Hi, I tried to run an example on the python and everything turned out fine. But when I run an example on c ++, the result does not match the declared one.

The console displays: Processing time = 361 ms Output_blob (n, c, h, w) = 1, 19, 512, 1024 Processing time on the python is 1800 ms.

If I use weights with Segnet, then I get a fine result, but the speed has not changed.

I0626 19:33:00.715394 10046 net.cpp:100] Creating Layer bn1_0_2 I0626 19:33:00.715397 10046 net.cpp:434] bn1_0_2 <- conv1_0_2 I0626 19:33:00.715414 10046 net.cpp:408] bn1_0_2 -> bn1_0_2 I0626 19:33:00.715584 10046 net.cpp:150] Setting up bn1_0_2 I0626 19:33:00.715590 10046 net.cpp:157] Top shape: 4 64 128 256 (8388608) I0626 19:33:00.715593 10046 net.cpp:165] Memory required for data: 394657792 I0626 19:33:00.715602 10046 layer_factory.hpp:77] Creating layer drop1_0_3 ImportError: No module named spatial_dropout *** Aborted at 1498473180 (unix time) try "date -d @1498473180" if you are using GNU date *** PC: @ 0x7f5b31ba7a95 caffe::Blob<>::~Blob() *** SIGSEGV (@0x7f5b321d02b8) received by PID 10046 (TID 0x7f5b3239ca40) from PID 840762040; stack trace: *** @ 0x7f5b2fc1acb0 (unknown) @ 0x7f5b31ba7a95 caffe::Blob<>::~Blob() @ 0x7f5b31c3fed8 caffe::BasePrefetchingDataLayer<>::~BasePrefetchingDataLayer() @ 0x7f5b31c797d4 caffe::DenseImageDataLayer<>::~DenseImageDataLayer() @ 0x7f5b31c79909 caffe::DenseImageDataLayer<>::~DenseImageDataLayer() @ 0x40e226 std::vector<>::~vector() @ 0x7f5b31c1f098 caffe::Net<>::Net() @ 0x7f5b31bb7cea caffe::Solver<>::InitTrainNet() @ 0x7f5b31bb8efc caffe::Solver<>::Init() @ 0x7f5b31bb922a caffe::Solver<>::Solver() @ 0x7f5b31bc2b33 caffe::Creator_AdamSolver<>() @ 0x411ff6 caffe::SolverRegistry<>::CreateSolver() @ 0x40af72 train() @ 0x4089ac main @ 0x7f5b2fc05f45 (unknown) @ 0x4092b3 (unknown) @ 0x0 (unknown) Segmentation fault (core dumped)

Thank you for publishing your code. But why does the algorithm work much better on the cityscape test set images than on random traffic images from the internet? Is there some preprocessing necessary?

Hai,

During training of an encoder iam met with Import error:no module named spatial_dropout. Kindly suggest me how to solve this issue since iam under job pressure to get some decent results. Kindly do the needful.

Regards Kumar

Hello, I have a question.

When I train, I met the error.

