зеркало из https://github.com/microsoft/caffe.git
Back-merge fixes + docs
and other fixes and documentation updates.
This commit is contained in:
Evan Shelhamer
Коммит
43bae95d7f
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@ -17,15 +17,14 @@ CUDA_DIR := /usr/local/cuda
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# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
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# CUDA_DIR := /usr
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# CUDA architecture setting: going with all of them (up to CUDA 5.5 compatible).
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# For the latest architecture, you need to install CUDA >= 6.0 and uncomment
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# the *_50 lines below.
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# CUDA architecture setting: going with all of them.
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# For CUDA < 6.0, comment the *_50 lines for compatibility.
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CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
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-gencode arch=compute_20,code=sm_21 \
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-gencode arch=compute_30,code=sm_30 \
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-gencode arch=compute_35,code=sm_35 \
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#-gencode arch=compute_50,code=sm_50 \
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#-gencode arch=compute_50,code=compute_50
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-gencode arch=compute_50,code=sm_50 \
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-gencode arch=compute_50,code=compute_50
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# BLAS choice:
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# atlas for ATLAS (default)
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@ -2,3 +2,7 @@
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Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.<br />
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Consult the [project website](http://caffe.berkeleyvision.org) for all documentation.
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Please ask usage questions and how to model different tasks on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
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@ -17,7 +17,7 @@ Networks are specified in simple config files, with no hard-coded parameters in
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Switching between CPU and GPU is as simple as setting a flag -- so models can be trained on a GPU machine, and then used on commodity clusters.
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**Readable & modifiable implementation** fosters active development.
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In Caffe's first six months, it has been forked by over 300 developers on Github, and many have pushed significant changes.
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In Caffe's first year, it has been forked by over 600 developers on Github, and many have pushed significant changes.
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**Speed** makes Caffe perfect for industry use.
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Caffe can process over **40M images per day** with a single NVIDIA K40 or Titan GPU\*.
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@ -35,11 +35,11 @@ Consult performance [details](/performance_hardware.html).
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## Documentation
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- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)<br>
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Caffe tutorial slides.
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- [ACM MM paper](http://ucb-icsi-vision-group.github.io/caffe-paper/caffe.pdf)<br>
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A 4-page report for the ACM Multimedia Open Source competition.
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- [Caffe Tutorial](/tutorial)<br>
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DIY deep learning with this hands-on tutorial to Caffe.
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Tutorial presentation.
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- [Tutorial Documentation](/tutorial)<br>
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Practical guide and framework reference.
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- [arXiv / ACM MM '14 paper](http://arxiv.org/abs/1408.5093)<br>
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A 4-page report for the ACM Multimedia Open Source competition (arXiv:1408.5093v1).
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- [Installation instructions](/installation.html)<br>
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Tested on Ubuntu, Red Hat, OS X.
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* [Model Zoo](/model_zoo.html)<br>
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@ -67,11 +67,11 @@ Developer documentation automagically generated from code comments.
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Please cite Caffe in your publications if it helps your research:
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@misc{Jia13caffe,
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Author = {Yangqing Jia},
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Title = { {Caffe}: An Open Source Convolutional Architecture for Fast Feature Embedding},
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Year = {2013},
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Howpublished = {\url{http://caffe.berkeleyvision.org/}}
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@article{jia2014caffe,
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Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
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Journal = {arXiv preprint arXiv:1408.5093},
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Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
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Year = {2014}
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}
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If you do publish a paper where Caffe helped your research, we encourage you to update the [publications wiki](https://github.com/BVLC/caffe/wiki/Publications).
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@ -79,17 +79,19 @@ Citations are also tracked automatically by [Google Scholar](http://scholar.goog
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## Acknowledgements
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Yangqing would like to thank the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
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The BVLC Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
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A core set of BVLC members have contributed much new functionality and many fixes since the original release (alphabetical by first name):
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[Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/).
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The BVLC members who have contributed to Caffe are (alphabetical by first name):
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[Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), and [Yangqing Jia](http://daggerfs.com/).
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Additionally, the open-source community plays a large and growing role in Caffe's development.
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The open-source community plays an important and growing role in Caffe's development.
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Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity, and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for a sorted list.
