Image processing ValueError:无法将输入数组从形状（2224224,3）广播到形状（2224224）
TensorFlow-如何在不同的测试数据集上使用经过训练的模型进行预测？我在做图像分割。预测结果有不同的维度，给我带来了困难。任何帮助Image processing ValueError:无法将输入数组从形状（2224224,3）广播到形状（2224224）,image-processing,tensorflow,prediction,Image Processing,Tensorflow,Prediction,TensorFlow-如何在不同的测试数据集上使用经过训练的模型进行预测？我在做图像分割。预测结果有不同的维度，给我带来了困难。任何帮助 def main(argv=None): keep_probability = tf.placeholder(tf.float32, name="keep_probabilty") image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="
def main(argv=None):
keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
annotation = tf.placeholder(tf.int32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 1], name="annotation")
pred_annotation, logits = inference(image, keep_probability)
tf.summary.image("input_image", image, max_outputs=2)
tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=tf.squeeze(annotation, squeeze_dims=[3]),
name="entropy")))
tf.summary.scalar("entropy", loss)
trainable_var = tf.trainable_variables()
if FLAGS.debug:
for var in trainable_var:
utils.add_to_regularization_and_summary(var)
train_op = train(loss, trainable_var)
print("Setting up summary op...")
summary_op = tf.summary.merge_all()
print("Setting up image reader...")
train_records, valid_records, test_records= scene_parsing.read_dataset(FLAGS.data_dir)
print(len(train_records))
print(len(valid_records))
print(len(test_records))
print("Setting up dataset reader")
image_options = {'resize': True, 'resize_size': IMAGE_SIZE}
if FLAGS.mode == 'train':
train_dataset_reader = dataset.BatchDatset(train_records, image_options)
validation_dataset_reader = dataset.BatchDatset(valid_records, image_options)
test_dataset_reader = dataset.BatchDatset(test_records, image_options)
sess = tf.Session()
print("Setting up Saver...")
saver = tf.train.Saver()
summary_writer = tf.summary.FileWriter(FLAGS.logs_dir, sess.graph)
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
if FLAGS.mode == "train":
for itr in xrange(MAX_ITERATION):
train_images, train_annotations = train_dataset_reader.next_batch(FLAGS.batch_size)
feed_dict = {image: train_images, annotation: train_annotations, keep_probability: 0.85}
sess.run(train_op, feed_dict=feed_dict)
if itr % 10 == 0:
train_loss, summary_str = sess.run([loss, summary_op], feed_dict=feed_dict)
print("Step: %d, Train_loss:%g" % (itr, train_loss))
summary_writer.add_summary(summary_str, itr)
if itr % 100 == 0:
valid_images, valid_annotations = validation_dataset_reader.next_batch(FLAGS.batch_size)
valid_loss = sess.run(loss, feed_dict={image: valid_images, annotation: valid_annotations,
keep_probability: 1.0})
print("%s ---> Validation_loss: %g" % (datetime.datetime.now(), valid_loss))
saver.save(sess, FLAGS.logs_dir + "model.ckpt", itr)
elif FLAGS.mode == "predict":
predict_dataset_reader = dataset.BatchDatset(train_records, image_options)
test_images = predict_dataset_reader.get_random_batch(FLAGS.batch_size)
pred = sess.run(pred_annotation, feed_dict={image: test_images,
keep_probability: 1.0})
#test_annotations = np.squeeze(test_annotations, axis=3)
pred = np.squeeze(pred, axis=3)
for itr in range(FLAGS.batch_size):
utils.save_image(test_images[itr].astype(np.uint8), FLAGS.logs_dir, name="inp_" + str(20+itr))
utils.save_image(pred[itr].astype(np.uint8), FLAGS.logs_dir, name="pred_" + str(20+itr))
print("Saved image: %d" % itr)
if __name__ == "__main__":
tf.app.run()
错误出现在“预测”，因为“列车”工作正常
您是否确保列车注释是灰度图像（而不是RGB）？错误出现在“预测”上，因为“列车”工作正常。问题似乎出现在这一行中引用的函数中
pred\u annotation，logits=推断（图像，保持概率）[22224224]sess.run（）image