Merge pull request BVLC#7 from BVLC/master

from master fork

docs/installation.md

@@ -18,7 +18,7 @@ Caffe depends on several software packages.
 * [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.*)
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
 * [OpenCV](http://opencv.org/).
-* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 is tested)
+* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 and 1.56 are tested)
 * `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
 * For the Python wrapper
     * `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
@@ -141,11 +141,13 @@ Do `brew edit opencv` and change the lines that look like the two lines below to
 **NOTE**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
 In other `ENV` settings, things may not work as expected.
 
+**NOTE**: There is currently a conflict between boost 1.56 and CUDA in some configurations. Check the [conflict description](https://github.com/BVLC/caffe/issues/1193#issuecomment-57491906) and try downgrading to 1.55.
+
 #### 10.8-specific Instructions
 
 Simply run the following:
 
-    brew install --build-from-source --with-python boost
+    brew install --build-from-source boost boost-python
     brew install --with-python protobuf
     for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
 
@@ -186,16 +188,16 @@ After this, run
 
     for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
     brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --with-python --fresh -vd boost
+    brew install --build-from-source --fresh -vd boost boost-python
 
 **Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.
 
 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
 If you're not using Anaconda, include `hdf5` in the list above.
 
-**Note** that in order to build the caffe python wrappers you must install boost using the --with-python option:
+**Note** that in order to build the Caffe Python wrappers you must install `boost` and `boost-python`:
 
-    brew install --build-from-source --with-python --fresh -vd boost
+    brew install --build-from-source --fresh -vd boost boost-python
 
 **Note** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
 

examples/finetune_flickr_style/readme.md

@@ -34,7 +34,7 @@ All steps are to be done from the caffe root directory.
 The dataset is distributed as a list of URLs with corresponding labels.
 Using a script, we will download a small subset of the data and split it into train and val sets.
 
-    caffe % ./models/finetune_flickr_style/assemble_data.py -h
+    caffe % ./examples/finetune_flickr_style/assemble_data.py -h
     usage: assemble_data.py [-h] [-s SEED] [-i IMAGES] [-w WORKERS]
 
     Download a subset of Flickr Style to a directory
@@ -48,7 +48,7 @@ Using a script, we will download a small subset of the data and split it into tr
                             num workers used to download images. -x uses (all - x)
                             cores.
 
-    caffe % python models/finetune_flickr_style/assemble_data.py --workers=-1 --images=2000 --seed 831486
+    caffe % python examples/finetune_flickr_style/assemble_data.py --workers=-1 --images=2000 --seed 831486
     Downloading 2000 images with 7 workers...
     Writing train/val for 1939 successfully downloaded images.
 

examples/mnist/readme.md

@@ -1,23 +1,22 @@
 ---
-title: MNIST Tutorial
-description: Train and test "LeNet" on MNIST data.
+title: LeNet MNIST Tutorial
+description: Train and test "LeNet" on the MNIST handwritten digit data.
 category: example
 include_in_docs: true
 priority: 1
 ---
 
-# Training MNIST with Caffe
+# Training LeNet on MNIST with Caffe
 
 We will assume that you have Caffe successfully compiled. If not, please refer to the [Installation page](/installation.html). In this tutorial, we will assume that your Caffe installation is located at `CAFFE_ROOT`.
 
 ## Prepare Datasets
 
 You will first need to download and convert the data format from the MNIST website. To do this, simply run the following commands:
 
-    cd $CAFFE_ROOT/data/mnist
-    ./get_mnist.sh
-    cd $CAFFE_ROOT/examples/mnist
-    ./create_mnist.sh
+    cd $CAFFE_ROOT
+    ./data/mnist/get_mnist.sh
+    ./examples/mnist/create_mnist.sh
 
 If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be two datasets, `mnist_train_lmdb`, and `mnist_test_lmdb`.
 
@@ -228,8 +227,8 @@ Check out the comments explaining each line in the prototxt `$CAFFE_ROOT/example
 
 Training the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run `train_lenet.sh`, or the following command directly:
 
-    cd $CAFFE_ROOT/examples/mnist
-    ./train_lenet.sh
+    cd $CAFFE_ROOT
+    ./examples/mnist/train_lenet.sh
 
 `train_lenet.sh` is a simple script, but here is a quick explanation: the main tool for training is `caffe` with action `train` and the solver protobuf text file as its argument.
 

src/caffe/solver.cpp

@@ -23,7 +23,7 @@ template <typename Dtype>
 Solver<Dtype>::Solver(const string& param_file)
     : net_() {
   SolverParameter param;
-  ReadProtoFromTextFile(param_file, &param);
+  ReadProtoFromTextFileOrDie(param_file, &param);
   Init(param);
 }