# Caffe standalone Dockerfiles.
The `standalone` subfolder contains docker files for generating both CPU and GPU executable images for Caffe. The images can be built using make, or by running:
```
docker build -t caffe:cpu standalone/cpu
```
for example. (Here `gpu` can be substituted for `cpu`, but to keep the readme simple, only the `cpu` case will be discussed in detail).
Note that the GPU standalone requires a CUDA 7.5 capable driver to be installed on the system and [nvidia-docker] for running the Docker containers. Here it is generally sufficient to use `nvidia-docker` instead of `docker` in any of the commands mentioned.
# Running Caffe using the docker image
In order to test the Caffe image, run:
```
docker run -ti caffe:cpu caffe --version
```
which should show a message like:
```
libdc1394 error: Failed to initialize libdc1394
caffe version 1.0.0-rc3
```
One can also build and run the Caffe tests in the image using:
```
docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest"
```
In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running:
```
docker run -ti --volume=$(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt
```
will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume=` Docker flag).
Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used:
```
docker run -ti --volume=$(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt
```
where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir=` Docker command line option.
# Other use-cases
Although running the `caffe` command in the docker containers as described above serves many purposes, the container can also be used for more interactive use cases. For example, specifying `bash` as the command instead of `caffe` yields a shell that can be used for interactive tasks. (Since the caffe build requirements are included in the container, this can also be used to build and run local versions of caffe).
Another use case is to run python scripts that depend on `caffe`'s Python modules. Using the `python` command instead of `bash` or `caffe` will allow this, and an interactive interpreter can be started by running:
```
docker run -ti caffe:cpu python
```
(`ipython` is also available in the container).
Since the `caffe/python` folder is also added to the path, the utility executable scripts defined there can also be used as executables. This includes `draw_net.py`, `classify.py`, and `detect.py`
CVPR图像去阴影，毕业设计可运用，效果显著
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CVPR图像去阴影，毕业设计可运用，效果显著 （535个子文件） 
matte_3_5_same.caffemodel 60.96MB gtest_main.cc 2KB caffe.cloc 1KB Utils.cmake 13KB Cuda.cmake 11KB Summary.cmake 7KB Targets.cmake 7KB FindLAPACK.cmake 7KB Dependencies.cmake 5KB ConfigGen.cmake 4KB ProtoBuf.cmake 4KB FindMKL.cmake 3KB FindNumPy.cmake 2KB gflags.cmake 2KB Misc.cmake 2KB FindMatlabMex.cmake 2KB FindLevelDB.cmake 2KB glog.cmake 2KB FindAtlas.cmake 2KB FindOpenBLAS.cmake 2KB FindGFlags.cmake 2KB lint.cmake 1KB FindGlog.cmake 1KB FindvecLib.cmake 1KB FindLMDB.cmake 1KB FindSnappy.cmake 1KB CNAME 25B Makefile.config 4KB gtest-all.cpp 329KB test_net.cpp 75KB test_upgrade_proto.cpp 71KB test_pooling_layer.cpp 50KB test_gradient_based_solver.cpp 44KB test_convolution_layer.cpp 43KB upgrade_proto.cpp 40KB net.cpp 39KB test_neuron_layer.cpp 34KB test_split_layer.cpp 25KB test_scale_layer.cpp 21KB caffe_.cpp 21KB test_bias_layer.cpp 19KB data_transformer.cpp 18KB solver.cpp 17KB test_random_number_generator.cpp 17KB window_data_layer.cpp 17KB test_lrn_layer.cpp 17KB base_conv_layer.cpp 16KB test_inner_product_layer.cpp 15KB test_data_layer.cpp 15KB _caffe.cpp 14KB blob.cpp 14KB test_io.cpp 13KB caffe.cpp 13KB parallel.cpp 13KB sgd_solver.cpp 12KB test_deconvolution_layer.cpp 12KB test_accuracy_layer.cpp 11KB pooling_layer.cpp 11KB test_memory_data_layer.cpp 11KB test_data_transformer.cpp 11KB lrn_layer.cpp 11KB common.cpp 10KB test_crop_layer.cpp 10KB test_argmax_layer.cpp 10KB math_functions.cpp 10KB cudnn_conv_layer.cpp 10KB test_reshape_layer.cpp 10KB test_blob.cpp 9KB test_reduction_layer.cpp 9KB batch_norm_layer.cpp 9KB im2col.cpp 9KB scale_layer.cpp 9KB layer_factory.cpp 9KB im2col_layer.cpp 8KB test_slice_layer.cpp 8KB test_eltwise_layer.cpp 8KB spp_layer.cpp 8KB test_concat_layer.cpp 8KB test_dummy_data_layer.cpp 7KB io.cpp 7KB test_filler.cpp 7KB test_embed_layer.cpp 7KB test_image_data_layer.cpp 7KB test_math_functions.cpp 6KB image_data_layer.cpp 6KB extract_features.cpp 6KB hdf5.cpp 6KB test_im2col_layer.cpp 6KB test_stochastic_pooling.cpp 6KB softmax_loss_layer.cpp 6KB hdf5_data_layer.cpp 6KB test_tile_layer.cpp 5KB inner_product_layer.cpp 5KB eltwise_layer.cpp 5KB test_mvn_layer.cpp 5KB test_contrastive_loss_layer.cpp 5KB crop_layer.cpp 5KB test_power_layer.cpp 5KB test_softmax_layer.cpp 5KB insert_splits.cpp 5KB共 535 条
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