seth-park / text_objseg_caffe Goto Github PK

Hi Seth:
Thank you for your great work.
When I train the detection model. I got the following error:

I0322 11:15:13.847193 15945 net.cpp:129] Top shape: 50 1000 (50000)
I0322 11:15:13.847195 15945 net.cpp:137] Memory required for data: 5770873800
I0322 11:15:13.847198 15945 layer_factory.hpp:77] Creating layer img_l2norm
F0322 11:15:13.847214 15945 layer_factory.hpp:81] Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: L2Normalize (known types: AbsVal, Accuracy, ArgMax, BNLL, BatchNorm, BatchReindex, Bias, Concat, ContrastiveLoss, Convolution, Crop, Data, Deconvolution, Dropout, DummyData, ELU, Eltwise, Embed, EuclideanLoss, Exp, Filter, Flatten, HDF5Data, HDF5Output, HingeLoss, Im2col, ImageData, InfogainLoss, InnerProduct, Input, LRN, LSTM, LSTMUnit, Log, MVN, MemoryData, MultinomialLogisticLoss, PReLU, Parameter, Pooling, Power, Python, RNN, ReLU, Reduction, Reshape, SPP, Scale, Sigmoid, SigmoidCrossEntropyLoss, Silence, Slice, Softmax, SoftmaxWithLoss, Split, TanH, Threshold, Tile, WindowData)
*** Check failure stack trace: ***
Aborted (core dumped)

I am using official version of caffe and successfully compiled the caffe with pycaffe and anaconda. and followed your steps.But I still got the error. Is there anything I am missing?
My system is Ubuntu16.04 LTS with Nvidia Cuda8.0 Titan Xp
I appreciate if you can help me.

Hi @Seth-Park . It seems from https://github.com/Seth-Park/text_objseg_caffe/blob/master/seg_model/referit_data_provider.py#L47 that in the data-providing Python layer, the image is in RGB order. However, shouldn't image input to Caffe be in BGR order? Is there any channel-switching step that I missed?
Thank you very much!

Hi @Seth-Park . Thank you for the code. I was wondering which Caffe version are you using? The network structure uses L2Normalize layer, which I cannot find in the BVLC Caffe.
Thanks!

when I running train_det_model.py，I meet this question:
Cannot copy param 0 weights from layer 'fc6'; shape mismatch. Source param shape is 1024 512 3 3 (4718592); target param shape is 4096 25088 (102760448). To learn this layer's parameters from scratch rather than copying from a saved net, rename the layer.
how can i solve this problem?