keras_retinanet 目標檢測——自定義圖片數據集的模型訓練步驟

最近在學習 keras_retinanet ，下面就記錄下用自己的數據集進行的模型訓練。

大致分爲以下幾步：

自定義訓練數據
圖片目標標註
生成用於訓練的圖片名稱、目標標註位置及目標類別的.csv文件
開始訓練模型（注意參數調整）
轉換訓練好的模型
用轉換後的模型進行目標檢測

下面就一步一步介紹吧：

由於我之前已經安裝了vs2015、Anaconda和Pycharm，就不在此贅述了。

本機配置：

GTX 1060 3G
Win10 64
vs2015
Anaconda3 5.1.0
Pycharm

1.下載包，安裝環境。

從Github上下載Github ——> keras-retinanet這個倉庫
確保你的環境裏有tensorflow、numpy、keras
切換到當前目錄下運行
```
pip install . --user
```
或者直接從克隆的倉庫中運行代碼，但是需要運行python setup.py build_ext --inplace來首先編譯Cython代碼。
如果還不行就把keras_retinanet這整個目錄拷貝到你自己環境的D:\Anaconda3-5.0.1\envs\tf-gpu\Lib\site-packages下

測試準備：點擊測試模型下載，將用於測試的模型resnet50_coco_best_v2.1.0.h5放在snapshots目錄下

下面就可以在jupyter notebook運行examples裏的ResNet50RetinaNet.ipynb進行測試，當然也可以在jupyter notebook中將文件保存成.py格式的在pycharm裏運行。

2.準備數據集

retinanet模型訓練的數據是按照VOC2007格式處理的，所以你也需要將自己的數據準備成VOC2007的格式

準備如圖三個文件夾，JPEGImages放你自己準備訓練模型的圖片，圖片名稱最好是按1.jpg,2.jpg這種類型；
Annotations放圖片目標的位置和類型標註的.xml文件，這個可以用Github——>labellmg裏的生成目標標註的工具自動生成.xml的文件（注意使用時將保存路徑改到Annotations文件夾）；

ImageSets裏的子文件夾Main裏放按比例隨機抽樣切分的訓練集、驗證集、測試集樣本下標值的txt文件，這個可以用如下gen_main_txt.py自動生成

import os
import random

trainval_percent = 0.8  # 自定義用於訓練模型的數據（訓練數據和交叉驗證數據之和）佔全部數據的比例
train_percent = 0.8  # 自定義訓練數據佔訓練數據交叉驗證數據之和的比例
xmlfilepath = 'Annotations'
txtsavepath = 'ImageSets\Main'
total_xml = os.listdir(xmlfilepath)

num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)

ftrainval = open('ImageSets/Main/trainval.txt', 'w')
ftest = open('ImageSets/Main/test.txt', 'w')
ftrain = open('ImageSets/Main/train.txt', 'w')
fval = open('ImageSets/Main/val.txt', 'w')

for i in list:
    name = total_xml[i][:-4]+'\n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftrain.write(name)
        else:
            fval.write(name)
    else:
        ftest.write(name)

ftrainval.close()
ftrain.close()
fval.close()
ftest .close()

生成包含文件名、目標位置、目標類型的annotations.csv文件和類別標籤的classes.csv文件，這個可以用gen_csv.py自動生成

import csv
import os
import glob
import sys

class PascalVOC2CSV(object):
    def __init__(self, xml=[], ann_path='./annotations.csv', classes_path='./classes.csv'):
        '''
        :param xml: 所有Pascal VOC的xml文件路徑組成的列表
        :param ann_path: ann_path
        :param classes_path: classes_path
        '''
        self.xml = xml
        self.ann_path = ann_path
        self.classes_path = classes_path
        self.label = []
        self.annotations = []

        self.data_transfer()
        self.write_file()

    def data_transfer(self):
        for num, xml_file in enumerate(self.xml):
            try:
                # print(xml_file)
                # 進度輸出
                sys.stdout.write('\r>> Converting image %d/%d' % (
                    num + 1, len(self.xml)))
                sys.stdout.flush()

                with open(xml_file, 'r') as fp:
                    for p in fp:
                        if '<filename>' in p:
                            self.filen_ame = p.split('>')[1].split('<')[0]

                        if '<object>' in p:
                            # 類別
                            d = [next(fp).split('>')[1].split('<')[0] for _ in range(9)]
                            self.supercategory = d[0]
                            if self.supercategory not in self.label:
                                self.label.append(self.supercategory)

                            # 邊界框
                            x1 = int(d[-4]);
                            y1 = int(d[-3]);
                            x2 = int(d[-2]);
                            y2 = int(d[-1])

                            self.annotations.append(
                                [os.path.join('JPEGImages', self.filen_ame), x1, y1, x2, y2, self.supercategory])
            except:
                continue

        sys.stdout.write('\n')
        sys.stdout.flush()

    def write_file(self, ):
        with open(self.ann_path, 'w', newline='') as fp:
            csv_writer = csv.writer(fp, dialect='excel')
            csv_writer.writerows(self.annotations)

        class_name = sorted(self.label)
        class_ = []
        for num, name in enumerate(class_name):
            class_.append([name, num])
        with open(self.classes_path, 'w', newline='') as fp:
            csv_writer = csv.writer(fp, dialect='excel')
            csv_writer.writerows(class_)

if __name__ == "__main__":
    xml_file = glob.glob('./Annotations/*.xml')
    PascalVOC2CSV(xml_file)

