Clone and modified from https://github.com/yangxue0827/FPN_Tensorflow
1.3 Run convert_txt2xml.py to convert txt annotations to xml ones from folder data/layer/annotations to folder data/layer/Annotations.
1.4 Run data/io/convert_data_to_tfrecord.py to convert images and annotations to tfrecords files which located in folder data/tfrecords.
- run
tools/test.pyto label ground truth annotations on images to check if the data are right.
- Parameters
NET_NAME = 'resnet_v1_101'
DATASET_NAME = 'layer'
VERSION = 'v1_{}'.format(DATASET_NAME)
ANCHOR_SCALES = [0.5, 1., 2.]
ANCHOR_RATIOS = [0.1, 0.2, 0.3] # height to width
SCALE_FACTORS = [10., 5., 1., 0.5]- Classes
CLASS_NUM = 1 #Equal to really class number (except for background class)configs/config_res101.py
pretrained_model_path # to use a pretrained model
batch_size- line 1 of function
next_batch
['nwpu', 'airplane', 'SSDD', 'ship', 'pascal', 'coco', 'icecream', 'layer']- Directly add them if the number of classes is not big
- Or add
NAME_LABEL_MAPusing generateddata/layer/classes.txtfile. - Examples:
elif cfgs.DATASET_NAME == 'icecream':
NAME_LABEL_MAP = {}
NAME_LABEL_MAP['back_ground'] = 0
with open('classes.txt') as f:
lines = [line.strip() for line in f.readlines()]
for i, line in enumerate(lines, 1):
NAME_LABEL_MAP[line] = i
elif cfgs.DATASET_NAME == 'layer':
NAME_LABEL_MAP = {
'back_ground': 0,
"layer": 1
}3.Run scripts/train.sh to train the model and the output and logs will be saved in the root directory
cd $ FPN_Tensorflow
# ./scripts/train.sh GPU DATASET
./scripts/train.sh 0 cooler4.Run scripts/[test.sh, eval.sh, demo.sh, inference.sh] to test, evaluate the model or run a demo using the trained model
cd $ FPN_Tensorflow
# ./scripts/test.sh GPU MODEL_PATH IMG_NUM
./scripts/test.sh 0 output/res101_trained_weights/v1_layer/layer_model.ckpt 20
# ./scripts/eval.sh GPU MODEL_PATH IMG_NUM
./scripts/eval.sh 0 output/res101_trained_weights/v1_layer/layer_model.ckpt 20
# ./scripts/demo.sh GPU MODEL_PATH
./scripts/demo.sh 0 output/res101_trained_weights/v1_layer/layer_model.ckpt
# ./scripts/inference.sh GPU MODEL_PATH
./scripts/inference.sh 0 output/res101_trained_weights/v1_layer/layer_model.ckptInvalidArgumentError (see above for traceback): LossTensor is inf or nan : Tensor had NaN values
[[Node: train_op/CheckNumerics = CheckNumerics[T=DT_FLOAT, message="LossTensor is inf or nan", _device="/job:localhost/replica:0/task:0/device:GPU:0"](control_dependency)]]
[[Node: gradients/rpn_net/concat_grad/Squeeze_3/_1493 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_8085_gradients/rpn_net/concat_grad/Squeeze_3", tensor_type=DT_INT64, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]- This was raised because the annotations were beyond the images, for example, xmax or ymax larger than width or height of image, or xmin or ymin less than 0.
- This error has been solved by adding these lines in
data/io/convert_data_to_tfrecord.py:
xmin = np.where(xmin < 0, 0, xmin)
ymin = np.where(ymin < 0, 0, ymin)
xmax = np.where(xmax > img_width, img_width, xmax)
ymax = np.where(ymax > img_height, img_height, ymax)2.tensorflow.python.framework.errors_impl.UnknownError: exceptions.OverflowError: signed integer is less than minimum
UnknownError (see above for traceback): exceptions.OverflowError: signed integer is less than minimum
[[Node: fast_rcnn_loss/PyFunc_1 = PyFunc[Tin=[DT_FLOAT, DT_FLOAT, DT_INT32], Tout=[DT_UINT8], token="pyfunc_7", _device="/job:localhost/replica:0/task:0/device:CPU:0"](rpn_losses/Squeeze/_1579, fast_rcnn_loss/mul_1/_1759, fast_rcnn_loss/strided_slice_1/_1761)]]
[[Node: draw_proposals/Reshape_2/tensor/_1825 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_3802_draw_proposals/Reshape_2/tensor", tensor_type=DT_UINT8, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]- The reason of this error is the same as
1.InvalidArgumentError.
A Tensorflow implementation of FPN detection framework. You can refer to the paper Feature Pyramid Networks for Object Detection Rotation detection method baesd on FPN reference R2CNN and R2CNN_HEAD and R-DFPN If useful to you, please star to support my work. Thanks.
ubuntu(Encoding problems may occur on windows) + python2 + tensorflow1.2 + cv2 + cuda8.0 + GeForce GTX 1080 You can also use docker environment, command: docker push yangxue2docker/tensorflow3_gpu_cv2_sshd:v1.0
Clone the repository
git clone https://github.com/yangxue0827/FPN_Tensorflow.gitThe image name is best in English. The data is VOC format, reference here data path format ($FPN_ROOT/data/io/divide_data.py) VOCdevkit
VOCdevkit_train
Annotation JPEGImages
VOCdevkit_test
Annotation JPEGImages
cd $FPN_ROOT/data/io/
python convert_data_to_tfrecord.py --VOC_dir='***/VOCdevkit/VOCdevkit_train/' --save_name='train' --img_format='.jpg' --dataset='ship'1、Unzip the weight $FPN_ROOT/output/res101_trained_weights/*.rar 2、put images in $FPN_ROOT/tools/inference_image 3、Configure parameters in $FPN_ROOT/libs/configs/cfgs.py and modify the project's root directory 4、image slice
cd $FPN_ROOT/tools
python inference.py5、big image
cd $FPN_ROOT/tools
python demo.py --src_folder=.\demo_src --des_folder=.\demo_des1、Modify $FPN_ROOT/libs/lable_name_dict/***_dict.py, corresponding to the number of categories in the configuration file 2、download pretrain weight(resnet_v1_101_2016_08_28.tar.gz or resnet_v1_50_2016_08_28.tar.gz) from here, then extract to folder $FPN_ROOT/data/pretrained_weights 3、
cd $FPN_ROOT/tools
python train.pycd $FPN_ROOT/tools
python $FPN_ROOT/tools/test.pycd $FPN_ROOT/tools
python ship_eval.pytensorboard --logdir=$FPN_ROOT/output/res101_summary/
This code works better when detecting single targets, but not suitable for multi-target detection tasks. Hope you can help find bugs, thank you very much.