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
