def yolo_eval(graph, yolo_outputs, image_shape, max_boxes=10, score_threshold=.6, iou_threshold=.5): with graph.as_default(): box_xy, box_wh, box_confidence, box_class_probs = yolo_outputs boxes = yolo_boxes_to_corners(graph, box_xy, box_wh) boxes, scores, classes = yolo_filter_boxes(graph, boxes, box_confidence, box_class_probs, threshold=score_threshold) # Scale boxes back to original image shape. height = image_shape[0] width = image_shape[1] image_dims = K.stack([height, width, height, width]) image_dims = K.reshape(image_dims, [1, 4]) boxes = boxes * image_dims # TODO: Something must be done about this ugly hack! max_boxes_tensor = K.variable(max_boxes, dtype='int32') K.get_session().run(tf.variables_initializer([max_boxes_tensor])) nms_index = tf.image.non_max_suppression(boxes, scores, max_boxes_tensor, iou_threshold=iou_threshold) boxes = K.gather(boxes, nms_index) scores = K.gather(scores, nms_index) classes = K.gather(classes, nms_index) return boxes, scores, classes
def yolo_eval(yolo_outputs, anchors, num_classes, image_shape, max_boxes=20, score_threshold=.6, iou_threshold=.5): """Evaluate YOLO model on given input and return filtered boxes.""" num_layers = len(yolo_outputs) anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] if num_layers == 3 else [[ 3, 4, 5 ], [1, 2, 3]] # default setting input_shape = K.shape(yolo_outputs[0])[1:3] * 32 # with tf.Session() as sess: # print(sess.run(input_shape[1])) boxes = [] box_scores = [] for l in range(num_layers): _boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l], anchors[anchor_mask[l]], num_classes, input_shape, image_shape) boxes.append(_boxes) box_scores.append(_box_scores) boxes = K.concatenate(boxes, axis=0) box_scores = K.concatenate(box_scores, axis=0) mask = box_scores >= score_threshold max_boxes_tensor = K.constant(max_boxes, dtype='int32') boxes_ = [] scores_ = [] classes_ = [] for c in range(num_classes): # TODO: use keras backend instead of tf. class_boxes = tf.boolean_mask(boxes, mask[:, c]) class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c]) nms_index = tf.image.non_max_suppression(class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=iou_threshold) class_boxes = K.gather(class_boxes, nms_index) class_box_scores = K.gather(class_box_scores, nms_index) classes = K.ones_like(class_box_scores, 'int32') * c boxes_.append(class_boxes) scores_.append(class_box_scores) classes_.append(classes) boxes_ = K.concatenate(boxes_, axis=0) scores_ = K.concatenate(scores_, axis=0) classes_ = K.concatenate(classes_, axis=0) return boxes_, scores_, classes_
def test_gather(self): shape = (10, 2, 3) ref = np.arange(np.prod(shape)).reshape(shape) ref_th = KTH.variable(ref) ref_tf = KTF.variable(ref) inds = [1, 3, 7, 9] inds_th = KTH.variable(inds, dtype='int32') inds_tf = KTF.variable(inds, dtype='int32') th_z = KTH.gather(ref_th, inds_th) th_result = KTH.eval(th_z) tf_result = KTF.eval(KTF.gather(ref_tf, inds_tf)) assert_allclose(tf_result, th_result, atol=1e-05) if hasattr(th_z, '_keras_shape'): assert th_z._keras_shape == th_result.shape # test theano shape inference when # input shape has None entries if K.backend() == 'theano': x = K.placeholder(shape=(None, 3, 4)) indices = K.placeholder(shape=(5, 6), dtype='int32') y = K.gather(x, indices) assert y._keras_shape == (5, 6, 3, 4)
def test_gather(self): shape = (10, 2, 3) ref = np.arange(np.prod(shape)).reshape(shape) ref_th = KTH.variable(ref) ref_tf = KTF.variable(ref) inds = [1, 3, 7, 9] inds_th = KTH.variable(inds, dtype='int32') inds_tf = KTF.variable(inds, dtype='int32') th_z = KTH.gather(ref_th, inds_th) th_result = KTH.eval(th_z) tf_result = KTF.eval(KTF.gather(ref_tf, inds_tf)) assert_allclose(tf_result, th_result, atol=1e-05) if hasattr(th_z, '_keras_shape'): assert th_z._keras_shape == th_result.shape # test theano shape inference when # input shape has None entries if K.backend() == 'theano': x = K.placeholder(shape=(None, 3, 4)) indices = K.placeholder(shape=(5, 6), dtype='int32') y = K.gather(x, indices) assert y._keras_shape == (5, 6, 3, 4)
def test_gather(self): shape = (10, 2, 3) ref = np.arange(np.prod(shape)).reshape(shape) ref_th = KTH.variable(ref) ref_tf = KTF.variable(ref) inds = [1, 3, 7, 9] inds_th = KTH.variable(inds, dtype='int32') inds_tf = KTF.variable(inds, dtype='int32') th_z = KTH.gather(ref_th, inds_th) th_result = KTH.eval(th_z) tf_result = KTF.eval(KTF.gather(ref_tf, inds_tf)) assert_allclose(tf_result, th_result, atol=1e-05) if hasattr(th_z, '_keras_shape'): assert th_z._keras_shape == th_result.shape