Assign requires shapes of both tensors to match. lhs shape= [2048,40] rhs shape= [2048,84]

I am trying to run the demo for another data named “vedai” which has 9 classes. But when I run the demo it doesn’t show the pictures, and when I run the demo with the new data, it shows an error as:

Assign requires shapes of both tensors to match. lhs shape= [2048,40] rhs shape= [2048,84]

My data has 9 classes (9 + 1 for background = 10 and 10 * 4 =40). I think I should change a 21 to 9 somewhere, but I don’t know where.

##This is my code for demo:

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import _init_paths
from model.config import cfg
from model.test import im_detect
from model.nms_wrapper import nms

from utils.timer import Timer
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import os, cv2
import argparse

from nets.vgg16 import vgg16
from nets.resnet_v1 import resnetv1

CLASSES = ('__background__',
           'car', 'pickup', 'tractor', 'boat',
           'truck', 'campingcar', 'van', 'other', 'plane')

NETS = {'vgg16': ('vgg16_faster_rcnn_iter_5000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',)}
DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)}

def vis_detections(im, class_name, dets, ax, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return

    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]

        ax.add_patch(
            plt.Rectangle((bbox[0], bbox[1]),
                          bbox[2] - bbox[0],
                          bbox[3] - bbox[1], fill=False,
                          edgecolor='red', linewidth=3.5)
            )
        ax.text(bbox[0], bbox[1] - 2,
                '{:s} {:.3f}'.format(class_name, score),
                bbox=dict(facecolor='blue', alpha=0.5),
                fontsize=14, color='white')

    ax.set_title(('{} detections with '
                  'p({} | box) >= {:.1f}').format(class_name, class_name,
                                                  thresh),
                  fontsize=14)
    plt.axis('off')
    plt.tight_layout()
    plt.draw()

def vedai(sess, net, image_name):
    """Detect object classes in an image using pre-computed object proposals."""

    # Load the vedai image
    im_file = os.path.join(cfg.DATA_DIR, 'vedai', image_name)
    im = cv2.imread(im_file)

    # Detect all object classes and regress object bounds
    timer = Timer()
    timer.tic()
    scores, boxes = im_detect(sess, net, im)
    timer.toc()
    print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))

    # Visualize detections for each class
    im = im[:, :, (2, 1, 0)]
    fig, ax = plt.subplots(figsize=(12, 12))
    ax.imshow(im, aspect='equal')

    CONF_THRESH = 0.8
    NMS_THRESH = 0.3
    for cls_ind, cls in enumerate(CLASSES[1:]):
        cls_ind += 1 # because we skipped background
        cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
        cls_scores = scores[:, cls_ind]
        dets = np.hstack((cls_boxes,
                          cls_scores[:, np.newaxis])).astype(np.float32)
        keep = nms(dets, NMS_THRESH)
        dets = dets[keep, :]
        vis_detections(im, cls, dets, ax, thresh=CONF_THRESH)

def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN vedai_test')
    parser.add_argument('--net', dest='vedai_net', help='Network to use [vgg16 res101]',
                        choices=NETS.keys(), default='res101')
    parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]',
                        choices=DATASETS.keys(), default='pascal_voc_0712')
    args = parser.parse_args()

    return args

if __name__ == '__main__':
    cfg.TEST.HAS_RPN = True  # Use RPN for proposals
    args = parse_args()

    # model path
    vedainet = args.vedai_net
    dataset = args.dataset
    tfmodel = os.path.join('output', vedainet, DATASETS[dataset][0], 'default',
                              NETS[vedainet][0])


    if not os.path.isfile(tfmodel + '.meta'):
        raise IOError(('{:s} not found.\nDid you download the proper networks from '
                       'our server and place them properly?').format(tfmodel + '.meta'))

    # set config
    tfconfig = tf.ConfigProto(allow_soft_placement=True)
    tfconfig.gpu_options.allow_growth=True

    # init session
    sess = tf.Session(config=tfconfig)
    # load network
    if vedainet == 'vgg16':
        net = vgg16(batch_size=1)
    elif vedainet == 'res101':
        net = resnetv1(batch_size=1, num_layers=101)
    else:
        raise NotImplementedError
    net.create_architecture(sess, "TEST", 10,
                          tag='default', anchor_scales=[8, 16, 32])
    saver = tf.train.Saver()
    saver.restore(sess, tfmodel)

    print('Loaded network {:s}'.format(tfmodel))

    im_names = ['00000024_co.png', '00000028_co.png', 
		'00000034_co.png', '00000039_co.png', 
		'00000341_co.png', '00000365_co.png',
		'00001233.png', '00001237.png']

    for im_name in im_names:
        print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
        print('vedai for data/vedai/{}'.format(im_name))
        vedai(sess, net, im_name)

    plt.show()

[heyan4869]

@zqdeepbluesky just had this issue as well, mine is because I didn’t change the number of classes argument in net.create_architecture(). In the above code, net.create_architecture(sess, "TEST", 10, tag='default', anchor_scales=[8, 16, 32]) should be net.create_architecture(sess, "TEST", len(CLASSES), tag='default', anchor_scales=[8, 16, 32])

[zqdeepbluesky]

@hadi-ghnd hi,I met the same error,can you tell me how to fix it?thanks so much.