TensorFlow 训练模型保存四个文件 逃离我推掉我的手 2021-11-09 09:01 458阅读 0赞 if mAP > best_mAP: best_mAP = mAP saver_best.save(sess, args.save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format( epoch, int(__global_step), best_mAP, val_loss_total.average, __lr)) 如上部分代码，当训练结果比预设的best\_mAP好，则保存此时的TensorFlow 训练模型,此时，在第几个epoch，第几个global\_step,最好的best\_mAP值，平均验证损失val\_loss\_total.average，lr。 **tf.train.Saver().save(sess, 'ckpts/')在ckpts/ 路径下主要保存四个文件checkpoint：** **checkpoint:model.ckpt.data-00000-of-00001:** 某个ckpt的数据文件，保存每个变量的取值，保存的是网络的权值，偏置，操作等等。 **model.ckpt.index ：**某个ckpt的index文件 二进制 或者其他格式 不可直接查看 。是一个不可变得字符串表，每一个键都是张量的名称，它的值是一个序列化的BundleEntryProto。 每个BundleEntryProto描述张量的元数据：“数据”文件中的哪个文件包含张量的内容，该文件的偏移量，校验和，一些辅助数据等等。 **model.ckpt.meta：**某个ckpt的meta数据 二进制 或者其他格式 不可直接查看，保存了TensorFlow计算图的结构信息。model.ckpt-200.meta文件保存的是图结构，通俗地讲就是神经网络的网络结构。一般而言网络结构是不会发生改变，所以可以只保存一个就行了。我们可以使用下面的代码只在第一次保存meta文件。 **checkpoint：**记录训练较好的几次训练结果 本人训练后的checkpoint文件内容，如下（Epoch 26,30,34,94,98,）： model_checkpoint_path: "best_model_Epoch_98_step_50291_mAP_0.7358_loss_3.6063_lr_1e-05" all_model_checkpoint_paths: "best_model_Epoch_26_step_13715_mAP_0.7163_loss_3.3344_lr_0.0001" all_model_checkpoint_paths: "best_model_Epoch_30_step_15747_mAP_0.7211_loss_3.3941_lr_0.0001" all_model_checkpoint_paths: "best_model_Epoch_34_step_17779_mAP_0.7317_loss_3.3543_lr_3e-05" all_model_checkpoint_paths: "best_model_Epoch_94_step_48259_mAP_0.7328_loss_3.5932_lr_1e-05" all_model_checkpoint_paths: "best_model_Epoch_98_step_50291_mAP_0.7358_loss_3.6063_lr_1e-05" **模型加载需要利用Saver.restore方法。可以加载固定参数，也可以加在所有参数。** tf.train.Saver.restore(sess,model_path) 训练过程保存了大量tensorflow模型 : -4.2$ ls best_model_Epoch_10_step_5664_mAP_0.0729_loss_8.1562_lr_0.0001.data-00000-of-00001 best_model_Epoch_10_step_5664_mAP_0.0729_loss_8.1562_lr_0.0001.index best_model_Epoch_10_step_5664_mAP_0.0729_loss_8.1562_lr_0.0001.meta best_model_Epoch_12_step_6694_mAP_0.0748_loss_7.4896_lr_0.0001.data-00000-of-00001 best_model_Epoch_12_step_6694_mAP_0.0748_loss_7.4896_lr_0.0001.index best_model_Epoch_12_step_6694_mAP_0.0748_loss_7.4896_lr_0.0001.meta best_model_Epoch_16_step_8754_mAP_0.0775_loss_7.6105_lr_0.0001.data-00000-of-00001 best_model_Epoch_16_step_8754_mAP_0.0775_loss_7.6105_lr_0.0001.index best_model_Epoch_16_step_8754_mAP_0.0775_loss_7.6105_lr_0.0001.meta best_model_Epoch_20_step_10814_mAP_0.0715_loss_8.2555_lr_0.0001.data-00000-of-00001 best_model_Epoch_20_step_10814_mAP_0.0715_loss_8.2555_lr_0.0001.index best_model_Epoch_20_step_10814_mAP_0.0715_loss_8.2555_lr_0.0001.meta best_model_Epoch_24_step_12874_mAP_0.0717_loss_8.5067_lr_0.0001.data-00000-of-00001 best_model_Epoch_24_step_12874_mAP_0.0717_loss_8.5067_lr_0.0001.index best_model_Epoch_24_step_12874_mAP_0.0717_loss_8.5067_lr_0.0001.meta best_model_Epoch_26_step_13715_mAP_0.7163_loss_3.3344_lr_0.0001.data-00000-of-00001 best_model_Epoch_26_step_13715_mAP_0.7163_loss_3.3344_lr_0.0001.index best_model_Epoch_26_step_13715_mAP_0.7163_loss_3.3344_lr_0.0001.meta