caffe

I'm trying to reshape the size of a convolution layer of a caffemodel (This is a follow-up question to this question ). Although there is a tutorial on how to do net surgery , it only shows how to copy weight parameters from one caffemodel to another of the same size. Instead I need to add a new channel (all 0) to my convolution filter such that it changes its size from currently ( 64 x 3 x 3 x 3 ) to ( 64 x 4 x 3 x 3 ). Say the convolution layer is called 'conv1' . This is what I tried so far: # Load the original network and extract the fully connected layers' parameters. net = caffe.Net('..

安装依赖 sudo apt -get install libprotobuf -dev libleveldb -dev libsnappy -dev libhdf5 - serial -dev protobuf -compile sudo apt - get install -- no -install -recommends libboost -all -dev sudo apt - get install libopenblas -dev liblapack -dev libatlas -base -dev sudo apt - get install libgflags -dev libgoogle -glog -dev liblmdb -dev sudo apt - get install git cmake build -essential 修改配置文件 来源： https://www.cnblogs.com/gris/p/11957016.html

nvidia-smi是用来查看GPU版本信息，GPU使用信息查询： nvidia-smi 第一栏的Fan：N/A是风扇转速，从0到100%之间变动，这个速度是计算机期望的风扇转速，实际情况下如果风扇堵转，可能打不到显示的转速。有的设备不会返回转速，因为它不依赖风扇冷却而是通过其他外设保持低温（比如我们实验室的服务器是常年放在空调房间里的）。 第二栏的Temp：是温度，单位摄氏度。 第三栏的Perf：是性能状态，从P0到P12，P0表示最大性能，P12表示状态最小性能。 第四栏下方的Pwr：是能耗，上方的Persistence-M：是持续模式的状态，持续模式虽然耗能大，但是在新的GPU应用启动时，花费的时间更少，这里显示的是off的状态。 第五栏的Bus-Id是涉及GPU总线的东西，domain:bus:device.function 第六栏的Disp.A是Display Active，表示GPU的显示是否初始化。 第五第六栏下方的Memory Usage是显存使用率。 第七栏是浮动的GPU利用率。 第八栏上方是关于ECC的东西。 第八栏下方Compute M是计算模式。 下面一张表示每个进程占用的显存使用率。 显存占用和GPU占用是两个不一样的东西，显卡是由GPU和显存等组成的，显存和GPU的关系有点类似于内存和CPU的关系。我跑caffe代码的时候显存占得少，GPU占得多

I'm writing C++ code using CAFFE to predict a single (for now) image. The image has already been preprocessed and is in .png format. I have created a Net object and read in the trained model. Now, I need to use the .png image as an input layer and call net.Forward() - but can someone help me figure out how to set the input layer? I found a few examples on the web, but none of them work, and almost all of them use deprecated functionality. According to: Berkeley's Net API , using "ForwardPrefilled" is deprecated, and using "Forward(vector, float*)" is deprecated. API indicates that one should

I am finetuning a network. In a specific case I want to use it for regression, which works. In another case, I want to use it for classification. For both cases I have an HDF5 file, with a label. With regression, this is just a 1-by-1 numpy array that contains a float. I thought I could use the same label for classification, after changing my EuclideanLoss layer to SoftmaxLoss. However, then I get a negative loss as so: Iteration 19200, loss = -118232 Train net output #0: loss = 39.3188 (* 1 = 39.3188 loss) Can you explain if, and so what, goes wrong? I do see that the training loss is about

In the AlexNet implementation in caffe, I saw the following layer in the deploy.prototxt file: layer { name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7" dropout_param { dropout_ratio: 0.5 } } Now the key idea of dropout is to randomly drop units (along with their connections) from the neural network during training. Does this mean that I can simply delete this layer from deploy.prototxt, as this file is meant to be used during testing only? Yes. Dropout is not required during Testing. Even if you include a dropout layer, nothing special happens during Testing. See the source code of

How can I calculate the area (in the original image) covered by each of the filters in my network? e.g. Lets say the size of the image is WxW pixels. I am using the following network: layer 1 : conv : 5x5 layer 2 : pool : 3x3 layer 3 : conv : 5x5 ..... layer N : conv : 5x5 I want to calculate how much area in the original image will be covered by each filter. e.g. the filter in the layer 1 will cover 5x5 pixels in the original image. A similar problem would be, how many pixels will be covered by each activation? which is essentially the same as, how large an input image has to be in order to

I am using fast-rcnn and try to train the system for new class (label) I followed this: https://github.com/EdisonResearch/fast-rcnn/tree/master/help/train Placed the images Placed the annotations Prepare the ImageSet with all the image name prefix Prepared selective search output: train.mat I failed while running the train_net.py with the following error: ./tools/train_net.py --gpu 0 --solver models/VGG_1024_pascal2007/solver.prototxt --imdb voc_2007_train_top_5000 Called with args: Namespace(cfg_file=None, gpu_id=0, imdb_name='voc_2007_train_top_5000', max_iters=40000, pretrained_model=None,

is possible to set all GPUs for Caffe (especially pyCaffe)? Something like: caffe train -solver examples/mnist/lenet_solver.prototxt -gpu all AFAIK Caffe is not supporting multi gpu training at the moment. It is planned for future release. See a discussion here . It seems like NVIDIA's branch of caffe has this functionality. See the issue here . Both forks have supported multi-GPU for a while now. BVLC/caffe got support for multi-GPU on 08/13/2015 (see commit , issue ). NVIDIA/caffe got support for multi-GPU on 06/19/2015 (see release note ). You may be interested to know that there is a

I am trying to compile caffe with python interface on an Ubuntu 14.04 machine. I have installed Anaconda and opencv with conda install opencv . I have also installed all the requirement stipulated in the coffee and changed the commentary blocks in makefile.config so that PYTHON_LIB and PYTHON_INCLUDE point towards Anaconda distributions. When I am calling make all , the following command is issued: g++ .build_release/tools/caffe.o -o .build_release/tools/caffe.bin -pthread -fPIC -DNDEBUG -O2 -DWITH_PYTHON_LAYER -I/home/andrei/anaconda/include -I/home/andrei/anaconda/include/python2.7 -I/home