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A easy-to-use FPN modified from https://github.com/yangxue0827/FPN_Tensorflow.

Python 99.56% Shell 0.44%

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SCALE_FACTORS = [10., 5., 1., 0.5]作用是什么。应该怎么修改

您好，请问fast-r-cnn的分类损失函数是否可以更换?我在换的过程中出现了.InvalidArgumentError: tags and values not the same shape: [] != [256] (tag 'RPN_LOSS/cls_loss')这个问题，请问您是否遇到过？

谢谢~

Hello! I run eval.sh ,it occured:
Traceback (most recent call last):
File "./tools/eval.py", line 326, in
eval_dict_convert(args)
File "./tools/eval.py", line 109, in eval_dict_convert
restorer, restore_ckpt = restore_model.get_restorer(checkpoint_path=args.weights)
TypeError: get_restorer() got an unexpected keyword argument 'checkpoint_path'

which datasets to use

Hello:
I just want to know what datasets you use in this project.I can't find any datasets about one target to train.Do I need to crate my own datasets?
Thank you!

do you have try this code on voc07+12?

I try this code on voc07/12. But it is not good at find other object but person.The car dog car can't be found in the picture.only a few car or other object can be found.

为什么我训练时loss一直在上下波动并不收敛，结果也没有框出东西来，类别也是0，啥都检测不到

TypeError: cond() got an unexpected keyword argument 'true_fn'

hello,when i run python train.py, it occured:
Traceback (most recent call last):
File "train.py", line 230, in
train()
File "train.py", line 35, in train
is_training=True)
File "../data/io/read_tfrecord.py", line 83, in next_batch
is_training=is_training)
File "../data/io/read_tfrecord.py", line 57, in read_and_prepocess_single_img
target_shortside_len=shortside_len)
File "../data/io/image_preprocess.py", line 27, in short_side_resize
false_fn=lambda: (target_shortside_len * h//w, target_shortside_len))
TypeError: cond() got an unexpected keyword argument 'true_fn'

It seems stop when I train my data

Output is as follows ：

+ set -e
+ export PYTHONUNBUFFERED=True
+ PYTHONUNBUFFERED=True
+ GPU_ID=0
+ DATASET=cow
+ array=($@)
+ len=2
+ EXTRA_ARGS=
+ EXTRA_ARGS_SLUG=
++ date +%Y_%m_%d_%H_%M_%S
+ LOG=logs/FPN_cow.txt.2018_08_31_14_51_58
+ exec
++ tee -a logs/FPN_cow.txt.2018_08_31_14_51_58
tee: logs/FPN_cow.txt.2018_08_31_14_51_58: No such file or directory
+ echo Logging output to logs/FPN_cow.txt.2018_08_31_14_51_58
Logging output to logs/FPN_cow.txt.2018_08_31_14_51_58
+ CUDA_VISIBLE_DEVICES=0
+ python ./tools/train.py
tfrecord path is --> /home/dongpeijie/FPN_TensorFlow/data/tfrecords/cow_train*
/home/dongpeijie/miniconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
model restore from pretrained mode, path is: data/pretrained_weights/resnet_v1_101.ckpt
2018-08-31 14:52:26.532747: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2018-08-31 14:52:26.532790: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2018-08-31 14:52:26.532798: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-08-31 14:52:26.532803: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-08-31 14:52:26.532808: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2018-08-31 14:52:28.340651: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties:
name: Tesla P100-PCIE-16GB
major: 6 minor: 0 memoryClockRate (GHz) 1.3285
pciBusID 0000:06:00.0
Total memory: 15.89GiB
Free memory: 15.60GiB
2018-08-31 14:52:28.340709: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0
2018-08-31 14:52:28.340716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0:   Y
2018-08-31 14:52:28.340727: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:06:00.0)
restore model

I have waited for a long time without other output information.

Thank you for helping me solving the problem.

我的错误和您提示得一样，可是按照修改提示修改之后还是会报错。

你使用的opencv是什么版本的啊？

生成classes.txt出现问题

我想使用pascal voc2007数据集进行训练，数据集本身有Anonotions，tfrecords文件，那么我该怎样生成classes.txt文件呢

OutOfRangeError: PaddingFIFOQueue '_2_get_batch/batch/padding_fifo_queue' is closed and has insufficient elements

Hi, I have followed your steps to convert my own tfrecord and start to train, yet there is something wrong called "OutOfRangeError: PaddingFIFOQueue '_2_get_batch/batch/padding_fifo_queue' is closed and has insufficient elements".

Do you know what might be the cause of this error?

Thanks!

Have you tried the VGg network? I have been trying to change the network, modify C2, C3, C4 and C5. There has been a mistake. Can you give me some advice?

test.py运行的图片是放在了哪里啊

想要测试的图片放在哪了啊，只看到了图片数量，没看到读取路径啊

你好，看精度直接运行eval.py吗？

Can I change the value of batch-size？

Can I change the value of batch-size？If I change its value, other values need to be modified at the same time？ Thanks！!