best_model_Epoch_26_step_13904_mAP_0.0726_loss_8.5469_lr_0.0001.data-00000-of-00001 best_model_Epoch_26_step_13904_mAP_0.0726_loss_8.5469_lr_0.0001.index best_model_Epoch_26_step_13904_mAP_0.0726_loss_8.5469_lr_0.0001.meta best_model_Epoch_26_step_13904_mAP_0.0788_loss_7.9227_lr_0.0001.data-00000-of-00001 best_model_Epoch_26_step_13904_mAP_0.0788_loss_7.9227_lr_0.0001.index best_model_Epoch_26_step_13904_mAP_0.0788_loss_7.9227_lr_0.0001.meta best_model_Epoch_28_step_14731_mAP_0.7189_loss_3.2645_lr_0.0001.data-00000-of-00001 best_model_Epoch_28_step_14731_mAP_0.7189_loss_3.2645_lr_0.0001.index best_model_Epoch_28_step_14731_mAP_0.7189_loss_3.2645_lr_0.0001.meta best_model_Epoch_30_step_15747_mAP_0.7211_loss_3.3941_lr_0.0001.data-00000-of-00001 best_model_Epoch_30_step_15747_mAP_0.7211_loss_3.3941_lr_0.0001.index best_model_Epoch_30_step_15747_mAP_0.7211_loss_3.3941_lr_0.0001.meta best_model_Epoch_30_step_15747_mAP_0.7310_loss_3.4142_lr_0.0001.data-00000-of-00001 best_model_Epoch_30_step_15747_mAP_0.7310_loss_3.4142_lr_0.0001.index best_model_Epoch_30_step_15747_mAP_0.7310_loss_3.4142_lr_0.0001.meta best_model_Epoch_30_step_15964_mAP_0.0797_loss_8.0055_lr_0.0001.data-00000-of-00001 best_model_Epoch_30_step_15964_mAP_0.0797_loss_8.0055_lr_0.0001.index best_model_Epoch_30_step_15964_mAP_0.0797_loss_8.0055_lr_0.0001.meta best_model_Epoch_32_step_16763_mAP_0.7343_loss_3.3788_lr_0.0001.data-00000-of-00001 best_model_Epoch_32_step_16763_mAP_0.7343_loss_3.3788_lr_0.0001.index best_model_Epoch_32_step_16763_mAP_0.7343_loss_3.3788_lr_0.0001.meta best_model_Epoch_34_step_17779_mAP_0.7317_loss_3.3543_lr_3e-05.data-00000-of-00001 best_model_Epoch_34_step_17779_mAP_0.7317_loss_3.3543_lr_3e-05.index best_model_Epoch_34_step_17779_mAP_0.7317_loss_3.3543_lr_3e-05.meta best_model_Epoch_34_step_17779_mAP_0.7419_loss_3.2561_lr_3e-05.data-00000-of-00001 best_model_Epoch_34_step_17779_mAP_0.7419_loss_3.2561_lr_3e-05.index best_model_Epoch_34_step_17779_mAP_0.7419_loss_3.2561_lr_3e-05.meta best_model_Epoch_36_step_18795_mAP_0.7390_loss_3.3124_lr_3e-05.data-00000-of-00001 best_model_Epoch_36_step_18795_mAP_0.7390_loss_3.3124_lr_3e-05.index best_model_Epoch_36_step_18795_mAP_0.7390_loss_3.3124_lr_3e-05.meta best_model_Epoch_38_step_19811_mAP_0.7427_loss_3.3455_lr_3e-05.data-00000-of-00001 best_model_Epoch_38_step_19811_mAP_0.7427_loss_3.3455_lr_3e-05.index best_model_Epoch_38_step_19811_mAP_0.7427_loss_3.3455_lr_3e-05.meta best_model_Epoch_38_step_19811_mAP_0.7428_loss_3.3389_lr_3e-05.data-00000-of-00001 best_model_Epoch_38_step_19811_mAP_0.7428_loss_3.3389_lr_3e-05.index best_model_Epoch_38_step_19811_mAP_0.7428_loss_3.3389_lr_3e-05.meta best_model_Epoch_48_step_24891_mAP_0.7435_loss_3.4464_lr_3e-05.data-00000-of-00001 best_model_Epoch_48_step_24891_mAP_0.7435_loss_3.4464_lr_3e-05.index best_model_Epoch_48_step_24891_mAP_0.7435_loss_3.4464_lr_3e-05.meta best_model_Epoch_4_step_2574_mAP_0.0042_loss_9.6720_lr_0.0001.data-00000-of-00001 best_model_Epoch_4_step_2574_mAP_0.0042_loss_9.6720_lr_0.0001.index