@BestSonny ,Hi. when i use more than 2 graphic card to run , it wrong. like following: Check failed: error == cudaSuccess (77 vs. 0) an illegal memory access was encountered. Check failed: error == status == CUBLAS_STATUS_SUCCESS (11 vs. 0)CUBLAS_STATUS_MAPPING_ERROR

I guess that, maybe "Annotated_mask_data_layer" is something wrong. but i am not sure. Have met the problem? please give me some advice. Thanks in advance.

my cuda is 8.0 and cudnn is 5, but when i compile this code failed.

==== CXX .build_release/src/caffe/proto/caffe.pb.cc CXX src/caffe/blob.cpp In file included from ./include/caffe/util/device_alternate.hpp:40:0, from ./include/caffe/common.hpp:19, from ./include/caffe/blob.hpp:8, from src/caffe/blob.cpp:4: ./include/caffe/util/cudnn.hpp: In function ‘void caffe::cudnn::setConvolutionDesc(cudnnConvolutionStruct**, cudnnTensorDescriptor_t, cudnnFilterDescriptor_t, int, int, int, int)’: ./include/caffe/util/cudnn.hpp:112:3: error: too few arguments to function ‘cudnnStatus_t cudnnSetConvolution2dDescriptor(cudnnConvolutionDescriptor_t, int, int, int, int, int, int, cudnnConvolutionMode_t, cudnnDataType_t)’ CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv, ^ In file included from ./include/caffe/util/cudnn.hpp:5:0, from ./include/caffe/util/device_alternate.hpp:40, from ./include/caffe/common.hpp:19, from ./include/caffe/blob.hpp:8, from src/caffe/blob.cpp:4: /usr/local/cuda/include/cudnn.h:537:27: note: declared here cudnnStatus_t CUDNNWINAPI cudnnSetConvolution2dDescriptor( cudnnConvolutionDescriptor_t convDesc, ^ Makefile:577: recipe for target '.build_release/src/caffe/blob.o' failed make: *** [.build_release/src/caffe/blob.o] Error 1

====

please told me your cudnn and cuda version

Excuse me, I wanted to ask if I need opencv3 for this repository? I am getting errors which apparently are related to opencv3. I am using only opencv 2.4.9. And also do I have to use the caffe u ship? I have a caffe version on my ubuntu installation. Is yours modified somehow?

'make' command generates

~/Desktop/A_M-arbeit/G_Code/G_SSTD$ make -j8 LD -o .build_release/lib/libcaffe.so.1.0.0-rc3 /usr/bin/ld: cannot find -lopencv_imgcodecs /usr/bin/ld: cannot find -lopencv_videoio

warn("The default mode, 'constant', will be changed to 'reflect' in " F1206 10:15:04.766489 447 syncedmem.cpp:56] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Aborted (core dumped) How should I do for this?

SSTD article says: "we use a softmax function for binary classification of text or non-text, and apply the smooth-l1 loss for regressing 5 parameters for each word bounding box, including a parameter for box orientation"

I can find the box orientation parameter in the .cpp implementation But I cannot find the box orientation parameter in the .cu implementation

The .cpp implementation (on CPU) cannot run because "mask_resize_layer.cpp:42] Not Implemented Yet"

Is there a way to get orientation from .cu implementation?

Have you already implemented the mask_resize_layer.cpp?

     i build caffe src code with maskresize  layer  successfully. but  i can't  use python to create a MaskResize layer.  Could you tell me what should i do to correct it ?

Following is my python code:

name = '{}_mask_resize'.format(from_layers[i])

        mask_resize_param = {
                'output_height': 1,
                'output_width': 1,
                'factor_height': factors[i],
                'factor_width': factors[i],
                }
        net[name] = L.MaskRisize(net.slice1, mask_resize_param=mask_resize_param)

run the python code , it shows ：

I0108 23:21:11.590718 20850 layer_factory.hpp:77] Creating layer conv4_3_mask_resize

