00-classification.ipynb
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# set up Python environment: numpy for numerical routines, and matplotlib for plottingimport numpy as npimport matplotlib.pyplot as plt# display plots in this notebook%matplotlib inline# set display defaultsplt.rcParams['figure.figsize'] = (10, 10) # large imagesplt.rcParams['image.interpolation'] = 'nearest' # don't interpolate: show square pixelsplt.rcParams['image.cmap'] = 'gray' # use grayscale output rather than a (potentially misleading) color heatmap
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# The caffe module needs to be on the Python path;# we'll add it here explicitly.import syscaffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)sys.path.insert(0, caffe_root + 'python')import caffe# If you get "No module named _caffe", either you have not built pycaffe or you have the wrong path.
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import osif os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'): print 'CaffeNet found.'else: print 'Downloading pre-trained CaffeNet model...' !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet
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caffe.set_mode_cpu()model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'net = caffe.Net(model_def, # defines the structure of the model model_weights, # contains the trained weights caffe.TEST) # use test mode (e.g., don't perform dropout)
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# load the mean ImageNet image (as distributed with Caffe) for subtractionmu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel valuesprint 'mean-subtracted values:', zip('BGR', mu)# create transformer for the input called 'data'transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimensiontransformer.set_mean('data', mu) # subtract the dataset-mean value in each channeltransformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]transformer.set_channel_swap('data', (2,1,0)) # swap channels from RGB to BGR
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# set the size of the input (we can skip this if we're happy# with the default; we can also change it later, e.g., for different batch sizes)net.blobs['data'].reshape(50, # batch size 3, # 3-channel (BGR) images 227, 227) # image size is 227x227
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image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')transformed_image = transformer.preprocess('data', image)plt.imshow(image)
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# copy the image data into the memory allocated for the netnet.blobs['data'].data[...] = transformed_image### perform classificationoutput = net.forward()output_prob = output['prob'][0] # the output probability vector for the first image in the batchprint 'predicted class is:', output_prob.argmax()
In [9]:
# load ImageNet labelslabels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'if not os.path.exists(labels_file): !../data/ilsvrc12/get_ilsvrc_aux.sh labels = np.loadtxt(labels_file, str, delimiter='\t')print 'output label:', labels[output_prob.argmax()]
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# sort top five predictions from softmax outputtop_inds = output_prob.argsort()[::-1][:5] # reverse sort and take five largest itemsprint 'probabilities and labels:'zip(output_prob[top_inds], labels[top_inds])
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In [11]:
%timeit net.forward()
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caffe.set_device(0) # if we have multiple GPUs, pick the first onecaffe.set_mode_gpu()net.forward() # run once before timing to set up memory%timeit net.forward()
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# for each layer, show the output shapefor layer_name, blob in net.blobs.iteritems(): print layer_name + '\t' + str(blob.data.shape)
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for layer_name, param in net.params.iteritems(): print layer_name + '\t' + str(param[0].data.shape), str(param[1].data.shape)
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def vis_square(data): """Take an array of shape (n, height, width) or (n, height, width, 3) and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)""" # normalize data for display data = (data - data.min()) / (data.max() - data.min()) # force the number of filters to be square n = int(np.ceil(np.sqrt(data.shape[0]))) padding = (((0, n ** 2 - data.shape[0]), (0, 1), (0, 1)) # add some space between filters + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one) data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white) # tile the filters into an image data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1))) data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:]) plt.imshow(data); plt.axis('off')
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# the parameters are a list of [weights, biases]filters = net.params['conv1'][0].datavis_square(filters.transpose(0, 2, 3, 1))
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feat = net.blobs['conv1'].data[0, :36]vis_square(feat)
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feat = net.blobs['pool5'].data[0]vis_square(feat)
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feat = net.blobs['fc6'].data[0]plt.subplot(2, 1, 1)plt.plot(feat.flat)plt.subplot(2, 1, 2)_ = plt.hist(feat.flat[feat.flat > 0], bins=100)
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feat = net.blobs['prob'].data[0]plt.figure(figsize=(15, 3))plt.plot(feat.flat)
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In [ ]:
# download an imagemy_image_url = "..." # paste your URL here# for example:# my_image_url = "https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG"!wget -O image.jpg $my_image_url# transform it and copy it into the netimage = caffe.io.load_image('image.jpg')net.blobs['data'].data[...] = transformer.preprocess('data', image)# perform classificationnet.forward()# obtain the output probabilitiesoutput_prob = net.blobs['prob'].data[0]# sort top five predictions from softmax outputtop_inds = output_prob.argsort()[::-1][:5]plt.imshow(image)print 'probabilities and labels:'zip(output_prob[top_inds], labels[top_inds])
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