Update 00-classification.ipynb

Author: lyou

examples/00-classification.ipynb

@@ -58,7 +58,7 @@
     "# The caffe module needs to be on the Python path;\n",
     "#  we'll add it here explicitly.\n",
     "import sys\n",
-    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
+    "caffe_root = '/opt/caffe/'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
     "sys.path.insert(0, caffe_root + 'python')\n",
     "\n",
     "import caffe\n",
@@ -88,12 +88,11 @@
     }
    ],
    "source": [
-    "import os\n",
-    "if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
-    "    print 'CaffeNet found.'\n",
-    "else:\n",
-    "    print 'Downloading pre-trained CaffeNet model...'\n",
-    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+    "model_weights_url = 'http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel' \n",
+    "!wget -O bvlc_reference_caffenet.caffemodel $model_weights_url \n",
+    "model_weights = './bvlc_reference_caffenet.caffemodel'  \n"
+
+
    ]
   },
   {
@@ -116,7 +115,7 @@
     "caffe.set_mode_cpu()\n",
     "\n",
     "model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\n",
-    "model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+
     "\n",
     "net = caffe.Net(model_def,      # defines the structure of the model\n",
     "                model_weights,  # contains the trained weights\n",
@@ -153,7 +152,7 @@
     "# load the mean ImageNet image (as distributed with Caffe) for subtraction\n",
     "mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
     "mu = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values\n",
-    "print 'mean-subtracted values:', zip('BGR', mu)\n",
+    "print ('mean-subtracted values:', list(zip('BGR', mu))\n)",
     "\n",
     "# create transformer for the input called 'data'\n",
     "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
@@ -260,7 +259,7 @@
     "\n",
     "output_prob = output['prob'][0]  # the output probability vector for the first image in the batch\n",
     "\n",
-    "print 'predicted class is:', output_prob.argmax()"
+    "print ('predicted class is:', output_prob.argmax())"
    ]
   },
   {
@@ -287,13 +286,14 @@
    ],
    "source": [
     "# load ImageNet labels\n",
-    "labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "import os \n",
+    "labels_file = '../' + 'data/ilsvrc12/synset_words.txt'\n",
     "if not os.path.exists(labels_file):\n",
     "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
     "    \n",
     "labels = np.loadtxt(labels_file, str, delimiter='\\t')\n",
     "\n",
-    "print 'output label:', labels[output_prob.argmax()]"
+    "print ('output label:', labels[output_prob.argmax()])"
    ]
   },
   {
@@ -336,8 +336,8 @@
     "# sort top five predictions from softmax output\n",
     "top_inds = output_prob.argsort()[::-1][:5]  # reverse sort and take five largest items\n",
     "\n",
-    "print 'probabilities and labels:'\n",
-    "zip(output_prob[top_inds], labels[top_inds])"
+    "print ('probabilities and labels:') \n",
+    "print( list(zip(output_prob[top_inds], labels[top_inds])))"
    ]
   },
   {
@@ -372,7 +372,7 @@
     }
    ],
    "source": [
-    "%timeit net.forward()"
+    "# %timeit net.forward()"
    ]
   },
   {
@@ -398,10 +398,10 @@
     }
    ],
    "source": [
-    "caffe.set_device(0)  # if we have multiple GPUs, pick the first one\n",
-    "caffe.set_mode_gpu()\n",
-    "net.forward()  # run once before timing to set up memory\n",
-    "%timeit net.forward()"
+    "# caffe.set_device(0)  # if we have multiple GPUs, pick the first one\n",
+    "# caffe.set_mode_gpu()\n",
+    "# net.forward()  # run once before timing to set up memory\n",
+    "# %timeit net.forward()"
    ]
   },
   {
@@ -457,8 +457,8 @@
    ],
    "source": [
     "# for each layer, show the output shape\n",
-    "for layer_name, blob in net.blobs.iteritems():\n",
-    "    print layer_name + '\\t' + str(blob.data.shape)"
+    "for layer_name, blob in net.blobs.items():\n",
+    "    print (layer_name + '\\t' + str(blob.data.shape))"
    ]
   },
   {
@@ -493,8 +493,8 @@
     }
    ],
    "source": [
-    "for layer_name, param in net.params.iteritems():\n",
-    "    print layer_name + '\\t' + str(param[0].data.shape), str(param[1].data.shape)"
+    "for layer_name, param in net.params.items():\n",
+    "    print (layer_name + '\\t' + str(param[0].data.shape), str(param[1].data.shape))"
    ]
   },
   {
@@ -727,9 +727,9 @@
    "outputs": [],
    "source": [
     "# download an image\n",
-    "my_image_url = \"...\"  # paste your URL here\n",
+    "# my_image_url = \"...\"  # paste your URL here\n",
     "# for example:\n",
-    "# my_image_url = \"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\"\n",
+    "my_image_url = \"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\"\n",
     "!wget -O image.jpg $my_image_url\n",
     "\n",
     "# transform it and copy it into the net\n",
@@ -747,8 +747,8 @@
     "\n",
     "plt.imshow(image)\n",
     "\n",
-    "print 'probabilities and labels:'\n",
-    "zip(output_prob[top_inds], labels[top_inds])"
+    "print ('probabilities and labels:') \n",
+    "print (list(zip(output_prob[top_inds], labels[top_inds])))"
    ]
   }
  ],