

edited May 05 '17 at 02:14

lmiguelvargasf

63,191
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answered May 04 '17 at 13:43

Selina

1

Tensorflow Inception resnet v2 input tensor

2 Answers2