F0108 23:21:11.590739 20850 layer_factory.hpp:81] Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: MaskRisize (known types: AbsVal, Accuracy, AnnotatedData, AnnotatedDataMask, ArgMax, BNLL, BatchNorm, BatchReindex, Bias, Concat, ContrastiveLoss, Convolution, Crop, Data, Deconvolution, DetectionEvaluate, DetectionOutput, Dropout, DummyData, ELU, Eltwise, Embed, EuclideanLoss, Exp, Filter, Flatten, HDF5Data, HDF5Output, HingeLoss, Im2col, ImageData, InfogainLoss, InnerProduct, Input, LRN, LSTM, LSTMUnit, Log, MVN, MaskPooling, MaskResize, MemoryData, MultiBoxLoss, MultinomialLogisticLoss, Normalize, PReLU, Parameter, Permute, Pooling, Power, PriorBox, Python, RNN, ReLU, Reduction, Reshape, SPP, Scale, Sigmoid, SigmoidCrossEntropyLoss, Silence, Slice, SmoothL1Loss, Softmax, SoftmaxWithLoss, Split, TanH, Threshold, Tile, VideoData, WindowData) *** Check failure stack trace: ***

In training period, How can you produce segmentation loss and seg result? you use one softmaxwithlosslayer to calculate seg loss and one softmaxlayer to produce seg result? Or one softmaxlayer to produce seg result and a MultinomialLogisticLossLayer to produce seg loss?

#hi,BestSonny. Thanks for your good shared code. when i use the tool "convert_annoset_mask" to convert database into lmdb, i was OK. But when i train my net, i get a problem. It seems the tool "convert_annoset_mask" convert mask(single channel) into 3 channels. I don't knew where i did wrong. could you tell me why or share your "convert" shell ? Here is my "convert" command:

./build/tools/convert_annoset_mask --anno_type=detection --label_type=xml --label_map_file=/home/shi/caffe-ssd/data/VOC0712/labelmap_voc.prototxt --check_label=True --min_dim=0 --max_dim=0 --resize_height=0 --resize_width=0 --backend=lmdb --shuffle=False --check_size=False --encode_type=jpg --encoded=True --gray=False /home/shi/data/ ### /home/shi/data/all_train.txt /home/shi/data/VOC12_AUG/lmdb/VOC12_train_lmdb

here is the training wrong what showed in Caffe:

I0105 14:35:56.477653 16084 net.cpp:100] Creating Layer seg_loss

I0105 14:35:56.477654 16084 net.cpp:434] seg_loss <- upscore I0105 14:35:56.477658 16084 net.cpp:434] seg_loss <- mask I0105 14:35:56.477661 16084 net.cpp:408] seg_loss -> seg_loss I0105 14:35:56.477670 16084 layer_factory.hpp:77] Creating layer seg_loss I0105 14:35:56.485399 16084 softmax_loss_layer.cpp:47] softmaxwithloss bottom[0] size: 2,21,320,320 I0105 14:35:56.485427 16084 softmax_loss_layer.cpp:50] softmaxwithloss bottom[1] size: 2,3,320,320 F0105 14:35:56.485437 16084 softmax_loss_layer.cpp:53] Check failed: outer_num_ * inner_num_ == bottom[1]->count() (204800 vs. 614400) Number of labels must match number of predictions; e.g., if softmax axis == 1 and prediction shape is (N, C, H, W), label count (number of labels) must be NHW, with integer values in {0, 1, ..., C-1}.

I am gettin following error when running dem_test:

File "text/demo_test.py", line 21, in from nms.gpu_nms import gpu_nms ImportError: No module named gpu_nm

and the contained files in nms are:

cpu_nms.pyx gpu_nms.pyx nms_kernel.cu gpu_nms.hpp init.py py_cpu_nms.py

I also executed: sh text/download.sh before starting demo_test.py Best!

Valentin

Hi there,

I read the original paper and this implementation, and that's awsome!

However, I have a question concerning the details of SSTD net, and I'm really looking forward to see you reply:)

(1) In the deconvolution part, I see that you use groups=64 to upsample. But generally speaking, groups=1 might be more reasonale, so I guess it's for saving computational complexity? Or is there any other reasons?

(2) The original paper uses deconv3_3, conv1_1 to establish attention map. I see that you're using deconv16_16 and two conv3_3 to do it. Does it mean that this implementation is better than that in the original paper?

It's a very nice code and I really appretite your comment!

Thanks

Hi, Thank you very much for sharing your code. Currently I'm trying to reproduce result of ICDAR2015 in your paper but I cannot find prototxt for oriented texts (especifically for ICDAR2015 sets). It would be wonderful if you can share the model and pre-trained model for oriented texts.

Hello @BestSonny . Thank you for your contribution. i want to port your software to mobile. So currently, how many MB of GPU memory is used during test time.