用keras-retinanet-master\keras_retinanet\bin目錄下的debug.py測試數據集是否生成成功，這個程序需要在命令行執行

python D:/PyCharm/PycharmProjects/tf-gpu-env/project/keras-retinanet-master/keras_retinanet/bin/debug.py csv  D:/PyCharm/PycharmProjects/tf-gpu-env/project/keras-retinanet-master/examples/annotations.csv  D:/PyCharm/PycharmProjects/tf-gpu-env/project/keras-retinanet-master/examples/classes.csv

彈出你標註的圖片說明數據準備成功。

3.訓練模型

用keras-retinanet-master\keras_retinanet\bin目錄下的train.py來訓練模型，需要修改文件裏import的相對路徑

from keras_retinanet import layers  # noqa: F401
from keras_retinanet import losses
from keras_retinanet import models
from keras_retinanet.callbacks import RedirectModel
from keras_retinanet.callbacks.eval import Evaluate
from keras_retinanet.models.retinanet import retinanet_bbox
from keras_retinanet.preprocessing.csv_generator import CSVGenerator
from keras_retinanet.preprocessing.kitti import KittiGenerator
from keras_retinanet.preprocessing.open_images import OpenImagesGenerator
from keras_retinanet.preprocessing.pascal_voc import PascalVocGenerator
from keras_retinanet.utils.anchors import make_shapes_callback
from keras_retinanet.utils.config import read_config_file, parse_anchor_parameters
from keras_retinanet.utils.keras_version import check_keras_version
from keras_retinanet.utils.model import freeze as freeze_model
from keras_retinanet.utils.transform import random_transform_generator

根據你自己GPU的性能微調參數，如果報tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[1,256,100,100]錯誤說明你的GPU內存不夠用，可以通過降低batch-size、將image-min-side,image-max-side改小、改小網絡結構等方式解決

tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[1,256,100,100] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
         [[{{node training/Adam/gradients/classification_submodel/pyramid_classification_3/convolution_grad/Conv2DBackpropInput}} = Conv2DBackpropInput[T=DT_FLOAT, _class=["loc:@training/Adam/cond_85/Switch_2"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](training/Adam/gradients/classification_submodel/pyramid_classification_3/convolution_grad/ShapeN, pyramid_classification_3/kernel/read, training/Adam/gradients/classification_submodel/pyramid_classification_3/Relu_grad/ReluGrad)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

參數調整：

修改workers參數（不知道爲什麼多線程會出錯，就暫且不開啓吧）

在命令行運行train.py文件

python D:/PyCharm/PycharmProjects/tf-gpu-env/project/keras-retinanet-master/keras_retinanet/bin/train.py csv  D:/PyCharm/PycharmProjects/tf-gpu-env/project/keras-retinanet-master/examples/annotations.csv  D:/PyCharm/PycharmProjects/tf-gpu-env/project/keras-retinanet-master/examples/classes.csv

