Added demo code. Downloads model automatically

README.md

@@ -1,7 +1,3 @@
-# guided_perturbations
+Demo code for Guided Perturbations accompanying the paper:
 
-Code accompanying the paper:
-
-*Guided Perturbations: Self Corrective Behavior in Convolutional Neural Networks*, Swami Sankaranarayanan, Arpit Jain, and Ser Nam Lim.  International Conference on Computer Vision (ICCV) 2017.
-
-Code coming soon!
+Guided Perturbations: Self-Corrective for Semantic Segmentation and Classification, Swami Sankaranarayanan, Arpit Jain and Ser Nam Lim, Accepted to ICCV 2017.

demo.py

@@ -0,0 +1,142 @@
+import os,sys
+import caffe
+caffe.set_mode_gpu()
+from PIL import Image
+from datetime import datetime
+import numpy as np
+import timeit
+from timeit import default_timer as timer
+import matplotlib.pyplot as plt
+import copy
+import surgery
+import argparse
+
+
+def get_palette(num_cls):
+    """ Returns the color map for visualizing the segmentation mask.
+    Args:
+        num_cls: Number of classes
+    Returns:
+        The color map
+    """
+
+    n = num_cls
+    palette = [0] * (n * 3)
+    for j in xrange(0, n):
+        lab = j
+        palette[j * 3 + 0] = 0
+        palette[j * 3 + 1] = 0
+        palette[j * 3 + 2] = 0
+        i = 0
+        while lab:
+            palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
+            palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
+            palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
+            i += 1
+            lab >>= 3
+    return palette
+
+def colorize(label,palette):
+    out = Image.fromarray(label)
+    out.putpalette(palette)
+    return out
+
+
+def preprocess(im):
+    mean = (104.00698793, 116.66876762, 122.67891434) 
+    in_ = np.array(copy.deepcopy(im), dtype=np.float32)
+    in_ = in_[:,:,::-1]
+    in_ -= mean
+    in_ = in_.transpose((2,0,1))
+    return in_[np.newaxis,...]
+
+
+def main():
+
+    palette = get_palette(256)
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i','--input_file',default='input.png', help='Input image')
+    parser.add_argument('-e','--eps', type=float,default=0.55, help='Epsilon value')
+    parser.add_argument('-w','--model_weights',required=True, help='Model weights')
+    parser.add_argument('-p','--model_proto',required=True,help='Model prototxt file')
+    parser.add_argument('-t','--type',default='same',help='Type of output modifier (one-hot or same) ')
+    parser.add_argument('-o','--outfile', default='output.png')
+    parser.add_argument('-g', '--gpu', type=int, default=0)
+    args = parser.parse_args()
+
+
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
+    caffe.set_device(args.gpu)
+
+    net=caffe.Net(args.model_proto,args.model_weights,caffe.TEST)
+    # surgeries
+    interp_layers = [k for k in net.params.keys() if 'up' in k]
+    surgery.interp(net, interp_layers)
+
+    mean = (104.00698793, 116.66876762, 122.67891434) 
+    epsilon = args.eps
+
+    img = Image.open(args.input_file)
+    in_batch = preprocess(img)
+
+
+
+    net.blobs['data'].reshape(*in_batch.shape)
+    net.blobs['data'].data[...] = in_batch
+    net.forward()                
+    out = copy.deepcopy(net.blobs[net.outputs[0]].data[0])
+    out_argmax = out.argmax(0)
+
+
+
+    #Modify output to backprop gradient based on network output
+    sh = out.squeeze().shape
+    conf_softmax = np.zeros((1,sh[0],sh[1],sh[2]))        
+    if args.type == 'one-hot':
+        for i in np.arange(sh[0]):
+            for j in np.arange(sh[1]):                                                    
+                conf_softmax[0,out_argmax[i,j],i,j]=1.0  
+    elif args.type== 'same':
+        conf_softmax = copy.deepcopy(out[None,:,:,:])
+
+
+
+    sh = net.blobs['data'].diff[...].shape
+    net.blobs['data'].diff[...] = np.zeros(sh)
+
+
+
+    #Perform backward pass - no updates happen here
+    net.backward(**{net.outputs[0]: conf_softmax})
+
+
+    #Read of the diff from data layer - this is the perturbation
+    perturbed_data = (float(args.eps)*np.sign(net.blobs['data'].diff[...])+ in_batch)
+
+
+    #Run the forward pass using the perturbed_input
+    net.blobs['data'].reshape(*perturbed_data.shape)
+    net.blobs['data'].data[...] = perturbed_data
+    net.forward()
+
+    #Obtain the final output
+    out_gp = net.blobs[net.outputs[0]].data[0]
+    out_gp_argmax = out_gp.argmax(0)
+
+
+    #Colorize the label outputs and write to file
+    out_color = colorize(out_argmax.astype(np.uint8),palette)
+    out_color_gp = colorize(out_gp_argmax.astype(np.uint8),palette)
+    w,h = out_color.size
+    out_comb = Image.new('RGB',(3*w,h))
+
+    out_comb.paste(img,(0,0))
+    out_comb.paste(out_color,(w,0))
+    out_comb.paste(out_color_gp,(2*w,0))
+    out_comb.save(args.outfile)
+
+
+if __name__ == '__main__':
+    main()
+