I0626 10:41:47.785637 4806 layer_factory.hpp:77] Creating layer data I0626 10:41:47.786315 4806 net.cpp:100] Creating Layer data I0626 10:41:47.786324 4806 net.cpp:408] data -> data I0626 10:41:47.786337 4806 net.cpp:408] data -> label I0626 10:41:47.786352 4806 dense_image_data_layer.cpp:50] Opening file dataset/train_fine_cityscapes.txt I0626 10:41:47.788602 4806 dense_image_data_layer.cpp:60] Shuffling data I0626 10:41:47.789371 4806 dense_image_data_layer.cpp:65] A total of 2975 examples. I0626 10:41:47.857427 4806 dense_image_data_layer.cpp:118] output data size top[0]: 3,3,512,1024 I0626 10:41:47.857450 4806 dense_image_data_layer.cpp:121] output data size top[1]: 3,1,64,128 I0626 10:41:47.862567 4806 net.cpp:150] Setting up data I0626 10:41:47.862602 4806 net.cpp:157] Top shape: 3 3 512 1024 (4718592) I0626 10:41:47.862608 4806 net.cpp:157] Top shape: 3 1 64 128 (24576) I0626 10:41:47.862612 4806 net.cpp:165] Memory required for data: 18972672 I0626 10:41:47.862619 4806 layer_factory.hpp:77] Creating layer data_data_0_split I0626 10:41:47.862988 4806 net.cpp:100] Creating Layer data_data_0_split I0626 10:41:47.862996 4806 net.cpp:434] data_data_0_split <- data I0626 10:41:47.863008 4806 net.cpp:408] data_data_0_split -> data_data_0_split_0 I0626 10:41:47.863015 4806 net.cpp:408] data_data_0_split -> data_data_0_split_1 I0626 10:41:47.863376 4806 net.cpp:150] Setting up data_data_0_split I0626 10:41:47.863386 4806 net.cpp:157] Top shape: 3 3 512 1024 (4718592) I0626 10:41:47.863391 4806 net.cpp:157] Top shape: 3 3 512 1024 (4718592) I0626 10:41:47.863394 4806 net.cpp:165] Memory required for data: 56721408 I0626 10:41:47.863415 4806 layer_factory.hpp:77] Creating layer label_data_1_split I0626 10:41:47.863423 4806 net.cpp:100] Creating Layer label_data_1_split I0626 10:41:47.863430 4806 net.cpp:434] label_data_1_split <- label I0626 10:41:47.863436 4806 net.cpp:408] label_data_1_split -> label_data_1_split_0 I0626 10:41:47.863445 4806 net.cpp:408] label_data_1_split -> label_data_1_split_1 I0626 10:41:47.863474 4806 net.cpp:150] Setting up label_data_1_split I0626 10:41:47.863483 4806 net.cpp:157] Top shape: 3 1 64 128 (24576) I0626 10:41:47.863488 4806 net.cpp:157] Top shape: 3 1 64 128 (24576) I0626 10:41:47.863493 4806 net.cpp:165] Memory required for data: 56918016 I0626 10:41:47.863497 4806 layer_factory.hpp:77] Creating layer conv0_1 I0626 10:41:47.863513 4806 net.cpp:100] Creating Layer conv0_1 I0626 10:41:47.863519 4806 net.cpp:434] conv0_1 <- data_data_0_split_0 I0626 10:41:47.863526 4806 net.cpp:408] conv0_1 -> conv0_1 F0626 10:41:48.349544 4806 cudnn.hpp:113] Check failed: status == CUDNN_STATUS_SUCCESS (3 vs. 0) CUDNN_STATUS_BAD_PARAM *** Check failure stack trace: *** @ 0x7f0005e46daa (unknown) @ 0x7f0005e46ce4 (unknown) @ 0x7f0005e466e6 (unknown) @ 0x7f0005e49687 (unknown) @ 0x7f00064a3dc6 caffe::CuDNNConvolutionLayer<>::Reshape() @ 0x7f0006433045 caffe::Net<>::Init() @ 0x7f0006433ed5 caffe::Net<>::Net() @ 0x7f00063cccea caffe::Solver<>::InitTrainNet() @ 0x7f00063cdefc caffe::Solver<>::Init() @ 0x7f00063ce22a caffe::Solver<>::Solver() @ 0x7f00063d7b33 caffe::Creator_AdamSolver<>() @ 0x411ff6 caffe::SolverRegistry<>::CreateSolver() @ 0x40af72 train() @ 0x4089ac main @ 0x7f000441af45 (unknown) @ 0x4092b3 (unknown) @ (nil) (unknown) Aborted (core dumped)

my cudnn version is 5.1.0

The ENet paper claims to have 21fps on TX1 at the resolution 480*320 but I am getting 2fps (400ms forward pass time). Is there some kind of optimization that needs to be done.

Hi, I have try to training using multi-gpu . I got this error

[python_layer.hpp:25] PythonLayer is not implemented in Multi-GPU training

So, does any one have experience on change the code spatial_dropout.py to use multi-gpu ?

Hi Timo,

Got this when was running this command: /root/ENet/caffe-enet/build/tools/caffe train -solver /root/ENet/prototxts/enet_solver_encoder.prototxt

I0711 14:14:57.111349 209 net.cpp:100] Creating Layer eltwise2_0_4 I0711 14:14:57.111353 209 net.cpp:434] eltwise2_0_4 <- drop2_0_3 I0711 14:14:57.111371 209 net.cpp:434] eltwise2_0_4 <- bn2_0_4 I0711 14:14:57.111377 209 net.cpp:408] eltwise2_0_4 -> eltwise2_0_4 F0711 14:14:57.111387 209 eltwise_layer.cpp:34] Check failed: bottom[i]->shape() == bottom[0]->shape() *** Check failure stack trace: *** @ 0x7f6f05d78daa (unknown) @ 0x7f6f05d78ce4 (unknown) @ 0x7f6f05d786e6 (unknown) @ 0x7f6f05d7b687 (unknown) @ 0x7f6f0644fbbd caffe::EltwiseLayer<>::Reshape() @ 0x7f6f06317835 caffe::Net<>::Init() @ 0x7f6f063186c5 caffe::Net<>::Net() @ 0x7f6f063296ca caffe::Solver<>::InitTrainNet() @ 0x7f6f0632a8dc caffe::Solver<>::Init() @ 0x7f6f0632ac0a caffe::Solver<>::Solver() @ 0x7f6f062eab23 caffe::Creator_AdamSolver<>() @ 0x411fd6 caffe::SolverRegistry<>::CreateSolver() @ 0x40af52 train() @ 0x40898c main @ 0x7f6f0456ef45 (unknown) @ 0x409293 (unknown) @ (nil) (unknown) Aborted (core dumped)

What could be wrong?