創建模型，網絡結構如下

Creating model, this may take a second...
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
input_1 (InputLayer)            (None, None, None, 3 0
__________________________________________________________________________________________________
padding_conv1 (ZeroPadding2D)   (None, None, None, 3 0           input_1[0][0]
__________________________________________________________________________________________________
conv1 (Conv2D)                  (None, None, None, 6 9408        padding_conv1[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization)   (None, None, None, 6 256         conv1[0][0]
__________________________________________________________________________________________________
conv1_relu (Activation)         (None, None, None, 6 0           bn_conv1[0][0]
__________________________________________________________________________________________________
pool1 (MaxPooling2D)            (None, None, None, 6 0           conv1_relu[0][0]
__________________________________________________________________________________________________
res2a_branch2a (Conv2D)         (None, None, None, 6 4096        pool1[0][0]
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, None, None, 6 256         res2a_branch2a[0][0]
__________________________________________________________________________________________________
res2a_branch2a_relu (Activation (None, None, None, 6 0           bn2a_branch2a[0][0]
__________________________________________________________________________________________________
padding2a_branch2b (ZeroPadding (None, None, None, 6 0           res2a_branch2a_relu[0][0]
__________________________________________________________________________________________________
res2a_branch2b (Conv2D)         (None, None, None, 6 36864       padding2a_branch2b[0][0]
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, None, None, 6 256         res2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2b_relu (Activation (None, None, None, 6 0           bn2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2c (Conv2D)         (None, None, None, 2 16384       res2a_branch2b_relu[0][0]
__________________________________________________________________________________________________
res2a_branch1 (Conv2D)          (None, None, None, 2 16384       pool1[0][0]
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, None, None, 2 1024        res2a_branch2c[0][0]
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, None, None, 2 1024        res2a_branch1[0][0]
__________________________________________________________________________________________________
res2a (Add)                     (None, None, None, 2 0           bn2a_branch2c[0][0]
                                                                 bn2a_branch1[0][0]
__________________________________________________________________________________________________
res2a_relu (Activation)         (None, None, None, 2 0           res2a[0][0]
__________________________________________________________________________________________________
res2b_branch2a (Conv2D)         (None, None, None, 6 16384       res2a_relu[0][0]
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, None, None, 6 256         res2b_branch2a[0][0]
__________________________________________________________________________________________________
res2b_branch2a_relu (Activation (None, None, None, 6 0           bn2b_branch2a[0][0]
__________________________________________________________________________________________________
padding2b_branch2b (ZeroPadding (None, None, None, 6 0           res2b_branch2a_relu[0][0]
__________________________________________________________________________________________________
res2b_branch2b (Conv2D)         (None, None, None, 6 36864       padding2b_branch2b[0][0]
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, None, None, 6 256         res2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2b_relu (Activation (None, None, None, 6 0           bn2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2c (Conv2D)         (None, None, None, 2 16384       res2b_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, None, None, 2 1024        res2b_branch2c[0][0]
__________________________________________________________________________________________________
res2b (Add)                     (None, None, None, 2 0           bn2b_branch2c[0][0]
                                                                 res2a_relu[0][0]
__________________________________________________________________________________________________
res2b_relu (Activation)         (None, None, None, 2 0           res2b[0][0]
__________________________________________________________________________________________________
res2c_branch2a (Conv2D)         (None, None, None, 6 16384       res2b_relu[0][0]
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, None, None, 6 256         res2c_branch2a[0][0]
__________________________________________________________________________________________________
res2c_branch2a_relu (Activation (None, None, None, 6 0           bn2c_branch2a[0][0]
__________________________________________________________________________________________________
padding2c_branch2b (ZeroPadding (None, None, None, 6 0           res2c_branch2a_relu[0][0]
__________________________________________________________________________________________________
res2c_branch2b (Conv2D)         (None, None, None, 6 36864       padding2c_branch2b[0][0]
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, None, None, 6 256         res2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2b_relu (Activation (None, None, None, 6 0           bn2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2c (Conv2D)         (None, None, None, 2 16384       res2c_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, None, None, 2 1024        res2c_branch2c[0][0]
__________________________________________________________________________________________________
res2c (Add)                     (None, None, None, 2 0           bn2c_branch2c[0][0]
                                                                 res2b_relu[0][0]
__________________________________________________________________________________________________
res2c_relu (Activation)         (None, None, None, 2 0           res2c[0][0]
__________________________________________________________________________________________________
res3a_branch2a (Conv2D)         (None, None, None, 1 32768       res2c_relu[0][0]
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, None, None, 1 512         res3a_branch2a[0][0]
__________________________________________________________________________________________________
res3a_branch2a_relu (Activation (None, None, None, 1 0           bn3a_branch2a[0][0]
__________________________________________________________________________________________________
padding3a_branch2b (ZeroPadding (None, None, None, 1 0           res3a_branch2a_relu[0][0]
__________________________________________________________________________________________________
res3a_branch2b (Conv2D)         (None, None, None, 1 147456      padding3a_branch2b[0][0]
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, None, None, 1 512         res3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2b_relu (Activation (None, None, None, 1 0           bn3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2c (Conv2D)         (None, None, None, 5 65536       res3a_branch2b_relu[0][0]
__________________________________________________________________________________________________
res3a_branch1 (Conv2D)          (None, None, None, 5 131072      res2c_relu[0][0]
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, None, None, 5 2048        res3a_branch2c[0][0]
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, None, None, 5 2048        res3a_branch1[0][0]
__________________________________________________________________________________________________
res3a (Add)                     (None, None, None, 5 0           bn3a_branch2c[0][0]
                                                                 bn3a_branch1[0][0]
__________________________________________________________________________________________________
res3a_relu (Activation)         (None, None, None, 5 0           res3a[0][0]
__________________________________________________________________________________________________
res3b_branch2a (Conv2D)         (None, None, None, 1 65536       res3a_relu[0][0]
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, None, None, 1 512         res3b_branch2a[0][0]
__________________________________________________________________________________________________
res3b_branch2a_relu (Activation (None, None, None, 1 0           bn3b_branch2a[0][0]