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We sincerely appreciate your interest and contributions!
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If you'd like to contribute, please read the [developing & contributing](development.html) guide.
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Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for advice.
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## Contacting us
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All questions about usage, installation, code, and applications should be searched for and asked on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
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@ -17,7 +17,7 @@ Caffe depends on several software packages.
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* [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.5 (recommended), 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
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* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
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* [OpenCV](http://opencv.org/).
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* [OpenCV](http://opencv.org/) (>= 2.4)
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* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 and 1.56 are tested)
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* `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
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* For the Python wrapper
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@ -15,7 +15,7 @@ This pass goes from bottom to top.
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<img src="fig/forward.jpg" alt="Forward pass" width="320">
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The data $x$ is passed through an inner product layer for $g(x)$ then through a softmax for $h(g(x))$ and softmax loss to give $f_W(x)$.
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The data $$x$$ is passed through an inner product layer for $$g(x)$$ then through a softmax for $$h(g(x))$$ and softmax loss to give $$f_W(x)$$.
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The **backward** pass computes the gradient given the loss for learning.
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In backward Caffe reverse-composes the gradient of each layer to compute the gradient of the whole model by automatic differentiation.
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@ -24,7 +24,7 @@ This pass goes from top to bottom.
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<img src="fig/backward.jpg" alt="Backward pass" width="320">
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The backward pass begins with the loss and computes the gradient with respect to the output $\frac{\partial f_W}{\partial h}$. The gradient with respect to the rest of the model is computed layer-by-layer through the chain rule. Layers with parameters, like the `INNER_PRODUCT` layer, compute the gradient with respect to their parameters $\frac{\partial f_W}{\partial W_{\text{ip}}}$ during the backward step.
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The backward pass begins with the loss and computes the gradient with respect to the output $$\frac{\partial f_W}{\partial h}$$. The gradient with respect to the rest of the model is computed layer-by-layer through the chain rule. Layers with parameters, like the `INNER_PRODUCT` layer, compute the gradient with respect to their parameters $$\frac{\partial f_W}{\partial W_{\text{ip}}}$$ during the backward step.
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These computations follow immediately from defining the model: Caffe plans and carries out the forward and backward passes for you.
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@ -24,8 +24,8 @@ You will first need to download and convert the data format from the [CIFAR-10 w
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cd $CAFFE_ROOT/data/cifar10
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./get_cifar10.sh
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cd $CAFFE_ROOT/examples/cifar10
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./create_cifar10.sh
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cd $CAFFE_ROOT
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./examples/cifar10/create_cifar10.sh
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If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be the dataset, `./cifar10-leveldb`, and the data set image mean `./mean.binaryproto`.
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@ -39,8 +39,8 @@ Training and Testing the "Quick" Model
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Training the model is simple after you have written the network definition protobuf and solver protobuf files (refer to [MNIST Tutorial](../examples/mnist.html)). Simply run `train_quick.sh`, or the following command directly:
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cd $CAFFE_ROOT/examples/cifar10
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./train_quick.sh
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cd $CAFFE_ROOT
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./examples/cifar10/train_quick.sh
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`train_quick.sh` is a simple script, so have a look inside. The main tool for training is `caffe` with the `train` action, and the solver protobuf text file as its argument.
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@ -3,7 +3,8 @@
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"description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
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"example_name": "ImageNet classification",
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"include_in_docs": true,
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"priority": 1
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"priority": 1,
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"signature": "sha256:2caae2c1fe3e282b8f836d380a45622351c91db18a1591e4f2fa67faba9ab72c"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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@ -19,7 +20,7 @@
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"\n",
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"Caffe provides a general Python interface for models with `caffe.Net` in `python/caffe/pycaffe.py`, but to make off-the-shelf classification easy we provide a `caffe.Classifier` class and `classify.py` script. Both Python and MATLAB wrappers are provided. However, the Python wrapper has more features so we will describe it here. For MATLAB, refer to `matlab/caffe/matcaffe_demo.m`.\n",
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"\n",
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"Before we begin, you must compile Caffe and install the python wrapper by setting your `PYTHONPATH`. If you haven't yet done so, please refer to the [installation instructions](installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`. Note that this pre-trained model is licensed for academic research / non-commercial use only.\n",
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"Before we begin, you must compile Caffe and install the python wrapper by setting your `PYTHONPATH`. If you haven't yet done so, please refer to the [installation instructions](installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.\n",
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"\n",
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"Ready? Let's start."
