Refactor: facenet->openface.

batch-represent/opts.lua

@@ -11,7 +11,7 @@ end
 function M.parse(arg)
    local cmd = torch.CmdLine()
    cmd:text()
-   cmd:text('FaceNet')
+   cmd:text('OpenFace')
    cmd:text()
    cmd:text('Options:')
 
@@ -21,7 +21,7 @@ function M.parse(arg)
               paths.concat(script_path(), '..', 'data', 'lfw', 'dlib-affine-sz:96'),
               'Home of dataset')
    cmd:option('-model',
-              paths.concat(script_path(), '..', 'models', 'facenet', 'nn4.v1.t7'),
+              paths.concat(script_path(), '..', 'models', 'openface', 'nn4.v1.t7'),
               'Path to model to use.')
    cmd:option('-imgDim', 96, 'Image dimension. nn1=224, nn4=96')
    cmd:option('-batchSize',       50,   'mini-batch size')

cloc.sh

@@ -1,4 +1,4 @@
 #!/bin/bash
 
-cloc batch-represent evaluation facenet models training util \
+cloc batch-represent evaluation openface models training util \
   examples/web/{*.{py,html,sh},js,css}

demos/compare.py

@@ -33,13 +33,13 @@
 fileDir = os.path.dirname(os.path.realpath(__file__))
 sys.path.append(os.path.join(fileDir, ".."))
 
-import facenet
-import facenet.helper
-from facenet.data import iterImgs
+import openface
+import openface.helper
+from openface.data import iterImgs
 
 modelDir = os.path.join(fileDir, '..', 'models')
 dlibModelDir = os.path.join(modelDir, 'dlib')
-facenetModelDir = os.path.join(modelDir, 'facenet')
+openfaceModelDir = os.path.join(modelDir, 'openface')
 
 parser = argparse.ArgumentParser()
 
@@ -52,7 +52,7 @@
                     default=os.path.expanduser("~/src/dlib-18.16/python_examples"),
                     help="dlib directory with the dlib.so Python library.")
 parser.add_argument('--networkModel', type=str, help="Path to Torch network model.",
-                    default=os.path.join(facenetModelDir, 'nn4.v1.t7'))
+                    default=os.path.join(openfaceModelDir, 'nn4.v1.t7'))
 parser.add_argument('--imgDim', type=int, help="Default image dimension.", default=96)
 parser.add_argument('--cuda', action='store_true')
 parser.add_argument('--verbose', action='store_true')
@@ -62,15 +62,15 @@
 sys.path.append(args.dlibRoot)
 import dlib
 
-from facenet.alignment import NaiveDlib # Depends on dlib.
+from openface.alignment import NaiveDlib # Depends on dlib.
 if args.verbose:
     print("Argument parsing and loading libraries took {} seconds.".format(time.time()-start))
 
 start = time.time()
 align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
-net = facenet.TorchWrap(args.networkModel, imgDim=args.imgDim, cuda=args.cuda)
+net = openface.TorchWrap(args.networkModel, imgDim=args.imgDim, cuda=args.cuda)
 if args.verbose:
-    print("Loading the dlib and FaceNet models took {} seconds.".format(time.time()-start))
+    print("Loading the dlib and OpenFace models took {} seconds.".format(time.time()-start))
 
 def getRep(imgPath):
     if args.verbose:
@@ -98,7 +98,7 @@ def getRep(imgPath):
     start = time.time()
     rep = net.forwardImage(alignedFace)
     if args.verbose:
-        print("  + FaceNet forward pass took {} seconds.".format(time.time()-start))
+        print("  + OpenFace forward pass took {} seconds.".format(time.time()-start))
         print("Representation:")
         print(rep)
         print("-----\n")

demos/web/create-unknown-vectors.py

@@ -27,16 +27,16 @@
 
 from skimage import io
 
-import facenet
-from facenet.alignment import NaiveDlib
-from facenet.data import iterImgs
+import openface
+from openface.alignment import NaiveDlib
+from openface.data import iterImgs
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('imgDir', type=str, help="Input image directory.")
     parser.add_argument('--numImages', type=int, default=1000)
     parser.add_argument('--model', type=str, help="TODO",
-                        default="./models/facenet/nn4.v1.t7")
+                        default="./models/openface/nn4.v1.t7")
     parser.add_argument('--outputFile', type=str,
                         help="Output file, stored in numpy serialized format.",
                         default="./unknown.npy")
@@ -46,14 +46,14 @@
 
