ValueError: Cannot feed value of shape (1, 233, 472, 4) for Tensor 'image_tensor:0', which has shape '(?, ?, ?, 3)'

Question

我是 TensorFlow 的新手，我在某些图像上遇到此估值错误，而在其他图像上工作正常。代码有问题吗？

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

from object_detection.utils import label_map_util

from object_detection.utils import visualization_utils as vis_util

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

MODEL_NAME = 'new_graph'  # change to whatever folder has the new graph
# MODEL_FILE = MODEL_NAME + '.tar.gz'   # these lines not needed as we are using our own model
# DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')  # our labels are in training/object-detection.pbkt

NUM_CLASSES = 3

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)


def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape(
        (im_height, im_width, 4)).astype(np.uint8)


PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.png'.format(i)) for i in range(1, 8)]
# adjust range for # of images in folder

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)


with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        i = 0
        for image_path in TEST_IMAGE_PATHS:
            image = Image.open(image_path)
            # the array based representation of the image will be used later in order to prepare the
            # result image with boxes and labels on it.
            # # image = np.reshape(image, ())
            # print(image)
            image_np = load_image_into_numpy_array(image)
            # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
            image_np_expanded = np.expand_dims(image_np, axis=0)
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            # Each box represents a part of the image where a particular object was detected.
            boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
            # Each score represent how level of confidence for each of the objects.
            # Score is shown on the result image, together with the class label.
            scores = detection_graph.get_tensor_by_name('detection_scores:0')
            classes = detection_graph.get_tensor_by_name('detection_classes:0')
            num_detections = detection_graph.get_tensor_by_name('num_detections:0')
            # Actual detection.
            (boxes, scores, classes, num_detections) = sess.run(
                [boxes, scores, classes, num_detections],
                feed_dict={image_tensor: image_np_expanded})
            # Visualization of the results of a detection.
            vis_util.visualize_boxes_and_labels_on_image_array(
                image_np,
                np.squeeze(boxes),
                np.squeeze(classes).astype(np.int32),
                np.squeeze(scores),
                category_index,
                use_normalized_coordinates=True,
                line_thickness=8)

            plt.figure(figsize=IMAGE_SIZE)
            plt.imshow(image_np)    # matplotlib is configured for command line only so we save the outputs instead
            plt.savefig("outputs/detection_output{}.png".format(i))  # create an outputs folder for the images to be saved
            i = i+1  # this was a quick fix for iteration, create a pull request if you'd like

错误：

回溯（最近调用最后）：文件“custom_model_images.py”，第 82 行，位于 feed_dict={image_tensor: image_np_expanded})

文件“C:\Python36\lib\site-packages\tensorflow_core\python\client\session.py”，第 956 行，在运行 run_metadata_ptr)

文件“C:\Python36\lib\site-packages\tensorflow_core\python\client\session.py”，第 1156 行，位于 _运行 (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))

ValueError：无法为 Tensor 'image_tensor:0' 提供形状 (1, 233, 472, 4) 的值，其形状为“(?, ?, ?, 3)”

Answer 1

看起来你的一些图像有三个通道，其他的有四个通道（可能有 alpha）。您的模型需要三个通道

ValueError: Cannot feed value of shape (1, 233, 472, 4) for Tensor 'image_tensor:0', which has shape '(?, ?, ?, 3)'

ValueError: Cannot feed value of shape (1, 233, 472, 4) for Tensor 'image_tensor:0', which has shape '(?, ?, ?, 3)'

python

tensorflow

valueerror