How to use the pre-trained model?

When I trained my model with pretrained-model, the variables are zeros like this :

model restore from pretrained mode, path is : /home/..//data/pretrained_weights/resnet_50.ckpt
resnet_v1_50/conv1/weights:0
resnet_v1_50/conv1/BatchNorm/gamma:0
resnet_v1_50/conv1/BatchNorm/beta:0
resnet_v1_50/conv1/BatchNorm/moving_mean:0
resnet_v1_50/conv1/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_1/bottleneck_v1/shortcut/weights:0
resnet_v1_50/block1/unit_1/bottleneck_v1/shortcut/BatchNorm/gamma:0
resnet_v1_50/block1/unit_1/bottleneck_v1/shortcut/BatchNorm/beta:0
resnet_v1_50/block1/unit_1/bottleneck_v1/shortcut/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_1/bottleneck_v1/shortcut/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/weights:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/BatchNorm/gamma:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/BatchNorm/beta:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv2/weights:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv2/BatchNorm/gamma:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv2/BatchNorm/beta:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv2/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv2/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv3/weights:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv3/BatchNorm/gamma:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv3/BatchNorm/beta:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv3/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_1/bottleneck_v1/conv3/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv1/weights:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv1/BatchNorm/gamma:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv1/BatchNorm/beta:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv1/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv1/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv2/weights:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv2/BatchNorm/gamma:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv2/BatchNorm/beta:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv2/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv2/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv3/weights:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv3/BatchNorm/gamma:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv3/BatchNorm/beta:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv3/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_2/bottleneck_v1/conv3/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv1/weights:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv1/BatchNorm/gamma:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv1/BatchNorm/beta:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv1/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv1/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv2/weights:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv2/BatchNorm/gamma:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv2/BatchNorm/beta:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv2/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv2/BatchNorm/moving_variance:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv3/weights:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv3/BatchNorm/gamma:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv3/BatchNorm/beta:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv3/BatchNorm/moving_mean:0
resnet_v1_50/block1/unit_3/bottleneck_v1/conv3/BatchNorm/moving_variance:0
resnet_v1_50/block2/unit_1/bottleneck_v1/shortcut/weights:0
resnet_v1_50/block2/unit_1/bottleneck_v1/shortcut/BatchNorm/gamma:0
resnet_v1_50/block2/unit_1/bottleneck_v1/shortcut/BatchNorm/beta:0
resnet_v1_50/block2/unit_1/bottleneck_v1/shortcut/BatchNorm/moving_mean:0
resnet_v1_50/block2/unit_1/bottleneck_v1/shortcut/BatchNorm/moving_variance:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv1/weights:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv1/BatchNorm/gamma:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv1/BatchNorm/beta:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv1/BatchNorm/moving_mean:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv1/BatchNorm/moving_variance:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv2/weights:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv2/BatchNorm/gamma:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv2/BatchNorm/beta:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv2/BatchNorm/moving_mean:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv2/BatchNorm/moving_variance:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv3/weights:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv3/BatchNorm/gamma:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv3/BatchNorm/beta:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv3/BatchNorm/moving_mean:0
resnet_v1_50/block2/unit_1/bottleneck_v1/conv3/BatchNorm/moving_variance:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv1/weights:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv1/BatchNorm/gamma:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv1/BatchNorm/beta:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv1/BatchNorm/moving_mean:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv1/BatchNorm/moving_variance:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv2/weights:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv2/BatchNorm/gamma:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv2/BatchNorm/beta:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv2/BatchNorm/moving_mean:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv2/BatchNorm/moving_variance:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv3/weights:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv3/BatchNorm/gamma:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv3/BatchNorm/beta:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv3/BatchNorm/moving_mean:0
resnet_v1_50/block2/unit_2/bottleneck_v1/conv3/BatchNorm/moving_variance:0
resnet_v1_50/block2/unit_3/bottleneck_v1/conv1/weights:0
resnet_v1_50/block2/unit_3/bottleneck_v1/conv1/BatchNorm/gamma:0
resnet_v1_50/block2/unit_3/bottleneck_v1/conv1/BatchNorm/beta:0
resnet_v1_50/block2/unit_3/bottleneck_v1/conv1/BatchNorm/moving_mean:0
resnet_v1_50/block2/unit_3/bottleneck_v1/conv1/BatchNorm/moving_variance:0
resnet_v1_50/block2/unit_3/bottleneck_v1/conv2/weights:0
resnet_v1_50/block2/unit_3/bottleneck_v1/conv2/BatchNorm/gamma:0
resnet_v1_50/block2/unit_3/bottleneck_v1/conv2/BatchNorm/beta:0
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