updated reamde

Author: experiencor

gen_anchors.py

@@ -0,0 +1,141 @@
+import random
+import argparse
+import numpy as np
+
+from preprocessing import parse_annotation
+import json
+
+argparser = argparse.ArgumentParser()
+
+argparser.add_argument(
+    '-c',
+    '--conf',
+    default='config.json',
+    help='path to configuration file')
+
+argparser.add_argument(
+    '-a',
+    '--anchors',
+    default=5,
+    help='number of anchors to use')
+
+def IOU(ann, centroids):
+    w, h = ann
+    similarities = []
+
+    for centroid in centroids:
+        c_w, c_h = centroid
+
+        if c_w >= w and c_h >= h:
+            similarity = w*h/(c_w*c_h)
+        elif c_w >= w and c_h <= h:
+            similarity = w*c_h/(w*h + (c_w-w)*c_h)
+        elif c_w <= w and c_h >= h:
+            similarity = c_w*h/(w*h + c_w*(c_h-h))
+        else: #means both w,h are bigger than c_w and c_h respectively
+            similarity = (c_w*c_h)/(w*h)
+        similarities.append(similarity) # will become (k,) shape
+
+    return np.array(similarities)
+
+def avg_IOU(anns, centroids):
+    n,d = anns.shape
+    sum = 0.
+
+    for i in range(anns.shape[0]):
+        sum+= max(IOU(anns[i], centroids))
+
+    return sum/n
+
+def print_anchors(centroids):
+    anchors = centroids.copy()
+
+    widths = anchors[:, 0]
+    sorted_indices = np.argsort(widths)
+
+    r = "anchors: ["
+    for i in sorted_indices[:-1]:
+        r += '%0.2f,%0.2f, ' % (anchors[i,0], anchors[i,1])
+
+    #there should not be comma after last anchor, that's why
+    r += '%0.2f,%0.2f' % (anchors[sorted_indices[-1:],0], anchors[sorted_indices[-1:],1])
+    r += "]"
+
+    print r
+
+def run_kmeans(ann_dims, anchor_num):
+    ann_num = ann_dims.shape[0]
+    iterations = 0
+    prev_assignments = np.ones(ann_num)*(-1)
+    iteration = 0
+    old_distances = np.zeros((ann_num, anchor_num))
+
+    indices = [random.randrange(ann_dims.shape[0]) for i in range(anchor_num)]
+    centroids = ann_dims[indices]
+    anchor_dim = ann_dims.shape[1]
+
+    while True:
+        distances = []
+        iteration += 1
+        for i in range(ann_num):
+            d = 1 - IOU(ann_dims[i], centroids)
+            distances.append(d)
+        distances = np.array(distances) # distances.shape = (ann_num, anchor_num)
+
+        print "iteration {}: dists = {}".format(iteration, np.sum(np.abs(old_distances-distances)))
+
+        #assign samples to centroids
+        assignments = np.argmin(distances,axis=1)
+
+        if (assignments == prev_assignments).all() :
+            return centroids
+
+        #calculate new centroids
+        centroid_sums=np.zeros((anchor_num, anchor_dim), np.float)
+        for i in range(ann_num):
+            centroid_sums[assignments[i]]+=ann_dims[i]
+        for j in range(anchor_num):
+<<<<<<< HEAD
+            centroids[j] = centroid_sums[j]/(np.sum(assignments==j) + 1e-6)
+=======
+            centroids[j] = centroid_sums[j]/(np.sum(assignments==j))
+>>>>>>> 4843db4a6c199941d8e4e57bdd86af7d33882c61
+
+        prev_assignments = assignments.copy()
+        old_distances = distances.copy()
+
+def main(argv):
+    config_path = args.conf
+    num_anchors = args.anchors
+
+    with open(config_path) as config_buffer:
+        config = json.loads(config_buffer.read())
+
+    train_imgs, train_labels = parse_annotation(config['train']['train_annot_folder'],
+                                                config['train']['train_image_folder'],
+                                                config['model']['labels'])
+
+    grid_w = config['model']['input_size']/32
+    grid_h = config['model']['input_size']/32
+
+    # run k_mean to find the anchors
+    annotation_dims = []
+    for image in train_imgs:
+        cell_w = image['width']/grid_w
+        cell_h = image['height']/grid_h
+
+        for obj in image['object']:
+            relative_w = (float(obj['xmax']) - float(obj['xmin']))/cell_w
+            relatice_h = (float(obj["ymax"]) - float(obj['ymin']))/cell_h
+            annotation_dims.append(map(float, (relative_w,relatice_h)))
+    annotation_dims = np.array(annotation_dims)
+
+    centroids = run_kmeans(annotation_dims, num_anchors)
+
+    # write anchors to file
+    print '\naverage IOU for', num_anchors, 'anchors:', '%0.2f' % avg_IOU(annotation_dims, centroids)
+    print_anchors(centroids)
+
+if __name__ == '__main__':
+    args = argparser.parse_args()
+    main(args)