best_model_Epoch_4_step_2574_mAP_0.0042_loss_9.6720_lr_0.0001.meta best_model_Epoch_4_step_2574_mAP_0.0428_loss_7.8114_lr_0.0001.data-00000-of-00001 best_model_Epoch_4_step_2574_mAP_0.0428_loss_7.8114_lr_0.0001.index best_model_Epoch_4_step_2574_mAP_0.0428_loss_7.8114_lr_0.0001.meta best_model_Epoch_4_step_2574_mAP_0.0484_loss_7.5300_lr_0.0001.data-00000-of-00001 best_model_Epoch_4_step_2574_mAP_0.0484_loss_7.5300_lr_0.0001.index best_model_Epoch_4_step_2574_mAP_0.0484_loss_7.5300_lr_0.0001.meta best_model_Epoch_4_step_2574_mAP_0.0492_loss_7.8293_lr_0.0001.data-00000-of-00001 best_model_Epoch_4_step_2574_mAP_0.0492_loss_7.8293_lr_0.0001.index best_model_Epoch_4_step_2574_mAP_0.0492_loss_7.8293_lr_0.0001.meta best_model_Epoch_52_step_26923_mAP_0.7448_loss_3.5138_lr_3e-05.data-00000-of-00001 best_model_Epoch_52_step_26923_mAP_0.7448_loss_3.5138_lr_3e-05.index best_model_Epoch_52_step_26923_mAP_0.7448_loss_3.5138_lr_3e-05.meta best_model_Epoch_58_step_30384_mAP_0.0728_loss_9.0220_lr_1e-05.data-00000-of-00001 best_model_Epoch_58_step_30384_mAP_0.0728_loss_9.0220_lr_1e-05.index best_model_Epoch_58_step_30384_mAP_0.0728_loss_9.0220_lr_1e-05.meta best_model_Epoch_6_step_3604_mAP_0.0531_loss_8.0416_lr_0.0001.data-00000-of-00001 best_model_Epoch_6_step_3604_mAP_0.0531_loss_8.0416_lr_0.0001.index best_model_Epoch_6_step_3604_mAP_0.0531_loss_8.0416_lr_0.0001.meta best_model_Epoch_6_step_3604_mAP_0.0633_loss_7.4694_lr_0.0001.data-00000-of-00001 best_model_Epoch_6_step_3604_mAP_0.0633_loss_7.4694_lr_0.0001.index best_model_Epoch_6_step_3604_mAP_0.0633_loss_7.4694_lr_0.0001.meta best_model_Epoch_74_step_38624_mAP_0.0731_loss_9.1056_lr_1e-05.data-00000-of-00001 best_model_Epoch_74_step_38624_mAP_0.0731_loss_9.1056_lr_1e-05.index best_model_Epoch_74_step_38624_mAP_0.0731_loss_9.1056_lr_1e-05.meta best_model_Epoch_80_step_41147_mAP_0.7455_loss_3.5324_lr_1e-05.data-00000-of-00001 best_model_Epoch_80_step_41147_mAP_0.7455_loss_3.5324_lr_1e-05.index best_model_Epoch_80_step_41147_mAP_0.7455_loss_3.5324_lr_1e-05.meta best_model_Epoch_8_step_4634_mAP_0.0639_loss_7.5254_lr_0.0001.data-00000-of-00001 best_model_Epoch_8_step_4634_mAP_0.0639_loss_7.5254_lr_0.0001.index best_model_Epoch_8_step_4634_mAP_0.0639_loss_7.5254_lr_0.0001.meta best_model_Epoch_8_step_4634_mAP_0.0710_loss_7.8850_lr_0.0001.data-00000-of-00001 best_model_Epoch_8_step_4634_mAP_0.0710_loss_7.8850_lr_0.0001.index best_model_Epoch_8_step_4634_mAP_0.0710_loss_7.8850_lr_0.0001.meta best_model_Epoch_8_step_4634_mAP_0.0726_loss_7.0773_lr_0.0001.data-00000-of-00001 best_model_Epoch_8_step_4634_mAP_0.0726_loss_7.0773_lr_0.0001.index best_model_Epoch_8_step_4634_mAP_0.0726_loss_7.0773_lr_0.0001.meta best_model_Epoch_94_step_48259_mAP_0.7328_loss_3.5932_lr_1e-05.data-00000-of-00001 best_model_Epoch_94_step_48259_mAP_0.7328_loss_3.5932_lr_1e-05.index best_model_Epoch_94_step_48259_mAP_0.7328_loss_3.5932_lr_1e-05.meta best_model_Epoch_98_step_50291_mAP_0.7358_loss_3.6063_lr_1e-05.data-00000-of-00001 