__________________________________________________________________________________________________
padding3b_branch2b (ZeroPadding (None, None, None, 1 0           res3b_branch2a_relu[0][0]
__________________________________________________________________________________________________
res3b_branch2b (Conv2D)         (None, None, None, 1 147456      padding3b_branch2b[0][0]
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, None, None, 1 512         res3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2b_relu (Activation (None, None, None, 1 0           bn3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2c (Conv2D)         (None, None, None, 5 65536       res3b_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, None, None, 5 2048        res3b_branch2c[0][0]
__________________________________________________________________________________________________
res3b (Add)                     (None, None, None, 5 0           bn3b_branch2c[0][0]
                                                                 res3a_relu[0][0]
__________________________________________________________________________________________________
res3b_relu (Activation)         (None, None, None, 5 0           res3b[0][0]
__________________________________________________________________________________________________
res3c_branch2a (Conv2D)         (None, None, None, 1 65536       res3b_relu[0][0]
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, None, None, 1 512         res3c_branch2a[0][0]
__________________________________________________________________________________________________
res3c_branch2a_relu (Activation (None, None, None, 1 0           bn3c_branch2a[0][0]
__________________________________________________________________________________________________
padding3c_branch2b (ZeroPadding (None, None, None, 1 0           res3c_branch2a_relu[0][0]
__________________________________________________________________________________________________
res3c_branch2b (Conv2D)         (None, None, None, 1 147456      padding3c_branch2b[0][0]
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, None, None, 1 512         res3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2b_relu (Activation (None, None, None, 1 0           bn3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2c (Conv2D)         (None, None, None, 5 65536       res3c_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, None, None, 5 2048        res3c_branch2c[0][0]
__________________________________________________________________________________________________
res3c (Add)                     (None, None, None, 5 0           bn3c_branch2c[0][0]
                                                                 res3b_relu[0][0]
__________________________________________________________________________________________________
res3c_relu (Activation)         (None, None, None, 5 0           res3c[0][0]
__________________________________________________________________________________________________
res3d_branch2a (Conv2D)         (None, None, None, 1 65536       res3c_relu[0][0]
__________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizati (None, None, None, 1 512         res3d_branch2a[0][0]
__________________________________________________________________________________________________
res3d_branch2a_relu (Activation (None, None, None, 1 0           bn3d_branch2a[0][0]
__________________________________________________________________________________________________
padding3d_branch2b (ZeroPadding (None, None, None, 1 0           res3d_branch2a_relu[0][0]
__________________________________________________________________________________________________
res3d_branch2b (Conv2D)         (None, None, None, 1 147456      padding3d_branch2b[0][0]
__________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizati (None, None, None, 1 512         res3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2b_relu (Activation (None, None, None, 1 0           bn3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2c (Conv2D)         (None, None, None, 5 65536       res3d_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizati (None, None, None, 5 2048        res3d_branch2c[0][0]
__________________________________________________________________________________________________
res3d (Add)                     (None, None, None, 5 0           bn3d_branch2c[0][0]
                                                                 res3c_relu[0][0]
__________________________________________________________________________________________________
res3d_relu (Activation)         (None, None, None, 5 0           res3d[0][0]
__________________________________________________________________________________________________
res4a_branch2a (Conv2D)         (None, None, None, 2 131072      res3d_relu[0][0]
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, None, None, 2 1024        res4a_branch2a[0][0]
__________________________________________________________________________________________________
res4a_branch2a_relu (Activation (None, None, None, 2 0           bn4a_branch2a[0][0]
__________________________________________________________________________________________________
padding4a_branch2b (ZeroPadding (None, None, None, 2 0           res4a_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4a_branch2b (Conv2D)         (None, None, None, 2 589824      padding4a_branch2b[0][0]
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, None, None, 2 1024        res4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2b_relu (Activation (None, None, None, 2 0           bn4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2c (Conv2D)         (None, None, None, 1 262144      res4a_branch2b_relu[0][0]
__________________________________________________________________________________________________
res4a_branch1 (Conv2D)          (None, None, None, 1 524288      res3d_relu[0][0]
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, None, None, 1 4096        res4a_branch2c[0][0]
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, None, None, 1 4096        res4a_branch1[0][0]
__________________________________________________________________________________________________
res4a (Add)                     (None, None, None, 1 0           bn4a_branch2c[0][0]
                                                                 bn4a_branch1[0][0]
__________________________________________________________________________________________________
res4a_relu (Activation)         (None, None, None, 1 0           res4a[0][0]
__________________________________________________________________________________________________
res4b_branch2a (Conv2D)         (None, None, None, 2 262144      res4a_relu[0][0]
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, None, None, 2 1024        res4b_branch2a[0][0]
__________________________________________________________________________________________________
res4b_branch2a_relu (Activation (None, None, None, 2 0           bn4b_branch2a[0][0]
__________________________________________________________________________________________________
padding4b_branch2b (ZeroPadding (None, None, None, 2 0           res4b_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4b_branch2b (Conv2D)         (None, None, None, 2 589824      padding4b_branch2b[0][0]
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, None, None, 2 1024        res4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2b_relu (Activation (None, None, None, 2 0           bn4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2c (Conv2D)         (None, None, None, 1 262144      res4b_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, None, None, 1 4096        res4b_branch2c[0][0]
__________________________________________________________________________________________________
res4b (Add)                     (None, None, None, 1 0           bn4b_branch2c[0][0]
                                                                 res4a_relu[0][0]
__________________________________________________________________________________________________
res4b_relu (Activation)         (None, None, None, 1 0           res4b[0][0]
__________________________________________________________________________________________________
res4c_branch2a (Conv2D)         (None, None, None, 2 262144      res4b_relu[0][0]
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, None, None, 2 1024        res4c_branch2a[0][0]
__________________________________________________________________________________________________
res4c_branch2a_relu (Activation (None, None, None, 2 0           bn4c_branch2a[0][0]