     align = NaiveDlib("models/dlib/",
                       "shape_predictor_68_face_landmarks.dat")
-    facenet = facenet.TorchWrap(args.model, imgDim=args.imgDim, cuda=False)
+    openface = openface.TorchWrap(args.model, imgDim=args.imgDim, cuda=False)
 
     allImgs = list(iterImgs(args.imgDir))
     imgObjs = random.sample(allImgs, args.numImages)
 
     reps = []
     for imgObj in imgObjs:
-        rep = facenet.forward(imgObj.path)
+        rep = openface.forward(imgObj.path)
         rep = np.array(rep)
         reps.append(rep)
 

demos/web/index.html

@@ -167,7 +167,7 @@ <h2>Training
     <script src="vendor/js/handlebars.min.js"></script>
     <script src="vendor/js/jstat.min.js"></script>
     <script src="js/utils.js"></script>
-    <script src="js/facenet-demo.js"></script>
+    <script src="js/openface-demo.js"></script>
     <script type="text/javascript">
      registerHbarsHelpers();
 

demos/web/server.py

@@ -42,13 +42,13 @@
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
 
-import facenet
+import openface
 
 import tempfile
 
 modelDir = os.path.join(fileDir, '..', '..', 'models')
 dlibModelDir = os.path.join(modelDir, 'dlib')
-facenetModelDir = os.path.join(modelDir, 'facenet')
+openfaceModelDir = os.path.join(modelDir, 'openface')
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--dlibFaceMean', type=str, help="Path to dlib's face predictor.",
@@ -59,7 +59,7 @@
                     default=os.path.expanduser("~/src/dlib-18.15/python_examples"),
                     help="dlib directory with the dlib.so Python library.")
 parser.add_argument('--networkModel', type=str, help="Path to Torch network model.",
-                    default=os.path.join(facenetModelDir, 'nn4.v1.t7'))
+                    default=os.path.join(openfaceModelDir, 'nn4.v1.t7'))
 parser.add_argument('--imgDim', type=int, help="Default image dimension.", default=96)
 parser.add_argument('--cuda', type=bool, default=False)
 parser.add_argument('--unknown', type=bool, default=False, help='Try to predict unknown people')
@@ -68,10 +68,10 @@
 
 sys.path.append(args.dlibRoot)
 import dlib
-from facenet.alignment import NaiveDlib # Depends on dlib.
+from openface.alignment import NaiveDlib # Depends on dlib.
 
 align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
-net = facenet.TorchWrap(args.networkModel, imgDim=args.imgDim, cuda=args.cuda)
+net = openface.TorchWrap(args.networkModel, imgDim=args.imgDim, cuda=args.cuda)
 
 class Face:
     def __init__(self, rep, identity):
@@ -84,7 +84,7 @@ def __repr__(self):
             self.rep[0:5]
         )
 
-class FaceNetServerProtocol(WebSocketServerProtocol):
+class OpenFaceServerProtocol(WebSocketServerProtocol):
     def __init__(self):
         self.images = {}
         self.training = True
@@ -337,7 +337,7 @@ def processFrame(self, dataURL, identity):
     log.startLogging(sys.stdout)
 
     factory = WebSocketServerFactory("ws://localhost:9000", debug=False)
-    factory.protocol = FaceNetServerProtocol
+    factory.protocol = OpenFaceServerProtocol
 
     reactor.listenTCP(9000, factory)
     reactor.run()

evaluation/lfw.py

@@ -222,8 +222,8 @@ def plotClassifyExp(workDir):
                'DeepFace Ensemble [{:.2f}]'.format(deepfaceAUC),
                'OpenBR v1.1.0 [{:.2f}]'.format(brAUC),
                'Eigenfaces (img-restrict) [{:.2f}]'.format(eigAUC),
-               'CMU FaceNet nn4.v1 [{:.2f}]'.format(fnAUC),
-               'CMU FaceNet nn4.v1 folds'],
+               'OpenFace nn4.v1 [{:.2f}]'.format(fnAUC),
+               'OpenFace nn4.v1 folds'],
               loc='lower right')
 
     plt.plot([0,1], color='k', linestyle='dashed')

models/get-models.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 #
-# Download FaceNet models.
+# Download OpenFace models.
 