best_model_Epoch_98_step_50291_mAP_0.7358_loss_3.6063_lr_1e-05.index best_model_Epoch_98_step_50291_mAP_0.7358_loss_3.6063_lr_1e-05.meta checkpoint model-epoch_10_step_5664_loss_1.1807_lr_0.0001.data-00000-of-00001 model-epoch_10_step_5664_loss_1.1807_lr_0.0001.index model-epoch_10_step_5664_loss_1.1807_lr_0.0001.meta model-epoch_20_step_10667_loss_0.8032_lr_0.0001.data-00000-of-00001 model-epoch_20_step_10667_loss_0.8032_lr_0.0001.index model-epoch_20_step_10667_loss_0.8032_lr_0.0001.meta model-epoch_30_step_15747_loss_0.5327_lr_0.0001.data-00000-of-00001 model-epoch_30_step_15747_loss_0.5327_lr_0.0001.index model-epoch_30_step_15747_loss_0.5327_lr_0.0001.meta model-epoch_40_step_20827_loss_0.3800_lr_3e-05.data-00000-of-00001 model-epoch_40_step_20827_loss_0.3800_lr_3e-05.index model-epoch_40_step_20827_loss_0.3800_lr_3e-05.meta model-epoch_50_step_25907_loss_0.3512_lr_3e-05.data-00000-of-00001 model-epoch_50_step_25907_loss_0.3512_lr_3e-05.index model-epoch_50_step_25907_loss_0.3512_lr_3e-05.meta model-epoch_50_step_25907_loss_0.3513_lr_3e-05.data-00000-of-00001 model-epoch_50_step_25907_loss_0.3513_lr_3e-05.index model-epoch_50_step_25907_loss_0.3513_lr_3e-05.meta model-epoch_50_step_25907_loss_0.3590_lr_3e-05.data-00000-of-00001 model-epoch_50_step_25907_loss_0.3590_lr_3e-05.index model-epoch_50_step_25907_loss_0.3590_lr_3e-05.meta model-epoch_50_step_26264_loss_0.3299_lr_3e-05.data-00000-of-00001 model-epoch_50_step_26264_loss_0.3299_lr_3e-05.index model-epoch_50_step_26264_loss_0.3299_lr_3e-05.meta model-epoch_50_step_26264_loss_0.3480_lr_3e-05.data-00000-of-00001 model-epoch_50_step_26264_loss_0.3480_lr_3e-05.index model-epoch_50_step_26264_loss_0.3480_lr_3e-05.meta model-epoch_60_step_30987_loss_0.3373_lr_1e-05.data-00000-of-00001 model-epoch_60_step_30987_loss_0.3373_lr_1e-05.index model-epoch_60_step_30987_loss_0.3373_lr_1e-05.meta model-epoch_60_step_30987_loss_0.3422_lr_1e-05.data-00000-of-00001 model-epoch_60_step_30987_loss_0.3422_lr_1e-05.index model-epoch_60_step_30987_loss_0.3422_lr_1e-05.meta model-epoch_60_step_30987_loss_0.3430_lr_1e-05.data-00000-of-00001 model-epoch_60_step_30987_loss_0.3430_lr_1e-05.index model-epoch_60_step_30987_loss_0.3430_lr_1e-05.meta model-epoch_60_step_31414_loss_0.3199_lr_1e-05.data-00000-of-00001 model-epoch_60_step_31414_loss_0.3199_lr_1e-05.index model-epoch_60_step_31414_loss_0.3199_lr_1e-05.meta model-epoch_60_step_31414_loss_0.3371_lr_1e-05.data-00000-of-00001 model-epoch_60_step_31414_loss_0.3371_lr_1e-05.index model-epoch_60_step_31414_loss_0.3371_lr_1e-05.meta model-epoch_70_step_36067_loss_0.3288_lr_1e-05.data-00000-of-00001 model-epoch_70_step_36067_loss_0.3288_lr_1e-05.index model-epoch_70_step_36067_loss_0.3288_lr_1e-05.meta model-epoch_70_step_36067_loss_0.3350_lr_1e-05.data-00000-of-00001 model-epoch_70_step_36067_loss_0.3350_lr_1e-05.index model-epoch_70_step_36067_loss_0.3350_lr_1e-05.meta model-epoch_70_step_36564_loss_0.3164_lr_1e-05.data-00000-of-00001 model-epoch_70_step_36564_loss_0.3164_lr_1e-05.index