ValueError: Cannot feed value of shape (1, 256, 256) for Tensor 'input_x:0', which has shape '(?, 256, 256, 3)'

Question

Code:

from deeplab_v3 import Deeplab_v3  ###
from data_utils import DataSet   ##########
    
    
    import cv2
    import os
    # import tensorflow as tf
    import tensorflow._api.v2.compat.v1 as tf
    tf.disable_v2_behavior()
    import pandas as pd
    import numpy as np
    from color_utils import color_predicts
    from predicts_utils import total_image_predict
    
    from metric_utils import iou
    
    
    class args:
        batch_size = 1
        lr = 2e-4
        test_display = 2000
        weight_decay = 5e-4
        model_name = 'baseline'
        batch_norm_decay = 0.95
        test_image_path = 'dataset/train/images/'#'dataset/origin/5.png'
        test_label_path = 'dataset/train/labels/'#'dataset/origin/5_class.png'
        multi_scale = True # 是否多尺度预测
        gpu_num = 0
        pretraining = False
    
    # 打印以下超参数
    for key in args.__dict__:
        if key.find('__') == -1:
            offset = 20 - key.__len__()
            print(key + ' ' * offset, args.__dict__[key])
    
    # 使用那一块显卡
    os.environ["CUDA_VISIBLE_DEVICES"] = "%d" % args.gpu_num
    
    data_path_df = pd.read_csv('dataset/path_list.csv')
    data_path_df = data_path_df.sample(frac=1) # 第一次打乱
    
    dataset = DataSet(image_path=data_path_df['image'].values, label_path=data_path_df['label'].values)
    model = Deeplab_v3(batch_norm_decay=args.batch_norm_decay)
    
    image = tf.placeholder(tf.float32, [None, 256, 256, 3], name='input_x')
    label = tf.placeholder(tf.int32, [None, 256, 256])
    lr = tf.placeholder(tf.float32, )
    
    logits = model.forward_pass(image)
    logits_prob = tf.nn.softmax(logits=logits, name='logits_prob')
    predicts = tf.argmax(logits, axis=-1, name='predicts')
    
    variables_to_restore = tf.trainable_variables(scope='resnet_v2_50')
    
    # finetune resnet_v2_50的参数(block1到block4)
    restorer = tf.train.Saver(variables_to_restore)
    # cross_entropy
    cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label))
    
    
    # 只将weight加入到weight_decay中
    # https://arxiv.org/pdf/1807.11205.pdf
    # weight_for_weightdecay = []
    # for var in tf.trainable_variables():
    #     if var.name.__contains__('weight'):
    #         weight_for_weightdecay.append(var)
    #         print(var.op.name)
    #     else:
    #         continue
    # l2_norm l2正则化
    with tf.name_scope('weight_decay'):
        l2_loss = args.weight_decay * tf.add_n(
             [tf.nn.l2_loss(tf.cast(v, tf.float32)) for v in tf.trainable_variables()])
    
    
    optimizer = tf.train.AdamOptimizer(learning_rate=lr)
    loss = cross_entropy + l2_loss
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    # 计算梯度
    grads = optimizer.compute_gradients(loss=loss, var_list=tf.trainable_variables())
    # for grad, var in grads:
    #     if grad is not None:
    #         tf.summary.histogram(name='%s_gradients' % var.op.name, values=grad)
    #         tf.summary.histogram(name='%s' % var.op.name, values=var)
    # 梯度裁剪
    # gradients, variables = zip(*grads)
    # gradients, global_norm = tf.clip_by_global_norm(gradients, 5)
    
    #更新梯度
    apply_gradient_op = optimizer.apply_gradients(grads_and_vars=grads, global_step=tf.train.get_or_create_global_step())
    batch_norm_updates_op = tf.group(*tf.get_collection(tf.GraphKeys.UPDATE_OPS))
    train_op = tf.group(apply_gradient_op, batch_norm_updates_op)
    
    saver = tf.train.Saver(tf.all_variables())
    
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    # summary_op = tf.summary.merge_all()
    
    with tf.Session(config=config) as sess:
        sess.run(tf.local_variables_initializer())
        sess.run(tf.global_variables_initializer())
        graph = tf.get_default_graph()
    
        if args.pretraining:
            # finetune resnet_v2_50参数，需要下载权重文件
            restorer.restore(sess, 'ckpts/resnet_v2_50/resnet_v2_50.ckpt')
    
        log_path = 'logs/%s/' % args.model_name
        model_path = 'ckpts/%s/' % args.model_name
        
        if not os.path.exists(model_path): os.makedirs(model_path)
        if not os.path.exists('./logs'): os.makedirs('./logs')
        if not os.path.exists(log_path): os.makedirs(log_path)
            
    
        summary_writer = tf.summary.FileWriter('%s/' % log_path, sess.graph)
    
        learning_rate = args.lr
        for step in range(1, 50001):
            if step == 30000 or step == 40000:
                learning_rate = learning_rate / 10
            x_tr, y_tr = dataset.next_batch(args.batch_size)
    
    
            loss_tr, l2_loss_tr, predicts_tr, _ = sess.run(
                fetches=[cross_entropy, l2_loss, predicts, train_op],
                feed_dict={
                    image: x_tr,
                    label: y_tr,
                    model._is_training: True,
                    lr: learning_rate})
    
            # 前50, 100, 200 看一下是否搞错了
            if (step in [50, 100, 200]) or (step > 0 and step % args.test_display == 0):
    
                test_predict = total_image_predict(
                    ori_image_path=args.test_image_path,
                    input_placeholder=image,
                    logits_prob_node=logits_prob,
                    is_training_placeholder=model._is_training,
                    sess=sess,
                    multi_scale=args.multi_scale
                )
    
                test_label = cv2.imread(args.test_label_path, cv2.IMREAD_GRAYSCALE)
    
                # 保存图片
                cv2.imwrite(filename='%spredict_color_%d.png' % (log_path, step),
                            img=color_predicts(img=test_predict))
    
                result = iou(y_pre=np.reshape(test_predict, -1),
                             y_true=np.reshape(test_label, -1))
    
                print("======================%d======================" % step)
                for key in result.keys():
                    offset = 40 - key.__len__()
                    print(key + ' ' * offset + '%.4f' % result[key])
    
    
                test_summary = tf.Summary(
                    value=[tf.Summary.Value(tag=key, simple_value=result[key]) for key in result.keys()]
                )
    
        enter code here
                # 记录summary
 summary_writer.add_summary(test_summary, step)
 summary_writer.flush()

Error:

Traceback (most recent call last):
  File "D:/ashiyanchengxu/deeplabv3-Tensorflow-master/deeplabv3-Tensorflow-master/train.py", line 128, in <module>
    loss_tr, l2_loss_tr, predicts_tr, _ = sess.run(
  File "D:\anaconda\envs\tf2x\lib\site-packages\tensorflow\python\client\session.py", line 957, in run
    result = self._run(None, fetches, feed_dict, options_ptr,
  File "D:\anaconda\envs\tf2x\lib\site-packages\tensorflow\python\client\session.py", line 1154, in _run
    raise ValueError(
ValueError: Cannot feed value of shape (1, 256, 256) for Tensor 'input_x:0', which has shape '(?, 256, 256, 3)'