 cd "$(dirname "$0")"
 
@@ -13,10 +13,10 @@ if [ ! -f dlib/shape_predictor_68_face_landmarks.dat ]; then
   bunzip2 dlib/shape_predictor_68_face_landmarks.dat.bz2
 fi
 
-exit -1 # TODO - Add FaceNet nn4.v1 URL below
+exit -1 # TODO - Add OpenFace nn4.v1 URL below
 
-mkdir -p facenet
-if [ ! -f facenet/nn4.v1.t7 ]; then
+mkdir -p openface
+if [ ! -f openface/nn4.v1.t7 ]; then
   wget TODO \
-    -O facenet/nn4.v1.t7
+    -O openface/nn4.v1.t7
 fi

facenet/__init__.py → openface/__init__.py

@@ -26,11 +26,11 @@
 class TorchWrap:
     # Warning: This is very unstable!
     # Please join us in improving it at:
-    #   https://github.com/cmusatyalab/facenet/issues/1
-    #   https://github.com/cmusatyalab/facenet/issues/4
-    def __init__(self, model=os.path.join(myDir, '..', 'models', 'facenet', 'nn4.v1.t7'),
+    #   https://github.com/cmusatyalab/openface/issues/1
+    #   https://github.com/cmusatyalab/openface/issues/4
+    def __init__(self, model=os.path.join(myDir, '..', 'models', 'openface', 'nn4.v1.t7'),
                  imgDim=96, cuda=False):
-        cmd = ['/usr/bin/env', 'th', os.path.join(myDir,'facenet_server.lua'),
+        cmd = ['/usr/bin/env', 'th', os.path.join(myDir,'openface_server.lua'),
                '-model', model, '-imgDim', str(imgDim)]
         if cuda:
             cmd.append('-cuda')
@@ -42,7 +42,7 @@ def forwardPath(self, imgPath):
         return [float(x) for x in self.p.stdout.readline().strip().split(',')]
 
     def forwardImage(self, rgb):
-        t = '/tmp/facenet-torchwrap-{}.png'.format(binascii.b2a_hex(os.urandom(8)))
+        t = '/tmp/openface-torchwrap-{}.png'.format(binascii.b2a_hex(os.urandom(8)))
         cv2.imwrite(t, rgb)
         rep = np.array(self.forwardPath(t))
         os.remove(t)

facenet/facenet_server.lua → openface/openface_server.lua

@@ -17,8 +17,8 @@
 
 -- Warning: This is very unstable!
 -- Please join us in improving it at:
---   https://github.com/cmusatyalab/facenet/issues/1
---   https://github.com/cmusatyalab/facenet/issues/4
+--   https://github.com/cmusatyalab/openface/issues/1
+--   https://github.com/cmusatyalab/openface/issues/4
 
 require 'torch'
 require 'nn'
@@ -34,7 +34,7 @@ cmd:text('Face recognition server.')
 cmd:text()
 cmd:text('Options:')
 
-cmd:option('-model', './models/facenet/nn4.v1.t7', 'Path to model.')
+cmd:option('-model', './models/openface/nn4.v1.t7', 'Path to model.')
 cmd:option('-imgDim', 96, 'Image dimension. nn1=224, nn4=96')
 cmd:option('-cuda', false)
 cmd:text()

training/FaceNetOptim.lua → training/OpenFaceOptim.lua

@@ -2,9 +2,9 @@
 
 local pl = require('pl.import_into')()
 
-local FaceNetOptim, parent = torch.class('FaceNetOptim', 'nn.Optim')
+local OpenFaceOptim, parent = torch.class('OpenFaceOptim', 'nn.Optim')
 
-function FaceNetOptim:__init(model, optState, checkpoint_data)
+function OpenFaceOptim:__init(model, optState, checkpoint_data)
    parent.__init(self, model, optState, checkpoint_data)
 end
 
