"""YOLO_v2 Model Defined in Keras."""
import sys
import numpy as np
import tensorflow as tf
import keras.backend as K
from keras.layers import Lambda
from keras.layers.merge import concatenate
from keras.models import Model
from ..utils import compose
from .keras_darknet19 import (DarknetConv2D, DarknetConv2D_BN_Leaky, darknet_body)
sys.path.append('..')
voc_anchors = np.array(
[[1.08, 1.19], [3.42, 4.41], [6.63, 11.38], [9.42, 5.11], [16.62, 10.52]])
voc_classes = [
"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
"chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
"pottedplant", "sheep", "sofa", "train", "tvmonitor"
]
def space_to_depth_x2(x):
"""Thin wrapper for Tensorflow space_to_depth with block_size=2."""
# Import currently required to make Lambda work.
# See: https://github.com/fchollet/keras/issues/5088#issuecomment-273851273
import tensorflow as tf
return tf.space_to_depth(x, block_size=2)
def space_to_depth_x2_output_shape(input_shape):
"""Determine space_to_depth output shape for block_size=2.
Note: For Lambda with TensorFlow backend, output shape may not be needed.
"""
return (input_shape[0], input_shape[1] // 2, input_shape[2] // 2, 4 *
input_shape[3]) if input_shape[1] else (input_shape[0], None, None,
4 * input_shape[3])
def yolo_body(inputs, num_anchors, num_classes):
"""Create YOLO_V2 model CNN body in Keras."""
darknet = Model(inputs, darknet_body()(inputs))
conv20 = compose(
DarknetConv2D_BN_Leaky(1024, (3, 3)),
DarknetConv2D_BN_Leaky(1024, (3, 3)))(darknet.output)
conv13 = darknet.layers[43].output
conv21 = DarknetConv2D_BN_Leaky(64, (1, 1))(conv13)
# TODO: Allow Keras Lambda to use func arguments for output_shape?
conv21_reshaped = Lambda(
space_to_depth_x2,
output_shape=space_to_depth_x2_output_shape,
name='space_to_depth')(conv21)
x = concatenate([conv21_reshaped, conv20])
x = DarknetConv2D_BN_Leaky(1024, (3, 3))(x)
x = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x)
return Model(inputs, x)
def yolo_head(feats, anchors, num_classes):
"""Convert final layer features to bounding box parameters.
Parameters
----------
feats : tensor
Final convolutional layer features.
anchors : array-like
Anchor box widths and heights.
num_classes : int
Number of target classes.
Returns
-------
box_xy : tensor
x, y box predictions adjusted by spatial location in conv layer.
box_wh : tensor
w, h box predictions adjusted by anchors and conv spatial resolution.
box_conf : tensor
Probability estimate for whether each box contains any object.
box_class_pred : tensor
Probability distribution estimate for each box over class labels.
"""
num_anchors = len(anchors)
# Reshape to batch, height, width, num_anchors, box_params.
anchors_tensor = K.reshape(K.variable(anchors), [1, 1, 1, num_anchors, 2])
# Static implementation for fixed models.
# TODO: Remove or add option for static implementation.
# _, conv_height, conv_width, _ = K.int_shape(feats)
# conv_dims = K.variable([conv_width, conv_height])
# Dynamic implementation of conv dims for fully convolutional model.
conv_dims = K.shape(feats)[1:3] # assuming channels last
# In YOLO the height index is the inner most iteration.
conv_height_index = K.arange(0, stop=conv_dims[0])
conv_width_index = K.arange(0, stop=conv_dims[1])
conv_height_index = K.tile(conv_height_index, [conv_dims[1]])
# TODO: Repeat_elements and tf.split doesn't support dynamic splits.
# conv_width_index = K.repeat_elements(conv_width_index, conv_dims[1], axis=0)
conv_width_index = K.tile(K.expand_dims(conv_width_index, 0), [conv_dims[0], 1])
conv_width_index = K.flatten(K.transpose(conv_width_index))
conv_index = K.transpose(K.stack([conv_height_index, conv_width_index]))
conv_index = K.reshape(conv_index, [1, conv_dims[0], conv_dims[1], 1, 2])
conv_index = K.cast(conv_index, K.dtype(feats))
feats = K.reshape(feats, [-1, conv_dims[0], conv_dims[1], num_anchors, num_classes + 5])
conv_dims = K.cast(K.reshape(conv_dims, [1, 1, 1, 1, 2]), K.dtype(feats))
# Static generation of conv_index:
# conv_index = np.array([_ for _ in np.ndindex(conv_width, conv_height)])
# conv_index = conv_index[:, [1, 0]] # swap columns for YOLO ordering.
# conv_index = K.variable(
# conv_index.reshape(1, conv_height, conv_width, 1, 2))
# feats = Reshape(
# (conv_dims[0], conv_dims[1], num_anchors, num_classes + 5))(feats)
box_confidence = K.sigmoid(feats[..., 4:5])
box_xy = K.sigmoid(feats[..., :2])
box_wh = K.exp(feats[..., 2:4])
box_class_probs = K.softmax(feats[..., 5:])
# Adjust preditions to each spatial grid point and anchor size.
# Note: YOLO iterates over height index before width index.
box_xy = (box_xy + conv_index) / conv_dims
box_wh = box_wh * anchors_tensor / conv_dims
return [box_confidence, box_xy, box_wh, box_class_probs]
def yolo_boxes_to_corners(box_xy, box_wh):
"""Convert YOLO box predictions to bounding box corners."""