Iam training with NVIDIA Geforce GTX titanZ. While training, i got stuck with check failed : cudaSuccess(2 Vs 0) out of memory.

Kindly suggest what is the size of GPU needed to train ENet?

Regards Kumar

I0627 03:28:07.408565 10388 solver.cpp:244] Train net output #20: per_class_accuracy = 0.037037 I0627 03:28:07.408572 10388 sgd_solver.cpp:106] Iteration 74980, lr = 5e-06 I0627 03:28:14.539170 10388 solver.cpp:454] Snapshotting to binary proto file /home/chamcham/Dev/ENet/weights/snapshots_encoder/enet_iter_75000.caffemodel I0627 03:28:14.571485 10388 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/chamcham/Dev/ENet/weights/snapshots_encoder/enet_iter_75000.solverstate I0627 03:28:14.706820 10388 solver.cpp:317] Iteration 75000, loss = 0.411978 I0627 03:28:14.706852 10388 solver.cpp:322] Optimization Done. I0627 03:28:14.706856 10388 caffe.cpp:254] Optimization Done. *** Aborted at 1498501694 (unix time) try "date -d @1498501694" if you are using GNU date *** PC: @ 0x7fba3dfb813c (unknown) *** SIGSEGV (@0x0) received by PID 10388 (TID 0x7fba406eda40) from PID 0; stack trace: *** @ 0x7fba3df6bcb0 (unknown) @ 0x7fba3dfb813c (unknown) @ 0x7fba3fef8a87 caffe::Blob<>::~Blob() @ 0x7fba3ff90ecc caffe::BasePrefetchingDataLayer<>::~BasePrefetchingDataLayer() @ 0x7fba3ffca7d4 caffe::DenseImageDataLayer<>::~DenseImageDataLayer() @ 0x7fba3ffca909 caffe::DenseImageDataLayer<>::~DenseImageDataLayer() @ 0x40e226 std::vector<>::~vector() @ 0x40ec10 caffe::Net<>::~Net() @ 0x40edc9 caffe::Net<>::~Net() @ 0x7fba3ff12690 caffe::AdamSolver<>::~AdamSolver() @ 0x40d2fe boost::detail::sp_counted_base::release() @ 0x40b0a7 train() @ 0x4089ac main @ 0x7fba3df56f45 (unknown) @ 0x4092b3 (unknown) @ 0x0 (unknown) Segmentation fault (core dumped)

Hi I am following your tutorial to train on the Cityscape dataset.

I am running the following code for the ENet predictions.

python calculate_class_weighting.py --source ENet/dataset/train_fine_cityscapes.txt --num_classes 19 

but i get:

Traceback (most recent call last):
  File "calculate_class_weighting.py", line 48, in <module>
    if ((labels == i).sum()) == 0:
AttributeError: 'bool' object has no attribute 'sum'

thank you

Does anyone reproduce the ENet？Why the params and GFLOPs of my reproduced Network are about 10 and 4 times bigger than the values mentiond in the original paper (Table 3) respectively? My calculated value—— params: 3.5Million，GFLOPs: 16.9

python3 create_enet_prototxt.py --source /ENet/dataset/train_fine_cityscapes.txt --mode train_encoder Traceback (most recent call last): File "create_enet_prototxt.py", line 284, in network, prev_layer = bottleneck(n, prev_layer, 1, 0, 64, 'downsampling') # stage, number_bottleneck, num_input, File "create_enet_prototxt.py", line 210, in bottleneck return n.to_proto(), last_layer File "/ENet/caffe-enet/python/caffe/net_spec.py", line 189, in to_proto top._to_proto(layers, names, autonames) File "/ENet/caffe-enet/python/caffe/net_spec.py", line 97, in _to_proto return self.fn._to_proto(layers, names, autonames) File "/ENet/caffe-enet/python/caffe/net_spec.py", line 156, in _to_proto _param_names[self.type_name] + '_param'), k, v) File "/ENet/caffe-enet/python/caffe/net_spec.py", line 79, in assign_proto setattr(proto, name, val) TypeError: 16.0 has type float, but expected one of: int, long

Those unlabeled area is red in your example prediction. I checked your implementation, you have 19 output classes for Cityscape, but just do argmax to get the class. How do you deal with unlabeled class? Thanks.

Thanks for your works,but when I use this net to train the datasets of Pascal_voc,the result wasn't convergency. What should I do to solve this error?

hi,I started recently dealing with object segmentation and I want to know what are the output points positions of the segmentation operation ### (the points positions of the detector edge) and how i got it.any help??