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]
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@ -54,13 +55,17 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255]."
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"Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n",
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"\n",
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"We will set the phase to test since we are doing testing, and will first use CPU for the computation."
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"caffe.set_phase_test()\n",
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"caffe.set_mode_cpu()\n",
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"net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
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" mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'),\n",
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" channel_swap=(2,1,0),\n",
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@ -72,25 +77,6 @@
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"outputs": [],
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"prompt_number": 2
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We will set the phase to test since we are doing testing, and will first use CPU for the computation."
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"net.set_phase_test()\n",
|
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"net.set_mode_cpu()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
|
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"prompt_number": 3
|
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},
|
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{
|
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"cell_type": "markdown",
|
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"metadata": {},
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@ -111,7 +97,7 @@
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 4,
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"prompt_number": 3,
|
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"text": [
|
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"<matplotlib.image.AxesImage at 0x7fda204c0e10>"
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]
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@ -125,7 +111,7 @@
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]
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}
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],
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"prompt_number": 4
|
||||
"prompt_number": 3
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},
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{
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"cell_type": "markdown",
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@ -163,7 +149,7 @@
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]
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}
|
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],
|
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"prompt_number": 5
|
||||
"prompt_number": 4
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
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@ -205,7 +191,7 @@
|
|||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 6
|
||||
"prompt_number": 5
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
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|
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@ -233,7 +219,7 @@
|
|||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 7
|
||||
"prompt_number": 6
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
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|
|
@ -266,7 +252,7 @@
|
|||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 8
|
||||
"prompt_number": 7
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
|
@ -279,12 +265,12 @@
|
|||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"net.set_mode_gpu()"
|
||||
"caffe.set_mode_gpu()"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 9
|
||||
"prompt_number": 8
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
|
@ -314,7 +300,7 @@
|
|||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 10,
|
||||
"prompt_number": 9,
|
||||
"text": [
|
||||
"[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
|
||||
]
|
||||
|
|
@ -328,7 +314,7 @@
|
|||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 10
|
||||
"prompt_number": 9
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
|
@ -355,7 +341,7 @@
|
|||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 11
|
||||
"prompt_number": 10
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
|
|
@ -375,7 +361,7 @@
|
|||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 12
|
||||
"prompt_number": 11
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
|
@ -404,4 +390,4 @@
|
|||
"metadata": {}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -3,7 +3,8 @@
|
|||
"description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
|
||||
"example_name": "Filter visualization",
|
||||
"include_in_docs": true,
|
||||
"priority": 2
|
||||
"priority": 2,
|
||||
"signature": "sha256:526501b358e0f60c489eaf5799e8603a75019cc65401533baa307b5603fdefa1"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
|
|
@ -61,10 +62,10 @@
|
|||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"caffe.set_phase_test()\n",
|
||||
"caffe.set_mode_cpu()\n",
|
||||
"net = caffe.Classifier(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
|
||||
" caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
|
||||
"net.set_phase_test()\n",
|
||||
"net.set_mode_cpu()\n",
|
||||
"# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
|
||||
"net.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')) # ImageNet mean\n",
|
||||
"net.set_raw_scale('data', 255) # the reference model operates on images in [0,255] range instead of [0,1]\n",
|
||||
|
|
@ -598,4 +599,4 @@
|
|||
"metadata": {}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -4,7 +4,7 @@
|
|||
"example_name": "Editing model parameters",
|
||||
"include_in_docs": true,
|
||||
"priority": 5,
|
||||
"signature": "sha256:179fb20339497f5e64f6fbeb57987f27a962b7ae6d940c8fede2631aba9bffaf"
|
||||
"signature": "sha256:bf84bcbd78fe007310f86d71c5969ba4205b6e06f408029860eec94821844bee"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
|
|
@ -279,9 +279,10 @@
|
|||
"import matplotlib.pyplot as plt\n",
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"caffe.set_phase_test()\n",
|
||||
"\n",
|
||||
"# load input and configure preprocessing\n",
|
||||
"im = caffe.io.load_image('images/cat.jpg')\n",
|
||||
"net_full_conv.set_phase_test()\n",
|
||||
"net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
|
||||
"net_full_conv.set_channel_swap('data', (2,1,0))\n",
|
||||
"net_full_conv.set_raw_scale('data', 255.0)\n",
|
||||
|
|
@ -352,4 +353,4 @@
|
|||
"metadata": {}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -3,21 +3,13 @@
|
|||
|
||||
#include "caffe/common.hpp"
|
||||
|
||||
namespace caffe {
|
||||
|
||||
/**
|
||||
* A minimal wrapper for boost::thread to force host compilation for boost
|
||||
* Defined in caffe/util/thread.hpp
|
||||
Forward declare boost::thread instead of including boost/thread.hpp
|
||||
to avoid a boost/NVCC issues (#1009, #1010) on OSX.
