Saqoosha · July 7, 2015 17:55 · Jul 7, 2015
diff --git a/dd.py b/dd.py
@@ -0,0 +1,88 @@
+import numpy as np
+import scipy.ndimage as nd
+import PIL.Image
+from google.protobuf import text_format
+import caffe
+
+model_path = 'caffe/models/finetune_flickr_style/' # substitute your path here
+net_fn   = model_path + 'deploy.prototxt'
+param_fn = model_path + 'finetune_flickr_style.caffemodel'
+
+# Patching model to be able to compute gradients.
+# Note that you can also manually add "force_backward: true" line to "deploy.prototxt".
+model = caffe.io.caffe_pb2.NetParameter()
+text_format.Merge(open(net_fn).read(), model)
+model.force_backward = True
+open('tmp.prototxt', 'w').write(str(model))
+
+net = caffe.Classifier('tmp.prototxt', param_fn,
+                       mean = np.float32([104.0, 116.0, 122.0]), # ImageNet mean, training set dependent
+                       channel_swap = (2,1,0)) # the reference model has channels in BGR order instead of RGB
+
+# a couple of utility functions for converting to and from Caffe's input image layout
+def preprocess(net, img):
+    return np.float32(np.rollaxis(img, 2)[::-1]) - net.transformer.mean['data']
+def deprocess(net, img):
+    return np.dstack((img + net.transformer.mean['data'])[::-1])
+
+def make_step(net, step_size=1.5, end='inception_4c/output', jitter=32, clip=True):
+    '''Basic gradient ascent step.'''
+
+    src = net.blobs['data'] # input image is stored in Net's 'data' blob
+    dst = net.blobs[end]
+
+    ox, oy = np.random.randint(-jitter, jitter+1, 2)
+    src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) # apply jitter shift
+
+    net.forward(end=end)
+    dst.diff[:] = dst.data  # specify the optimization objective
+    net.backward(start=end)
+    g = src.diff[0]
+    # apply normalized ascent step to the input image
+    src.data[:] += step_size/np.abs(g).mean() * g
+
+    src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) # unshift image
+
+    if clip:
+        bias = net.transformer.mean['data']
+        src.data[:] = np.clip(src.data, -bias, 255-bias)    
+
+def deepdream(net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, end='inception_4c/output', clip=True, **step_params):
+    # prepare base images for all octaves
+    octaves = [preprocess(net, base_img)]
+    for i in xrange(octave_n-1):
+        octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))
+
+    src = net.blobs['data']
+    detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details
+    for octave, octave_base in enumerate(octaves[::-1]):
+        h, w = octave_base.shape[-2:]
+        if octave > 0:
+            # upscale details from the previous octave
+            h1, w1 = detail.shape[-2:]
+            detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)
+
+        src.reshape(1,3,h,w) # resize the network's input image size
+        src.data[0] = octave_base+detail
+        for i in xrange(iter_n):
+            make_step(net, end=end, clip=clip, **step_params)
+
+            # visualization
+            vis = deprocess(net, src.data[0])
+            if not clip: # adjust image contrast if clipping is disabled
+                vis = vis*(255.0/np.percentile(vis, 99.98))
+            # showarray(vis)
+            print octave, i, end, vis.shape
+            # clear_output(wait=True)
+
+        # extract details produced on the current octave
+        detail = src.data[0]-octave_base
+    # returning the resulting image
+    return deprocess(net, src.data[0])
+
+# print net.blobs.keys()
+
+img = np.float32(PIL.Image.open('Saqoosha512.jpg'))
+for i in xrange(1000):
+  img = deepdream(net, img, 1, octave_n=4, end='pool5')
+  PIL.Image.fromarray(np.uint8(img)).save("saqoosha-%03d.png" % i)