__________________________________________________________________________________________________
padding4c_branch2b (ZeroPadding (None, None, None, 2 0           res4c_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4c_branch2b (Conv2D)         (None, None, None, 2 589824      padding4c_branch2b[0][0]
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, None, None, 2 1024        res4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2b_relu (Activation (None, None, None, 2 0           bn4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2c (Conv2D)         (None, None, None, 1 262144      res4c_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, None, None, 1 4096        res4c_branch2c[0][0]
__________________________________________________________________________________________________
res4c (Add)                     (None, None, None, 1 0           bn4c_branch2c[0][0]
                                                                 res4b_relu[0][0]
__________________________________________________________________________________________________
res4c_relu (Activation)         (None, None, None, 1 0           res4c[0][0]
__________________________________________________________________________________________________
res4d_branch2a (Conv2D)         (None, None, None, 2 262144      res4c_relu[0][0]
__________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizati (None, None, None, 2 1024        res4d_branch2a[0][0]
__________________________________________________________________________________________________
res4d_branch2a_relu (Activation (None, None, None, 2 0           bn4d_branch2a[0][0]
__________________________________________________________________________________________________
padding4d_branch2b (ZeroPadding (None, None, None, 2 0           res4d_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4d_branch2b (Conv2D)         (None, None, None, 2 589824      padding4d_branch2b[0][0]
__________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizati (None, None, None, 2 1024        res4d_branch2b[0][0]
__________________________________________________________________________________________________
res4d_branch2b_relu (Activation (None, None, None, 2 0           bn4d_branch2b[0][0]
__________________________________________________________________________________________________
res4d_branch2c (Conv2D)         (None, None, None, 1 262144      res4d_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizati (None, None, None, 1 4096        res4d_branch2c[0][0]
__________________________________________________________________________________________________
res4d (Add)                     (None, None, None, 1 0           bn4d_branch2c[0][0]
                                                                 res4c_relu[0][0]
__________________________________________________________________________________________________
res4d_relu (Activation)         (None, None, None, 1 0           res4d[0][0]
__________________________________________________________________________________________________
res4e_branch2a (Conv2D)         (None, None, None, 2 262144      res4d_relu[0][0]
__________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizati (None, None, None, 2 1024        res4e_branch2a[0][0]
__________________________________________________________________________________________________
res4e_branch2a_relu (Activation (None, None, None, 2 0           bn4e_branch2a[0][0]
__________________________________________________________________________________________________
padding4e_branch2b (ZeroPadding (None, None, None, 2 0           res4e_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4e_branch2b (Conv2D)         (None, None, None, 2 589824      padding4e_branch2b[0][0]
__________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizati (None, None, None, 2 1024        res4e_branch2b[0][0]
__________________________________________________________________________________________________
res4e_branch2b_relu (Activation (None, None, None, 2 0           bn4e_branch2b[0][0]
__________________________________________________________________________________________________
res4e_branch2c (Conv2D)         (None, None, None, 1 262144      res4e_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizati (None, None, None, 1 4096        res4e_branch2c[0][0]
__________________________________________________________________________________________________
res4e (Add)                     (None, None, None, 1 0           bn4e_branch2c[0][0]
                                                                 res4d_relu[0][0]
__________________________________________________________________________________________________
res4e_relu (Activation)         (None, None, None, 1 0           res4e[0][0]
__________________________________________________________________________________________________
res4f_branch2a (Conv2D)         (None, None, None, 2 262144      res4e_relu[0][0]
__________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizati (None, None, None, 2 1024        res4f_branch2a[0][0]
__________________________________________________________________________________________________
res4f_branch2a_relu (Activation (None, None, None, 2 0           bn4f_branch2a[0][0]
__________________________________________________________________________________________________
padding4f_branch2b (ZeroPadding (None, None, None, 2 0           res4f_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4f_branch2b (Conv2D)         (None, None, None, 2 589824      padding4f_branch2b[0][0]
__________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizati (None, None, None, 2 1024        res4f_branch2b[0][0]
__________________________________________________________________________________________________
res4f_branch2b_relu (Activation (None, None, None, 2 0           bn4f_branch2b[0][0]
__________________________________________________________________________________________________
res4f_branch2c (Conv2D)         (None, None, None, 1 262144      res4f_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizati (None, None, None, 1 4096        res4f_branch2c[0][0]
__________________________________________________________________________________________________
res4f (Add)                     (None, None, None, 1 0           bn4f_branch2c[0][0]
                                                                 res4e_relu[0][0]
__________________________________________________________________________________________________
res4f_relu (Activation)         (None, None, None, 1 0           res4f[0][0]
__________________________________________________________________________________________________
res5a_branch2a (Conv2D)         (None, None, None, 5 524288      res4f_relu[0][0]
__________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizati (None, None, None, 5 2048        res5a_branch2a[0][0]
__________________________________________________________________________________________________
res5a_branch2a_relu (Activation (None, None, None, 5 0           bn5a_branch2a[0][0]
__________________________________________________________________________________________________
padding5a_branch2b (ZeroPadding (None, None, None, 5 0           res5a_branch2a_relu[0][0]
__________________________________________________________________________________________________
res5a_branch2b (Conv2D)         (None, None, None, 5 2359296     padding5a_branch2b[0][0]
__________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizati (None, None, None, 5 2048        res5a_branch2b[0][0]
__________________________________________________________________________________________________
res5a_branch2b_relu (Activation (None, None, None, 5 0           bn5a_branch2b[0][0]
__________________________________________________________________________________________________
res5a_branch2c (Conv2D)         (None, None, None, 2 1048576     res5a_branch2b_relu[0][0]
__________________________________________________________________________________________________
res5a_branch1 (Conv2D)          (None, None, None, 2 2097152     res4f_relu[0][0]
__________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizati (None, None, None, 2 8192        res5a_branch2c[0][0]
__________________________________________________________________________________________________
bn5a_branch1 (BatchNormalizatio (None, None, None, 2 8192        res5a_branch1[0][0]
__________________________________________________________________________________________________
res5a (Add)                     (None, None, None, 2 0           bn5a_branch2c[0][0]
                                                                 bn5a_branch1[0][0]