@@ -31,7 +31,7 @@ local function on_device_for_module(mod, f)
     return f()
 end
 
-function FaceNetOptim:optimizeTriplet(optimMethod, inputs, criterion)
+function OpenFaceOptim:optimizeTriplet(optimMethod, inputs, criterion)
    assert(optimMethod)
    assert(inputs)
    assert(criterion)

training/dataset.lua

@@ -1,5 +1,5 @@
 -- Source: https://github.com/facebook/fbcunn/blob/master/examples/imagenet/dataset.lua
--- Modified by Brandon Amos in Sept 2015 for FaceNet by adding
+-- Modified by Brandon Amos in Sept 2015 for OpenFace by adding
 -- `samplePeople` and `sampleTriplet`.
 
 require 'torch'

training/opts.lua

@@ -12,7 +12,7 @@ function M.parse(arg)
 
    local cmd = torch.CmdLine()
    cmd:text()
-   cmd:text('FaceNet')
+   cmd:text('OpenFace')
    cmd:text()
    cmd:text('Options:')
 
@@ -21,7 +21,7 @@ function M.parse(arg)
               paths.concat(script_path(), 'work'),
               'subdirectory in which to save/log experiments')
    cmd:option('-data',
-              paths.concat(os.getenv('HOME'), 'facenet', 'data',
+              paths.concat(os.getenv('HOME'), 'openface', 'data',
                            'casia-facescrub',
                            'dlib-affine-sz:96'),
                            -- 'dlib-affine-224-split'),
@@ -42,7 +42,7 @@ function M.parse(arg)
 
    ---------- Model options ----------------------------------
    cmd:option('-retrain',     'none', 'provide path to model to retrain with')
-   cmd:option('-modelDef', '../models/facenet/nn4.def.lua', 'path to model definiton')
+   cmd:option('-modelDef', '../models/openface/nn4.def.lua', 'path to model definiton')
    cmd:option('-imgDim', 96, 'Image dimension. nn1=224, nn4=96')
    cmd:text()
 

training/train.lua

@@ -16,12 +16,12 @@ require 'optim'
 require 'fbnn'
 require 'image'
 
-paths.dofile("FaceNetOptim.lua")
+paths.dofile("OpenFaceOptim.lua")
 
 
 local optimMethod = optim.adadelta
 local optimState = {} -- Use for other algorithms like SGD
-local optimator = FaceNetOptim(model, optimState)
+local optimator = OpenFaceOptim(model, optimState)
 
 trainLogger = optim.Logger(paths.concat(opt.save, 'train.log'))
 

util/align-dlib.py

@@ -70,21 +70,21 @@ def computeMeanMain(args):
 
 
 def alignMain(args):
-    facenet.helper.mkdirP(args.outputDir)
+    openface.helper.mkdirP(args.outputDir)
 
     imgs = list(iterImgs(args.inputDir))
 
     # Shuffle so multiple versions can be run at once.
     random.shuffle(imgs)
 
-    dlibAlign = facenet.alignment.NaiveDlib(args.modelDir,
+    dlibAlign = openface.alignment.NaiveDlib(args.modelDir,
                                             args.facePredictorName)
 
     nFallbacks = 0
     for imgObject in imgs:
         outDir = os.path.join(args.outputDir, imgObject.cls)
         imgName = "{}/{}.png".format(outDir, imgObject.name)
-        facenet.helper.mkdirP(outDir)
+        openface.helper.mkdirP(outDir)
         if not os.path.isfile(imgName):
             rgb = imgObject.getRGB(cache=False)
             out = dlibAlign.alignImg(args.method, args.size, rgb)
@@ -133,10 +133,10 @@ def alignMain(args):
     args = parser.parse_args()
 
     sys.path.append(args.dlibRoot)
-    import facenet
-    import facenet.helper
-    from facenet.data import iterImgs
-    from facenet.alignment import NaiveDlib
+    import openface
+    import openface.helper
+    from openface.data import iterImgs
+    from openface.alignment import NaiveDlib
 
     if args.mode == 'computeMean':
         computeMeanMain(args)