model-epoch_70_step_36564_loss_0.3164_lr_1e-05.meta model-epoch_70_step_36564_loss_0.3291_lr_1e-05.data-00000-of-00001 model-epoch_70_step_36564_loss_0.3291_lr_1e-05.index model-epoch_70_step_36564_loss_0.3291_lr_1e-05.meta model-epoch_80_step_41147_loss_0.3309_lr_1e-05.data-00000-of-00001 model-epoch_80_step_41147_loss_0.3309_lr_1e-05.index model-epoch_80_step_41147_loss_0.3309_lr_1e-05.meta model-epoch_80_step_41147_loss_0.3321_lr_1e-05.data-00000-of-00001 model-epoch_80_step_41147_loss_0.3321_lr_1e-05.index model-epoch_80_step_41147_loss_0.3321_lr_1e-05.meta model-epoch_80_step_41714_loss_0.3133_lr_1e-05.data-00000-of-00001 model-epoch_80_step_41714_loss_0.3133_lr_1e-05.index model-epoch_80_step_41714_loss_0.3133_lr_1e-05.meta model-epoch_80_step_41714_loss_0.3244_lr_1e-05.data-00000-of-00001 model-epoch_80_step_41714_loss_0.3244_lr_1e-05.index model-epoch_80_step_41714_loss_0.3244_lr_1e-05.meta model-epoch_90_step_46227_loss_0.3235_lr_1e-05.data-00000-of-00001 model-epoch_90_step_46227_loss_0.3235_lr_1e-05.index model-epoch_90_step_46227_loss_0.3235_lr_1e-05.meta model-epoch_90_step_46227_loss_0.3270_lr_1e-05.data-00000-of-00001 model-epoch_90_step_46227_loss_0.3270_lr_1e-05.index model-epoch_90_step_46227_loss_0.3270_lr_1e-05.meta model-epoch_90_step_46864_loss_0.3098_lr_1e-05.data-00000-of-00001 model-epoch_90_step_46864_loss_0.3098_lr_1e-05.index model-epoch_90_step_46864_loss_0.3098_lr_1e-05.meta model-epoch_90_step_46864_loss_0.3239_lr_1e-05.data-00000-of-00001 model-epoch_90_step_46864_loss_0.3239_lr_1e-05.index model-epoch_90_step_46864_loss_0.3239_lr_1e-05.meta sh-4.2$ 如下为 yolov3 train.py: # coding: utf-8 from __future__ import division, print_function import tensorflow as tf import numpy as np import logging from tqdm import trange import args from utils.data_utils import get_batch_data from utils.misc_utils import shuffle_and_overwrite, make_summary, config_learning_rate, config_optimizer, AverageMeter from utils.eval_utils import evaluate_on_cpu, evaluate_on_gpu, get_preds_gpu, voc_eval, parse_gt_rec from utils.nms_utils import gpu_nms from model import yolov3 # setting loggers logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(message)s', datefmt='%a, %d %b %Y %H:%M:%S', filename=args.progress_log_path, filemode='w') # setting placeholders is_training = tf.placeholder(tf.bool, name="phase_train") handle_flag = tf.placeholder(tf.string, [], name='iterator_handle_flag') # register the gpu nms operation here for the following evaluation scheme pred_boxes_flag = tf.placeholder(tf.float32, [1, None, None]) pred_scores_flag = tf.placeholder(tf.float32, [1, None, None]) gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, args.class_num, args.nms_topk, args.score_threshold, args.nms_threshold) ################## # tf.data pipeline ################## train_dataset = tf.data.TextLineDataset(args.train_file) train_dataset = train_dataset.shuffle(args.train_img_cnt) train_dataset = train_dataset.batch(args.batch_size) train_dataset = train_dataset.map( lambda x: tf.py_func(get_batch_data, inp=[x, args.class_num, args.img_size, args.anchors, 'train', args.multi_scale_train, args.use_mix_up, args.letterbox_resize], Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]), num_parallel_calls=args.num_threads ) train_dataset = train_dataset.prefetch(args.prefetech_buffer) val_dataset = tf.data.TextLineDataset(args.val_file) val_dataset = val_dataset.batch(1) val_dataset = val_dataset.map( lambda x: tf.py_func(get_batch_data, inp=[x, args.class_num, args.img_size, args.anchors, 'val', False, False, args.letterbox_resize], Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]), num_parallel_calls=args.num_threads ) val_dataset.prefetch(args.prefetech_buffer) iterator = tf.data.Iterator.from_structure(train_dataset.output_types, train_dataset.output_shapes) train_init_op = iterator.make_initializer(train_dataset) val_init_op = iterator.make_initializer(val_dataset) # get an element from the chosen dataset iterator image_ids, image, y_true_13, y_true_26, y_true_52 = iterator.get_next() y_true = [y_true_13, y_true_26, y_true_52] # tf.data pipeline will lose the data `static` shape, so we need to set it manually image_ids.set_shape([None]) image.set_shape([None, None, None, 3]) for y in y_true: y.set_shape([None, None, None, None, None]) ################## # Model definition ################## yolo_model = yolov3(args.class_num, args.anchors, args.use_label_smooth, args.use_focal_loss, args.batch_norm_decay, args.weight_decay, use_static_shape=False) with tf.variable_scope('yolov3'): pred_feature_maps = yolo_model.forward(image, is_training=is_training) loss = yolo_model.compute_loss(pred_feature_maps, y_true) y_pred = yolo_model.predict(pred_feature_maps) l2_loss = tf.losses.get_regularization_loss() # setting restore parts and vars to update saver_to_restore = tf.train.Saver(var_list=tf.contrib.framework.get_variables_to_restore(include=args.restore_include, exclude=args.restore_exclude)) update_vars = tf.contrib.framework.get_variables_to_restore(include=args.update_part) tf.summary.scalar('train_batch_statistics/total_loss', loss[0]) tf.summary.scalar('train_batch_statistics/loss_xy', loss[1]) tf.summary.scalar('train_batch_statistics/loss_wh', loss[2]) tf.summary.scalar('train_batch_statistics/loss_conf', loss[3]) tf.summary.scalar('train_batch_statistics/loss_class', loss[4]) tf.summary.scalar('train_batch_statistics/loss_l2', l2_loss) tf.summary.scalar('train_batch_statistics/loss_ratio', l2_loss / loss[0]) global_step = tf.Variable(float(args.global_step), trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES]) if args.use_warm_up: learning_rate = tf.cond(tf.less(global_step, args.train_batch_num * args.warm_up_epoch), lambda: args.learning_rate_init * global_step / (args.train_batch_num * args.warm_up_epoch), lambda: config_learning_rate(args, global_step - args.train_batch_num * args.warm_up_epoch)) else: learning_rate = config_learning_rate(args, global_step) tf.summary.scalar('learning_rate', learning_rate) if