__________________________________________________________________________________________________
res5a_relu (Activation)         (None, None, None, 2 0           res5a[0][0]
__________________________________________________________________________________________________
res5b_branch2a (Conv2D)         (None, None, None, 5 1048576     res5a_relu[0][0]
__________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizati (None, None, None, 5 2048        res5b_branch2a[0][0]
__________________________________________________________________________________________________
res5b_branch2a_relu (Activation (None, None, None, 5 0           bn5b_branch2a[0][0]
__________________________________________________________________________________________________
padding5b_branch2b (ZeroPadding (None, None, None, 5 0           res5b_branch2a_relu[0][0]
__________________________________________________________________________________________________
res5b_branch2b (Conv2D)         (None, None, None, 5 2359296     padding5b_branch2b[0][0]
__________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizati (None, None, None, 5 2048        res5b_branch2b[0][0]
__________________________________________________________________________________________________
res5b_branch2b_relu (Activation (None, None, None, 5 0           bn5b_branch2b[0][0]
__________________________________________________________________________________________________
res5b_branch2c (Conv2D)         (None, None, None, 2 1048576     res5b_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizati (None, None, None, 2 8192        res5b_branch2c[0][0]
__________________________________________________________________________________________________
res5b (Add)                     (None, None, None, 2 0           bn5b_branch2c[0][0]
                                                                 res5a_relu[0][0]
__________________________________________________________________________________________________
res5b_relu (Activation)         (None, None, None, 2 0           res5b[0][0]
__________________________________________________________________________________________________
res5c_branch2a (Conv2D)         (None, None, None, 5 1048576     res5b_relu[0][0]
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, None, None, 5 2048        res5c_branch2a[0][0]
__________________________________________________________________________________________________
res5c_branch2a_relu (Activation (None, None, None, 5 0           bn5c_branch2a[0][0]
__________________________________________________________________________________________________
padding5c_branch2b (ZeroPadding (None, None, None, 5 0           res5c_branch2a_relu[0][0]
__________________________________________________________________________________________________
res5c_branch2b (Conv2D)         (None, None, None, 5 2359296     padding5c_branch2b[0][0]
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, None, None, 5 2048        res5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2b_relu (Activation (None, None, None, 5 0           bn5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2c (Conv2D)         (None, None, None, 2 1048576     res5c_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, None, None, 2 8192        res5c_branch2c[0][0]
__________________________________________________________________________________________________
res5c (Add)                     (None, None, None, 2 0           bn5c_branch2c[0][0]
                                                                 res5b_relu[0][0]
__________________________________________________________________________________________________
res5c_relu (Activation)         (None, None, None, 2 0           res5c[0][0]
__________________________________________________________________________________________________
C5_reduced (Conv2D)             (None, None, None, 2 524544      res5c_relu[0][0]
__________________________________________________________________________________________________
P5_upsampled (UpsampleLike)     (None, None, None, 2 0           C5_reduced[0][0]
                                                                 res4f_relu[0][0]
__________________________________________________________________________________________________
C4_reduced (Conv2D)             (None, None, None, 2 262400      res4f_relu[0][0]
__________________________________________________________________________________________________
P4_merged (Add)                 (None, None, None, 2 0           P5_upsampled[0][0]
                                                                 C4_reduced[0][0]
__________________________________________________________________________________________________
P4_upsampled (UpsampleLike)     (None, None, None, 2 0           P4_merged[0][0]
                                                                 res3d_relu[0][0]
__________________________________________________________________________________________________
C3_reduced (Conv2D)             (None, None, None, 2 131328      res3d_relu[0][0]
__________________________________________________________________________________________________
P6 (Conv2D)                     (None, None, None, 2 4718848     res5c_relu[0][0]
__________________________________________________________________________________________________
P3_merged (Add)                 (None, None, None, 2 0           P4_upsampled[0][0]
                                                                 C3_reduced[0][0]
__________________________________________________________________________________________________
C6_relu (Activation)            (None, None, None, 2 0           P6[0][0]
__________________________________________________________________________________________________
P3 (Conv2D)                     (None, None, None, 2 590080      P3_merged[0][0]
__________________________________________________________________________________________________
P4 (Conv2D)                     (None, None, None, 2 590080      P4_merged[0][0]
__________________________________________________________________________________________________
P5 (Conv2D)                     (None, None, None, 2 590080      C5_reduced[0][0]
__________________________________________________________________________________________________
P7 (Conv2D)                     (None, None, None, 2 590080      C6_relu[0][0]
__________________________________________________________________________________________________
regression_submodel (Model)     (None, None, 4)      2443300     P3[0][0]
                                                                 P4[0][0]
                                                                 P5[0][0]
                                                                 P6[0][0]
                                                                 P7[0][0]
__________________________________________________________________________________________________
classification_submodel (Model) (None, None, 1)      2381065     P3[0][0]
                                                                 P4[0][0]
                                                                 P5[0][0]
                                                                 P6[0][0]
                                                                 P7[0][0]
__________________________________________________________________________________________________
regression (Concatenate)        (None, None, 4)      0           regression_submodel[1][0]
                                                                 regression_submodel[2][0]
                                                                 regression_submodel[3][0]
                                                                 regression_submodel[4][0]
                                                                 regression_submodel[5][0]
__________________________________________________________________________________________________
classification (Concatenate)    (None, None, 1)      0           classification_submodel[1][0]
                                                                 classification_submodel[2][0]
                                                                 classification_submodel[3][0]
                                                                 classification_submodel[4][0]
                                                                 classification_submodel[5][0]
==================================================================================================
Total params: 36,382,957
Trainable params: 36,276,717
Non-trainable params: 106,240
______________________________________________________________________________________________

