"description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
"example_name": "ImageNet classification",
"include_in_docs": true,
- "priority": 1
+ "priority": 1,
+ "signature": "sha256:2caae2c1fe3e282b8f836d380a45622351c91db18a1591e4f2fa67faba9ab72c"
},
"nbformat": 3,
"nbformat_minor": 0,
"cell_type": "markdown",
"metadata": {},
"source": [
- "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]."
+ "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",
+ "\n",
+ "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
+ "caffe.set_phase_test()\n",
+ "caffe.set_mode_cpu()\n",
"net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
" mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'),\n",
" channel_swap=(2,1,0),\n",
"cell_type": "markdown",
"metadata": {},
"source": [
- "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "net.set_phase_test()\n",
- "net.set_mode_cpu()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
"Let's take a look at our example image with Caffe's image loading helper."
]
},
{
"metadata": {},
"output_type": "pyout",
- "prompt_number": 4,
+ "prompt_number": 3,
"text": [
"<matplotlib.image.AxesImage at 0x7fda204c0e10>"
]
]
}
],
- "prompt_number": 4
+ "prompt_number": 3
},
{
"cell_type": "markdown",
]
}
],
- "prompt_number": 5
+ "prompt_number": 4
},
{
"cell_type": "markdown",
]
}
],
- "prompt_number": 6
+ "prompt_number": 5
},
{
"cell_type": "markdown",
]
}
],
- "prompt_number": 7
+ "prompt_number": 6
},
{
"cell_type": "markdown",
]
}
],
- "prompt_number": 8
+ "prompt_number": 7
},
{
"cell_type": "markdown",
"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",
{
"metadata": {},
"output_type": "pyout",
- "prompt_number": 10,
+ "prompt_number": 9,
"text": [
"[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
]
]
}
],
- "prompt_number": 10
+ "prompt_number": 9
},
{
"cell_type": "markdown",
]
}
],
- "prompt_number": 11
+ "prompt_number": 10
},
{
"cell_type": "code",
]
}
],
- "prompt_number": 12
+ "prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {}
}
]
-}
+}
\ No newline at end of file
"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,
"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",
"metadata": {}
}
]
-}
+}
\ No newline at end of file
"example_name": "Editing model parameters",
"include_in_docs": true,
"priority": 5,
- "signature": "sha256:179fb20339497f5e64f6fbeb57987f27a962b7ae6d940c8fede2631aba9bffaf"
+ "signature": "sha256:bf84bcbd78fe007310f86d71c5969ba4205b6e06f408029860eec94821844bee"
},
"nbformat": 3,
"nbformat_minor": 0,
"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",
"metadata": {}
}
]
-}
+}
\ No newline at end of file
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
}
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> >(
.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)
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>
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());
}
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)
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)
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
projects / platform / upstream / caffe.git / commitdiff