盗梦空间：如何处理与盗梦空间一起使用的图像

Question

I want to make tensorflow's inception v3 to give out tags for an image. My goal is to convert a JPEG image to input that is accepted by inception neural network. I don't know how to process the images first so that it can run with Google Inception's v3 model. The original tensorflow project is here: https://github.com/tensorflow/models/tree/master/inception

Originally, all the images are in a dataset and the entire dataset is first passed to input() or distorted_inputs() in ImageProcessing.py . The images in dataset are processed and passed to the train() or eval() methods (both of these work). The problem is I want a function to print out tags for one specific image (not dataset).

Below is the code for inference function that is used to generate tag with google inception. inceptionv4 function is a convolutional neural network implemented in tensorflow.

def inference(images, num_classes, for_training=False, restore_logits=True,
              scope=None):
  """Build Inception v3 model architecture.

  See here for reference: http://arxiv.org/abs/1512.00567

  Args:
    images: Images returned from inputs() or distorted_inputs().
    num_classes: number of classes
    for_training: If set to `True`, build the inference model for training.
      Kernels that operate differently for inference during training
      e.g. dropout, are appropriately configured.
    restore_logits: whether or not the logits layers should be restored.
      Useful for fine-tuning a model with different num_classes.
    scope: optional prefix string identifying the ImageNet tower.

  Returns:
    Logits. 2-D float Tensor.
    Auxiliary Logits. 2-D float Tensor of side-head. Used for training only.
  """
  # Parameters for BatchNorm.
  batch_norm_params = {
      # Decay for the moving averages.
      'decay': BATCHNORM_MOVING_AVERAGE_DECAY,
      # epsilon to prevent 0s in variance.
      'epsilon': 0.001,
  }
  # Set weight_decay for weights in Conv and FC layers.
  with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
    with slim.arg_scope([slim.ops.conv2d],
                        stddev=0.1,
                        activation=tf.nn.relu,
                        batch_norm_params=batch_norm_params):
      logits, endpoints = inception_v4(
          images,
          dropout_keep_prob=0.8,
          num_classes=num_classes,
          is_training=for_training,
          scope=scope)

  # Add summaries for viewing model statistics on TensorBoard.
  _activation_summaries(endpoints)

  # Grab the logits associated with the side head. Employed during training.
  auxiliary_logits = endpoints['AuxLogits']

  return logits, auxiliary_logits

This is my attempt to process the image before it is passed to inference function.

  def process_image(self, image_path):
    filename_queue = tf.train.string_input_producer(image_path)
    reader = tf.WholeFileReader()
    key, value = reader.read(filename_queue)

    img = tf.image.decode_jpeg(value)
    height = self.image_size
    width = self.image_size
    image_data = tf.cast(img, tf.float32)
    image_data = tf.reshape(image_data, shape=[1, height, width, 3])
    return image_data

I wanted to process an image file simply so that I can pass it to the inference function. And that inference prints out the tags. The above code didn't work and printed error:

ValueError: Shape () must have rank at least 1

I appreciate if anyone can provide any insight into this problem.

Answer 1

Inception just needs (299,299,3) images with inputs scaled between -1 and 1. See code below. I just change the images using this and put them in a TFRecord ( and then queue ) to run my stuff.

from PIL import Image
import PIL
import numpy as np
def load_image( self, image_path ):
    img = Image.open( image_path )
    newImg = img.resize((299,299), PIL.Image.BILINEAR).convert("RGB")
    data = np.array( newImg.getdata() )
    return 2*( data.reshape( (newImg.size[0], newImg.size[1], 3) ).astype( np.float32 )/255 ) - 1