開始訓練

____
None
Epoch 1/30
2019-03-13 13:03:36.209789: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.07GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-03-13 13:03:36.266466: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.07GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-03-13 13:03:36.275037: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.25GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-03-13 13:03:36.288299: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.26GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-03-13 13:03:36.315962: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-03-13 13:03:36.651783: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.06GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-03-13 13:03:36.660588: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.06GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-03-13 13:03:36.680880: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-03-13 13:03:36.689715: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-03-13 13:03:36.698370: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.26GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
100/100 [==============================] - 20s 204ms/step - loss: 0.7675 - regression_loss: 0.4491 - classification_loss: 0.3185

Epoch 00001: saving model to ./snapshots\resnet50_csv_01.h5
Epoch 2/30
100/100 [==============================] - 18s 177ms/step - loss: 1.1027 - regression_loss: 0.4177 - classification_loss: 0.6850

Epoch 00002: saving model to ./snapshots\resnet50_csv_02.h5
Epoch 3/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9961 - regression_loss: 0.3917 - classification_loss: 0.6044

Epoch 00003: saving model to ./snapshots\resnet50_csv_03.h5

Epoch 00003: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.
Epoch 4/30
100/100 [==============================] - 16s 161ms/step - loss: 1.0841 - regression_loss: 0.3990 - classification_loss: 0.6850

Epoch 00004: saving model to ./snapshots\resnet50_csv_04.h5
Epoch 5/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9457 - regression_loss: 0.3413 - classification_loss: 0.6044

Epoch 00005: saving model to ./snapshots\resnet50_csv_05.h5

Epoch 00005: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08.
Epoch 6/30
100/100 [==============================] - 16s 160ms/step - loss: 1.0741 - regression_loss: 0.3891 - classification_loss: 0.6850

Epoch 00006: saving model to ./snapshots\resnet50_csv_06.h5
Epoch 7/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9355 - regression_loss: 0.3311 - classification_loss: 0.6044

Epoch 00007: saving model to ./snapshots\resnet50_csv_07.h5

Epoch 00007: ReduceLROnPlateau reducing learning rate to 1.0000000116860975e-08.
Epoch 8/30
100/100 [==============================] - 16s 160ms/step - loss: 1.0721 - regression_loss: 0.3871 - classification_loss: 0.6850

Epoch 00008: saving model to ./snapshots\resnet50_csv_08.h5
Epoch 9/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9348 - regression_loss: 0.3304 - classification_loss: 0.6044

Epoch 00009: saving model to ./snapshots\resnet50_csv_09.h5

Epoch 00009: ReduceLROnPlateau reducing learning rate to 9.999999939225292e-10.
Epoch 10/30
100/100 [==============================] - 16s 160ms/step - loss: 1.0737 - regression_loss: 0.3887 - classification_loss: 0.6850

Epoch 00010: saving model to ./snapshots\resnet50_csv_10.h5
Epoch 11/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9367 - regression_loss: 0.3322 - classification_loss: 0.6044

Epoch 00011: saving model to ./snapshots\resnet50_csv_11.h5

Epoch 00011: ReduceLROnPlateau reducing learning rate to 9.999999717180686e-11.
Epoch 12/30
100/100 [==============================] - 16s 160ms/step - loss: 1.0711 - regression_loss: 0.3860 - classification_loss: 0.6850

Epoch 00012: saving model to ./snapshots\resnet50_csv_12.h5
Epoch 13/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9347 - regression_loss: 0.3303 - classification_loss: 0.6044

Epoch 00013: saving model to ./snapshots\resnet50_csv_13.h5

Epoch 00013: ReduceLROnPlateau reducing learning rate to 9.99999943962493e-12.
Epoch 14/30
100/100 [==============================] - 16s 160ms/step - loss: 1.0722 - regression_loss: 0.3872 - classification_loss: 0.6850

