输入张量与 Tensorflow 对象检测中的 Python 签名不兼容

如何解决输入张量与 Tensorflow 对象检测中的 Python 签名不兼容

我最近在 Tensorflow 中训练了一个对象检测模型，但由于某种原因，某些图像的输入张量与 Python 签名不兼容。这是我在 google colab 中运行的用于推理的代码：

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')   # Suppress Matplotlib warnings

def load_image_into_numpy_array(path):
    """Load an image from file into a numpy array.

    Puts image into numpy array to feed into tensorflow graph.
    Note that by convention we put it into a numpy array with shape
    (height,width,channels),where channels=3 for RGB.

    Args:
      path: the file path to the image

    Returns:
      uint8 numpy array with shape (img_height,img_width,3)
    """
    return np.array(Image.open(path))

for image_path in img:

    print('Running inference for {}... '.format(image_path),end='')
    image_np=load_image_into_numpy_array(image_path)


    # Things to try:
    # Flip horizontally
    # image_np = np.fliplr(image_np).copy()
    # Convert image to grayscale
    # image_np = np.tile(
    #     np.mean(image_np,2,keepdims=True),(1,1,3)).astype(np.uint8)

    # The input needs to be a tensor,convert it using `tf.convert_to_tensor`.
    input_tensor=tf.convert_to_tensor(image_np)
    # The model expects a batch of images,so add an axis with `tf.newaxis`.
    input_tensor=input_tensor[tf.newaxis,...]

    # input_tensor = np.expand_dims(image_np,0)
    detections=detect_fn(input_tensor)

    # All outputs are batches tensors.
    # Convert to numpy arrays,and take index [0] to remove the batch dimension.
    # We're only interested in the first num_detections.
    num_detections=int(detections.pop('num_detections'))
    detections={key:value[0,:num_detections].numpy()
                   for key,value in detections.items()}
    detections['num_detections']=num_detections

    # detection_classes should be ints.
    detections['detection_classes']=detections['detection_classes'].astype(np.int64)

    image_np_with_detections=image_np.copy()

    viz_utils.visualize_boxes_and_labels_on_image_array(
          image_np_with_detections,detections['detection_boxes'],detections['detection_classes'],detections['detection_scores'],category_index,use_normalized_coordinates=True,max_boxes_to_draw=100,#max number of bounding boxes in the image
          min_score_thresh=.25,#min prediction threshold
          agnostic_mode=False)
    %matplotlib inline
    plt.figure()
    plt.imshow(image_np_with_detections)
    print('Done')
    plt.show()

这是我在运行推理时收到的错误消息：

    Running inference for /content/gdrive/MyDrive/TensorFlow/workspace/training_demo/images/test/image_part_002.png... 
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    <ipython-input-23-5b465e5474df> in <module>()
         40 
         41     # input_tensor = np.expand_dims(image_np,0)
    ---> 42     detections=detect_fn(input_tensor)
         43 
         44     # All outputs are batches tensors.
    
    6 frames
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _convert_inputs_to_signature(inputs,input_signature,flat_input_signature)
       2804       flatten_inputs)):
       2805     raise ValueError("Python inputs incompatible with input_signature:\n%s" %
    -> 2806                      format_error_message(inputs,input_signature))
       2807 
       2808   if need_packing:
    
    ValueError: Python inputs incompatible with input_signature:
      inputs: (
        tf.Tensor(
    [[[[  0   0   0 255]
       [  0   0   0 255]
       [  0   0   0 255]
       ...
       [  0   0   0 255]
       [  0   0   0 255]
       [  0   0   0 255]]
    
      [[  0   0   0 255]
       [  0   0   0 255]
       [  0   0   0 255]
       ...
       [  0   0   0 255]
       [  0   0   0 255]
       [  0   0   0 255]]
    
      [[  0   0   0 255]
       [  0   0   0 255]
       [  0   0   0 255]
       ...
       [  0   0   0 255]
       [  0   0   0 255]
       [  0   0   0 255]]
    
      ...
    
      [[ 34  32  34 255]
       [ 35  33  35 255]
       [ 35  33  35 255]
       ...
       [ 41  38  38 255]
       [ 40  37  37 255]
       [ 40  37  37 255]]
    
      [[ 36  34  36 255]
       [ 35  33  35 255]
       [ 36  34  36 255]
       ...
       [ 41  38  38 255]
       [ 41  38  38 255]
       [ 43  40  40 255]]
    
      [[ 36  34  36 255]
       [ 36  34  36 255]
       [ 37  35  37 255]
       ...
       [ 41  38  38 255]
       [ 40  37  37 255]
       [ 39  36  36 255]]]],shape=(1,1219,1920,4),dtype=uint8))
      input_signature: (
        TensorSpec(shape=(1,None,3),dtype=tf.uint8,name='input_tensor'))

有谁知道我可以转换图像的输入张量的方法，以便我可以对它们进行推理？例如，我知道一张图像，其中推理的气体分辨率为 400x291，而推理不起作用的图像的分辨率为 1920x1219。我使用 SSD MobileNet V1 FPN 640x640 模型进行训练。

解决方法

您的问题是您的输入张量形状的形式为 (1,1219,1920,4)，更准确地说 4 有问题。

第一个元素 1 代表批次大小（在 input_tensor[tf.newaxis,...] 中添加）。

你说对了那部分，但在你实际读取图像的地方，问题就出现了，因为有 4 个通道（假设你读取 RGB-A？）而不是 3（典型的 RGB）或 1（灰度）。

>

我建议您检查图像并强制转换为 RGB，即 Image.open(path).convert('RGB')