|
||||
*/
|
||||
class Thread {
|
||||
public:
|
||||
template<typename Callable, class A1>
|
||||
Thread(Callable func, A1 a1);
|
||||
void join();
|
||||
bool joinable();
|
||||
private:
|
||||
void* thread_;
|
||||
};
|
||||
namespace boost { class thread; }
|
||||
|
||||
namespace caffe {
|
||||
|
||||
/**
|
||||
* Virtual class encapsulate boost::thread for use in base class
|
||||
|
|
@ -26,7 +18,7 @@ class Thread {
|
|||
*/
|
||||
class InternalThread {
|
||||
public:
|
||||
InternalThread() : thread_(NULL) {}
|
||||
InternalThread() : thread_() {}
|
||||
virtual ~InternalThread();
|
||||
|
||||
/** Returns true if the thread was successfully started. **/
|
||||
|
|
@ -35,16 +27,16 @@ class InternalThread {
|
|||
/** Will not return until the internal thread has exited. */
|
||||
bool WaitForInternalThreadToExit();
|
||||
|
||||
bool is_started() const { return thread_ != NULL && thread_->joinable(); }
|
||||
bool is_started() const;
|
||||
|
||||
protected:
|
||||
/* Implement this method in your subclass
|
||||
with the code you want your thread to run. */
|
||||
virtual void InternalThreadEntry() {}
|
||||
|
||||
caffe::Thread* thread_;
|
||||
shared_ptr<boost::thread> thread_;
|
||||
};
|
||||
|
||||
} // namespace caffe
|
||||
|
||||
#endif
|
||||
#endif // CAFFE_INTERNAL_THREAD_HPP_
|
||||
|
|
|
|||
|
|
@ -1,25 +0,0 @@
|
|||
#ifndef CAFFE_THREAD_CPP_HPP_
|
||||
#define CAFFE_THREAD_CPP_HPP_
|
||||
|
||||
#include <boost/thread.hpp>
|
||||
#include "caffe/common.hpp"
|
||||
#include "caffe/internal_thread.hpp"
|
||||
|
||||
namespace caffe {
|
||||
|
||||
template<typename Callable, class A1>
|
||||
Thread::Thread(Callable func, A1 a1) {
|
||||
this->thread_ = new boost::thread(func, a1);
|
||||
}
|
||||
|
||||
void Thread::join() {
|
||||
static_cast<boost::thread*>(this->thread_)->join();
|
||||
}
|
||||
|
||||
bool Thread::joinable() {
|
||||
return static_cast<boost::thread*>(this->thread_)->joinable();
|
||||
}
|
||||
|
||||
} // namespace caffe
|
||||
|
||||
#endif
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
name: BVLC AlexNet Model
|
||||
caffemodel: bvlc_alexnet.caffemodel
|
||||
caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
|
||||
license: non-commercial
|
||||
license: unrestricted
|
||||
sha1: 9116a64c0fbe4459d18f4bb6b56d647b63920377
|
||||
caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077
|
||||
---
|
||||
|
|
@ -22,6 +22,4 @@ This model was trained by Evan Shelhamer @shelhamer
|
|||
|
||||
## License
|
||||
|
||||
The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
|
||||
"Researcher shall use the Database only for non-commercial research and educational purposes."
|
||||
Accordingly, this model is distributed under a non-commercial license.
|
||||
This model is released for unrestricted use.
