def prepare_for_inference(self):
""" Sets up the AndOrRegion nodes, but also trains the K means. """
AndOrRegion.prepare_for_inference(self)
# Train the K means
self.kmeans_learners = []
for widx in range(len(self.windows)):
train_data = numpy.array(self.input_acts[widx], "float32")
kfl = KmeansFeatureLearner(self.K, train_data, max_iter=self.max_iter)
self.kmeans_learners.append(kfl)
def train(self):
""" Store a copy of every image in the iterator. """
Experiment.train(self)
start = time.time()
num_images = len(self.image_iterator)
print "Num images:", num_images
assert num_images > 0
gabor = self.gabor_region = GaborRegion((144, 192), rotations=3,
initial_wavelength=3,
num_wavelengths=2)
kmeans = KmeansRegion(max(1,int(float(num_images) * self.compression)),
self.window_sampler)
and_region = AndOrRegion(kmeans.image_shape, num_images)
# Regions = [ GaborRegion, Kmeans, AndRegion, OrRegion (classifier) ]
self.network = AndOrNetwork([gabor, kmeans, and_region])
classifier = self.network.get_classifier()
self.categories = []
print "Extracting windows..."
i = 0
while self.image_iterator.has_next():
image, category, img_idx = self.image_iterator.next()
self.categories.append(category)
gabor.do_inference(numpy.array(image))
kmeans.do_learning()
i += 1
print "Training K-means..."
kmeans.prepare_for_inference()
# Send all of the images through the feature learner to save the image
# activations.
print "Storing classifier features..."
self.image_iterator.reset()
while self.image_iterator.has_next():
image, category, img_idx = self.image_iterator.next()
gabor.do_inference(numpy.array(image))
kmeans.do_inference()
cxns = kmeans.get_active_nodes()
pos = and_region.create_node((0,0), cxns = cxns)
classifier.create_node(category, pos)
print "Preparing for inference..."
self.network.prepare_for_inference(2)
def __init__(self, K, window_sampler, max_iter=10):
self.K = K
self.max_iter = max_iter
self.windows = window_sampler.sample()
self.window_offsets = window_sampler.get_window_offsets()
self.feature_shape = window_sampler.feature_shape
self.window_sampler = window_sampler
# This is for learning
self.input_acts = [[] for i in range(len(self.windows))]
last_offset = self.window_offsets[-1]
image_shape = (last_offset[0] + 1, last_offset[1] + 1)
estimated_num_nodes = numpy.prod(image_shape) * K
AndOrRegion.__init__(self, image_shape, estimated_num_nodes)
self.init_Knodes()
