模型训练完毕，在win10系统下加载pb模型运行测试程序时，报错：
Process finished with exit code -1073741819 (0xC0000005)因为我把整个测试图片都加载进来，和底库中的1000id特征进行对比，把batch size改为10，这个问题就解决了，又报另外的错误：
Process finished with exit code -1073740791 (0xC0000409)后来发现是batch size 读取程序有问题，下面是改正后的代码：
# -*- coding: utf-8 -*- import os import argparse import time import tensorflow as tf import cv2 from sklearn.preprocessing import normalize import numpy as np # 1：1000 test class test_slim: def __init__(self, batch_size=1): self.yunce_path = r'F:\depth-face\test_all_id.tfrecords' self.ids_path = 'ids_1k.tfrecords' self.batch_size = batch_size def pre_process_orbbec(self, example_proto):# 读取整个测试集-> model features = { 'id': tf.FixedLenFeature([], tf.int64), 'label': tf.FixedLenFeature([], tf.int64), 'color_raw': tf.FixedLenFeature([], tf.string), } features = tf.parse_single_example(example_proto, features) color = tf.image.decode_jpeg(features['color_raw']) color = tf.cast(color, tf.float32) #color = tf.image.crop_to_bounding_box(color, 0, 0, 56, 112) ir = color depth = color label = tf.cast(features['label'], tf.int64) # return color, ir, depth, label return color, label def get_orbbec_batch(self, batch_size): # 读取batch dataset = tf.data.TFRecordDataset(self.yunce_path) dataset = dataset.map(self.pre_process_orbbec) dataset = dataset.batch(batch_size) # dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size)) dataset = dataset.repeat(100) iterator = dataset.make_one_shot_iterator() next_element = iterator.get_next() return next_element def cacu_roc(self,threshold, dis_list, issame_list): predict_issame = np.less(dis_list, threshold).squeeze() tp = np.sum(np.logical_and(predict_issame, issame_list)) fp = np.sum(np.logical_and(predict_issame, np.logical_not(issame_list))) tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(issame_list))) fn = np.sum(np.logical_and(np.logical_not(predict_issame), issame_list)) tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn) fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn) acc = float(tp + tn) / len(dis_list) return acc, tpr, fpr if __name__ == '__main__': embedding_1k = np.load(r'F:\depth-face\ids_se_112_embedding.npy')#加载1000id embedding #test_data = test_slim() print('length of embedding1k is:', len(embedding_1k)) embedding_list = [] #特征列表 test_data = test_slim(batch_size=1)#一次取一张图片 threshold = 0.01 pred_right = 0 tp = 0 tn = 0 fp = 0 fn = 0 ids = list(np.arange(1000)) data_test_batch = test_data.get_orbbec_batch(batch_size=1) print(data_test_batch) # 加载pb文件 with tf.gfile.GFile(r'F:\depth-face\ckpt_resnet50_se\train_resnet50_se.pb', 'rb') as f: restored_graph_def = tf.GraphDef() restored_graph_def.ParseFromString(f.read()) tf.import_graph_def(restored_graph_def, input_map=None, return_elements=None, name='') graph = tf.get_default_graph() input_node = graph.get_tensor_by_name('input:0') output_node = graph.get_tensor_by_name('output:0') print('output_node:',output_node) #with tf.Session(config=tf.ConfigProto(allow_soft_place=True, gpu_options=tf.GPUOptions(allow_growth=True))) as sess: with tf.Session() as sess: for i in range(89298): # 89298为测试图片的总数 dis_list = [] color, label = sess.run(data_test_batch)#读取图片与对应的label #print(color.shape) color = color[0] #print('color:',color) #print('label:',label) embedding = sess.run(output_node, feed_dict={input_node: [color]}) embedding = normalize(embedding)#将特征进行归一化 for index, em in enumerate(embedding_1k):#index就是对应图片特征的id diff = np.subtract(embedding, em) dis = np.sum(np.square(diff)) dis = np.sqrt(dis) if index % 1000 == 0: print(index) dis_list.append(dis) #print('length of dis_list is:', len(dis_list)) if np.min(dis_list) <= threshold:#预测图片与底库id的特征足够接近 min_val = np.min(dis_list) print(min_val) #pred_id = np.argmin(dis_list) pred_id = dis_list.index(min_val) print('pred_id', pred_id) if pred_id in ids: tp = tp + 1 else: fp = fp + 1 else: pred_id = np.argmin(dis_list) if pred_id in ids: fn = fn + 1 else: tn = tn + 1 '''if pred_id == label: pred_right = pred_right + 1''' tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn) fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn) acc = float(tp + tn) / len(dis_list) print('acc:', acc) print('tpr:', tpr) print('fpr:', fpr)加更：后来又出现了这个错误，通过指定显卡解决了问题，参考：链接
import os os.environ["CUDA_VISIBLE_DEVICES"] = "2"以上对我个人是有效的，造成这个错误的原因很多，需要慢慢排查。