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
name: BVLC CaffeNet Model
|
||||
caffemodel: bvlc_reference_caffenet.caffemodel
|
||||
caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel
|
||||
license: non-commercial
|
||||
license: unrestricted
|
||||
sha1: 4c8d77deb20ea792f84eb5e6d0a11ca0a8660a46
|
||||
caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077
|
||||
---
|
||||
|
|
@ -22,6 +22,4 @@ This model was trained by Jeff Donahue @jeffdonahue
|
|||
|
||||
## License
|
||||
|
||||
The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
|
||||
"Researcher shall use the Database only for non-commercial research and educational purposes."
|
||||
Accordingly, this model is distributed under a non-commercial license.
|
||||
This model is released for unrestricted use.
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
name: BVLC Reference RCNN ILSVRC13 Model
|
||||
caffemodel: bvlc_reference_rcnn_ilsvrc13.caffemodel
|
||||
caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_rcnn_ilsvrc13.caffemodel
|
||||
license: non-commercial
|
||||
license: unrestricted
|
||||
sha1: bdd8abb885819cba5e2fe1eb36235f2319477e64
|
||||
caffe_commit: a7e397abbda52c0b90323c23ab95bdeabee90a98
|
||||
---
|
||||
|
|
@ -17,6 +17,4 @@ This model was trained by Ross Girshick @rbgirshick
|
|||
|
||||
## License
|
||||
|
||||
The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
|
||||
"Researcher shall use the Database only for non-commercial research and educational purposes."
|
||||
Accordingly, this model is distributed under a non-commercial license.
|
||||
This model is released for unrestricted use.
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
from .pycaffe import Net, SGDSolver
|
||||
from .pycaffe import set_mode_cpu, set_mode_gpu, set_phase_train, set_phase_test
|
||||
from .pycaffe import set_device
|
||||
from .classifier import Classifier
|
||||
from .detector import Detector
|
||||
import io
|
||||
|
|
|
|||
|
|
@ -148,6 +148,13 @@ void PySGDSolver::SolveResume(const string& resume_file) {
|
|||
}
|
||||
|
||||
BOOST_PYTHON_MODULE(_caffe) {
|
||||
// Caffe utility methods
|
||||
bp::def("set_mode_cpu", &set_mode_cpu);
|
||||
bp::def("set_mode_gpu", &set_mode_gpu);
|
||||
bp::def("set_phase_train", &set_phase_train);
|
||||
bp::def("set_phase_test", &set_phase_test);
|
||||
bp::def("set_device", &Caffe::SetDevice);
|
||||
|
||||
// below, we prepend an underscore to methods that will be replaced
|
||||
// in Python
|
||||
bp::class_<PyNet, shared_ptr<PyNet> >(
|
||||
|
|
@ -158,11 +165,6 @@ BOOST_PYTHON_MODULE(_caffe) {
|
|||
.def("_forward", &PyNet::Forward)
|
||||
.def("_backward", &PyNet::Backward)
|
||||
.def("reshape", &PyNet::Reshape)
|
||||
.def("set_mode_cpu", &PyNet::set_mode_cpu)
|
||||
.def("set_mode_gpu", &PyNet::set_mode_gpu)
|
||||
.def("set_phase_train", &PyNet::set_phase_train)
|
||||
.def("set_phase_test", &PyNet::set_phase_test)
|
||||
.def("set_device", &PyNet::set_device)
|
||||
.add_property("_blobs", &PyNet::blobs)
|
||||
.add_property("layers", &PyNet::layers)
|
||||
.add_property("_blob_names", &PyNet::blob_names)
|
||||
|
|
|
|||
|
|
@ -20,6 +20,12 @@ using boost::shared_ptr;
|
|||
|
||||
namespace caffe {
|
||||
|
||||
// Selecting mode and phase.
|
||||
void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
|
||||
void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
|
||||
void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
|
||||
void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
|
||||
|
||||
// wrap shared_ptr<Blob> in a class that we construct in C++ and pass
|
||||
// to Python
|
||||
template <typename Dtype>
|
||||
|
|
@ -117,13 +123,6 @@ class PyNet {
|
|||
WriteProtoToBinaryFile(net_param, filename.c_str());
|
||||
}
|
||||
|
||||
// The caffe::Caffe utility functions.