not args.save_optimizer: saver_to_save = tf.train.Saver() saver_best = tf.train.Saver() optimizer = config_optimizer(args.optimizer_name, learning_rate) # set dependencies for BN ops update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): # train_op = optimizer.minimize(loss[0] + l2_loss, var_list=update_vars, global_step=global_step) # apply gradient clip to avoid gradient exploding gvs = optimizer.compute_gradients(loss[0] + l2_loss, var_list=update_vars) clip_grad_var = [gv if gv[0] is None else [ tf.clip_by_norm(gv[0], 100.), gv[1]] for gv in gvs] train_op = optimizer.apply_gradients(clip_grad_var, global_step=global_step) if args.save_optimizer: print('Saving optimizer parameters to checkpoint! Remember to restore the global_step in the fine-tuning afterwards.') saver_to_save = tf.train.Saver() saver_best = tf.train.Saver() with tf.Session() as sess: sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()]) saver_to_restore.restore(sess, args.restore_path) merged = tf.summary.merge_all() writer = tf.summary.FileWriter(args.log_dir, sess.graph) print('\n----------- start to train -----------\n') best_mAP = -np.Inf for epoch in range(args.total_epoches): sess.run(train_init_op) loss_total, loss_xy, loss_wh, loss_conf, loss_class = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter() for i in trange(args.train_batch_num): _, summary, __y_pred, __y_true, __loss, __global_step, __lr = sess.run( [train_op, merged, y_pred, y_true, loss, global_step, learning_rate], feed_dict={is_training: True}) writer.add_summary(summary, global_step=__global_step) loss_total.update(__loss[0], len(__y_pred[0])) loss_xy.update(__loss[1], len(__y_pred[0])) loss_wh.update(__loss[2], len(__y_pred[0])) loss_conf.update(__loss[3], len(__y_pred[0])) loss_class.update(__loss[4], len(__y_pred[0])) if __global_step % args.train_evaluation_step == 0 and __global_step > 0: # recall, precision = evaluate_on_cpu(__y_pred, __y_true, args.class_num, args.nms_topk, args.score_threshold, args.nms_threshold) recall, precision = evaluate_on_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __y_pred, __y_true, args.class_num, args.nms_threshold) info = "Epoch: {}, global_step: {} | loss: total: {:.2f}, xy: {:.2f}, wh: {:.2f}, conf: {:.2f}, class: {:.2f} | ".format( epoch, int(__global_step), loss_total.average, loss_xy.average, loss_wh.average, loss_conf.average, loss_class.average) info += 'Last batch: rec: {:.3f}, prec: {:.3f} | lr: {:.5g}'.format(recall, precision, __lr) print(info) logging.info(info) writer.add_summary(make_summary('evaluation/train_batch_recall', recall), global_step=__global_step) writer.add_summary(make_summary('evaluation/train_batch_precision', precision), global_step=__global_step) if np.isnan(loss_total.average): print('****' * 10) raise ArithmeticError( 'Gradient exploded! Please train again and you may need modify some parameters.') # NOTE: this is just demo. You can set the conditions when to save the