Epoch 00014: saving model to ./snapshots\resnet50_csv_14.h5
Epoch 15/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9349 - regression_loss: 0.3305 - classification_loss: 0.6044

Epoch 00015: saving model to ./snapshots\resnet50_csv_15.h5

Epoch 00015: ReduceLROnPlateau reducing learning rate to 9.999999092680235e-13.
Epoch 16/30
100/100 [==============================] - 16s 160ms/step - loss: 1.0713 - regression_loss: 0.3863 - classification_loss: 0.6850

Epoch 00016: saving model to ./snapshots\resnet50_csv_16.h5
Epoch 17/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9337 - regression_loss: 0.3293 - classification_loss: 0.6044

Epoch 00017: saving model to ./snapshots\resnet50_csv_17.h5

Epoch 00017: ReduceLROnPlateau reducing learning rate to 9.9999988758398e-14.
Epoch 18/30
100/100 [==============================] - 16s 160ms/step - loss: 1.0704 - regression_loss: 0.3853 - classification_loss: 0.6850

Epoch 00018: saving model to ./snapshots\resnet50_csv_18.h5
Epoch 19/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9337 - regression_loss: 0.3293 - classification_loss: 0.6044

Epoch 00019: saving model to ./snapshots\resnet50_csv_19.h5

Epoch 00019: ReduceLROnPlateau reducing learning rate to 9.999999146890344e-15.
Epoch 20/30
100/100 [==============================] - 16s 160ms/step - loss: 1.0695 - regression_loss: 0.3845 - classification_loss: 0.6850

Epoch 00020: saving model to ./snapshots\resnet50_csv_20.h5
Epoch 21/30
100/100 [==============================] - 16s 160ms/step - loss: 0.9350 - regression_loss: 0.3306 - classification_loss: 0.6044

Epoch 00021: saving model to ./snapshots\resnet50_csv_21.h5

Epoch 00021: ReduceLROnPlateau reducing learning rate to 9.999998977483753e-16.
Epoch 22/30
100/100 [==============================] - 16s 160ms/step - loss: 1.0708 - regression_loss: 0.3858 - classification_loss: 0.6850

Epoch 00022: saving model to ./snapshots\resnet50_csv_22.h5
Epoch 23/30
100/100 [==============================] - 16s 165ms/step - loss: 0.9333 - regression_loss: 0.3288 - classification_loss: 0.6044

Epoch 00023: saving model to ./snapshots\resnet50_csv_23.h5

Epoch 00023: ReduceLROnPlateau reducing learning rate to 9.999998977483754e-17.
Epoch 24/30
100/100 [==============================] - 17s 167ms/step - loss: 1.0715 - regression_loss: 0.3865 - classification_loss: 0.6850

Epoch 00024: saving model to ./snapshots\resnet50_csv_24.h5
Epoch 25/30
100/100 [==============================] - 17s 165ms/step - loss: 0.9352 - regression_loss: 0.3308 - classification_loss: 0.6044

Epoch 00025: saving model to ./snapshots\resnet50_csv_25.h5

Epoch 00025: ReduceLROnPlateau reducing learning rate to 9.999998845134856e-18.
Epoch 26/30
100/100 [==============================] - 17s 167ms/step - loss: 1.0729 - regression_loss: 0.3878 - classification_loss: 0.6850

Epoch 00026: saving model to ./snapshots\resnet50_csv_26.h5
Epoch 27/30
100/100 [==============================] - 17s 167ms/step - loss: 0.9355 - regression_loss: 0.3311 - classification_loss: 0.6044

Epoch 00027: saving model to ./snapshots\resnet50_csv_27.h5

Epoch 00027: ReduceLROnPlateau reducing learning rate to 9.999999010570977e-19.
Epoch 28/30
100/100 [==============================] - 17s 167ms/step - loss: 1.0724 - regression_loss: 0.3874 - classification_loss: 0.6850

Epoch 00028: saving model to ./snapshots\resnet50_csv_28.h5
Epoch 29/30
100/100 [==============================] - 17s 166ms/step - loss: 0.9339 - regression_loss: 0.3294 - classification_loss: 0.6044

Epoch 00029: saving model to ./snapshots\resnet50_csv_29.h5

Epoch 00029: ReduceLROnPlateau reducing learning rate to 9.999999424161285e-20.
Epoch 30/30
100/100 [==============================] - 17s 166ms/step - loss: 1.0727 - regression_loss: 0.3877 - classification_loss: 0.6850

Epoch 00030: saving model to ./snapshots\resnet50_csv_30.h5

4.目標檢測

將模型進行轉換後才能進行目標檢測，在命令行執行convert_model.py

python D:/PyCharm/PycharmProjects/tf-gpu-env/project/keras-retinanet-master/keras_retinanet/bin/convert_model.py D:\PyCharm\PycharmProjects\tf-gpu-env\project\keras-retinanet-master\keras_retinanet\bin\snapshots\resnet50_csv_34.h5 D:\PyCharm\PycharmProjects\tf-gpu-env\project\keras-retinanet-master\cov_resnet50_csv_34.h5

如果沒有轉換的話，會報錯誤： boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0)) 這一句出錯了，錯誤是：ValueError: not enough values to unpack (expected 3, got 2)