|
||||
void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
|
||||
void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
|
||||
void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
|
||||
void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
|
||||
void set_device(int device_id) { Caffe::SetDevice(device_id); }
|
||||
|
||||
vector<PyBlob<float> > blobs() {
|
||||
return vector<PyBlob<float> >(net_->blobs().begin(), net_->blobs().end());
|
||||
}
|
||||
|
|
|
|||
|
|
@ -24,12 +24,12 @@ class Classifier(caffe.Net):
|
|||
preprocessing options.
|
||||
"""
|
||||
caffe.Net.__init__(self, model_file, pretrained_file)
|
||||
self.set_phase_test()
|
||||
caffe.set_phase_test()
|
||||
|
||||
if gpu:
|
||||
self.set_mode_gpu()
|
||||
caffe.set_mode_gpu()
|
||||
else:
|
||||
self.set_mode_cpu()
|
||||
caffe.set_mode_cpu()
|
||||
|
||||
if mean is not None:
|
||||
self.set_mean(self.inputs[0], mean)
|
||||
|
|
|
|||
|
|
@ -36,12 +36,12 @@ class Detector(caffe.Net):
|
|||
R-CNN feature extraction.
|
||||
"""
|
||||
caffe.Net.__init__(self, model_file, pretrained_file)
|
||||
self.set_phase_test()
|
||||
caffe.set_phase_test()
|
||||
|
||||
if gpu:
|
||||
self.set_mode_gpu()
|
||||
caffe.set_mode_gpu()
|
||||
else:
|
||||
self.set_mode_cpu()
|
||||
caffe.set_mode_cpu()
|
||||
|
||||
if mean is not None:
|
||||
self.set_mean(self.inputs[0], mean)
|
||||
|
|
|
|||
|
|
@ -8,6 +8,8 @@ from itertools import izip_longest
|
|||
import numpy as np
|
||||
|
||||
from ._caffe import Net, SGDSolver
|
||||
from ._caffe import set_mode_cpu, set_mode_gpu, set_phase_train, set_phase_test
|
||||
from ._caffe import set_device
|
||||
import caffe.io
|
||||
|
||||
# We directly update methods from Net here (rather than using composition or
|
||||
|
|
|
|||
|
|
@ -13,3 +13,4 @@ pandas>=0.12.0
|
|||
python-dateutil>=1.4,<2
|
||||
protobuf>=2.5.0
|
||||
python-gflags>=2.0
|
||||
pyyaml>=3.10
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
|
||||
PORT=${1:-4000}
|
||||
|
||||
echo "usage: build.sh [port]"
|
||||
echo "usage: build_docs.sh [port]"
|
||||
|
||||
# Find the docs dir, no matter where the script is called
|
||||
ROOT_DIR="$( cd "$(dirname "$0")"/.. ; pwd -P )"
|
||||
|
|
|
|||
|
|
@ -1,23 +1,24 @@
|
|||
#include <boost/thread.hpp>
|
||||
#include "caffe/internal_thread.hpp"
|
||||
|
||||
#include "caffe/util/thread.hpp"
|
||||
|
||||
namespace caffe {
|
||||
|
||||
InternalThread::~InternalThread() {
|
||||
WaitForInternalThreadToExit();
|
||||
if (thread_ != NULL) {
|
||||
delete thread_;
|
||||
}
|
||||
}
|
||||
|
||||
bool InternalThread::is_started() const {
|
||||
return thread_.get() != NULL && thread_->joinable();
|
||||
}
|
||||
|
||||
|
||||
bool InternalThread::StartInternalThread() {
|
||||
if (!WaitForInternalThreadToExit()) {
|
||||
return false;
|
||||
}
|
||||
try {
|
||||
thread_ = new caffe::Thread
|
||||
(&InternalThread::InternalThreadEntry, this);
|
||||
thread_.reset(
|
||||
new boost::thread(&InternalThread::InternalThreadEntry, this));
|
||||
} catch (...) {
|
||||
return false;
|
||||
}
|
||||
|
|
|
|||
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