weights. if epoch % args.save_epoch == 0 and epoch > 0: if loss_total.average <= 2.: saver_to_save.save(sess, args.save_dir + 'model-epoch_{}_step_{}_loss_{:.4f}_lr_{:.5g}'.format(epoch, int(__global_step), loss_total.average, __lr)) # switch to validation dataset for evaluation if epoch % args.val_evaluation_epoch == 0 and epoch >= args.warm_up_epoch: sess.run(val_init_op) val_loss_total, val_loss_xy, val_loss_wh, val_loss_conf, val_loss_class = \ AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter() val_preds = [] for j in trange(args.val_img_cnt): __image_ids, __y_pred, __loss = sess.run([image_ids, y_pred, loss], feed_dict={is_training: False}) pred_content = get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __image_ids, __y_pred) val_preds.extend(pred_content) val_loss_total.update(__loss[0]) val_loss_xy.update(__loss[1]) val_loss_wh.update(__loss[2]) val_loss_conf.update(__loss[3]) val_loss_class.update(__loss[4]) # calc mAP rec_total, prec_total, ap_total = AverageMeter(), AverageMeter(), AverageMeter() gt_dict = parse_gt_rec(args.val_file, args.img_size, args.letterbox_resize) info = '======> Epoch: {}, global_step: {}, lr: {:.6g} <======\n'.format(epoch, __global_step, __lr) for ii in range(args.class_num): npos, nd, rec, prec, ap = voc_eval(gt_dict, val_preds, ii, iou_thres=args.eval_threshold, use_07_metric=args.use_voc_07_metric) info += 'EVAL: Class {}: Recall: {:.4f}, Precision: {:.4f}, AP: {:.4f}\n'.format(ii, rec, prec, ap) rec_total.update(rec, npos) prec_total.update(prec, nd) ap_total.update(ap, 1) mAP = ap_total.average info += 'EVAL: Recall: {:.4f}, Precison: {:.4f}, mAP: {:.4f}\n'.format(rec_total.average, prec_total.average, mAP) info += 'EVAL: loss: total: {:.2f}, xy: {:.2f}, wh: {:.2f}, conf: {:.2f}, class: {:.2f}\n'.format( val_loss_total.average, val_loss_xy.average, val_loss_wh.average, val_loss_conf.average, val_loss_class.average) print(info) logging.info(info) if mAP > best_mAP: best_mAP = mAP saver_best.save(sess, args.save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format( epoch, int(__global_step), best_mAP, val_loss_total.average, __lr)) writer.add_summary(make_summary('evaluation/val_mAP', mAP), global_step=epoch) writer.add_summary(make_summary('evaluation/val_recall', rec_total.average), global_step=epoch) writer.add_summary(make_summary('evaluation/val_precision', prec_total.average), global_step=epoch) writer.add_summary(make_summary('validation_statistics/total_loss', val_loss_total.average), global_step=epoch) writer.add_summary(make_summary('validation_statistics/loss_xy', val_loss_xy.average), global_step=epoch) writer.add_summary(make_summary('validation_statistics/loss_wh', val_loss_wh.average), global_step=epoch) writer.add_summary(make_summary('validation_statistics/loss_conf', val_loss_conf.average), global_step=epoch) writer.add_summary(make_summary('validation_statistics/loss_class', val_loss_class.average), global_step=epoch)
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