box_mins = box_xy - (box_wh / 2.)
box_maxes = box_xy + (box_wh / 2.)
return K.concatenate([
box_mins[..., 1:2], # y_min
box_mins[..., 0:1], # x_min
box_maxes[..., 1:2], # y_max
box_maxes[..., 0:1] # x_max
])
def yolo_loss(args,
anchors,
num_classes,
rescore_confidence=False,
print_loss=False):
"""YOLO localization loss function.
Parameters
----------
yolo_output : tensor
Final convolutional layer features.
true_boxes : tensor
Ground truth boxes tensor with shape [batch, num_true_boxes, 5]
containing box x_center, y_center, width, height, and class.
detectors_mask : array
0/1 mask for detector positions where there is a matching ground truth.
matching_true_boxes : array
Corresponding ground truth boxes for positive detector positions.
Already adjusted for conv height and width.
anchors : tensor
Anchor boxes for model.
num_classes : int
Number of object classes.
rescore_confidence : bool, default=False
If true then set confidence target to IOU of best predicted box with
the closest matching ground truth box.
print_loss : bool, default=False
If True then use a tf.Print() to print the loss components.
Returns
-------
mean_loss : float
mean localization loss across minibatch
"""
(yolo_output, true_boxes, detectors_mask, matching_true_boxes) = args
num_anchors = len(anchors)
object_scale = 5
no_object_scale = 1
class_scale = 1
coordinates_scale = 1
pred_xy, pred_wh, pred_confidence, pred_class_prob = yolo_head(
yolo_output, anchors, num_classes)
# Unadjusted box predictions for loss.
# TODO: Remove extra computation shared with yolo_head.
yolo_output_shape = K.shape(yolo_output)
feats = K.reshape(yolo_output, [
-1, yolo_output_shape[1], yolo_output_shape[2], num_anchors,
num_classes + 5
])
pred_boxes = K.concatenate(
(K.sigmoid(feats[..., 0:2]), feats[..., 2:4]), axis=-1)
# TODO: Adjust predictions by image width/height for non-square images?
# IOUs may be off due to different aspect ratio.
# Expand pred x,y,w,h to allow comparison with ground truth.
# batch, conv_height, conv_width, num_anchors, num_true_boxes, box_params
pred_xy = K.expand_dims(pred_xy, 4)
pred_wh = K.expand_dims(pred_wh, 4)
pred_wh_half = pred_wh / 2.
pred_mins = pred_xy - pred_wh_half
pred_maxes = pred_xy + pred_wh_half
true_boxes_shape = K.shape(true_boxes)
# batch, conv_height, conv_width, num_anchors, num_true_boxes, box_params
true_boxes = K.reshape(true_boxes, [
true_boxes_shape[0], 1, 1, 1, true_boxes_shape[1], true_boxes_shape[2]
])
true_xy = true_boxes[..., 0:2]
true_wh = true_boxes[..., 2:4]
# Find IOU of each predicted box with each ground truth box.
true_wh_half = true_wh / 2.
true_mins = true_xy - true_wh_half
true_maxes = true_xy + true_wh_half
intersect_mins = K.maximum(pred_mins, true_mins)
intersect_maxes = K.minimum(pred_maxes, true_maxes)
intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
true_areas = true_wh[..., 0] * true_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = intersect_areas / union_areas
# Best IOUs for each location.
best_ious = K.max(iou_scores, axis=4) # Best IOU scores.
best_ious = K.expand_dims(best_ious)
# A detector has found an object if IOU > thresh for some true box.
object_detections = K.cast(best_ious > 0.6, K.dtype(best_ious))
# TODO: Darknet region training includes extra coordinate loss for early
# training steps to encourage predictions to match anchor priors.
# Determine confidence weights from object and no_object weights.
# NOTE: YOLO does not use binary cross-entropy here.
no_object_weights = (no_object_scale * (1 - object_detections) *
(1 - detectors_mask))
no_objects_loss = no_object_weights * K.square(-pred_confidence)
if rescore_confidence:
objects_loss = (object_scale * detectors_mask *
K.square(best_ious - pred_confidence))
else:
objects_loss = (object_scale * detectors_mask *
K.square(1 - pred_confidence))
confidence_loss = objects_loss + no_objects_loss
# Classification loss for matching detections.
# NOTE: YOLO does not use categorical cross-entropy loss here.
matching_classes = K.cast(matching_true_boxes[..., 4], 'int32')
matching_classes = K.one_hot(matching_classes, num_classes)
classification_loss = (class_scale * detectors_mask *
K.square(matching_classes - pred_class_prob))
# Coordinate loss for matching detection boxes.
matching_boxes = matching_true_boxes[..., 0:4]
coordinates_loss = (coordinates_scale * detectors_mask *
K.square(matching_boxes - pred_boxes))
confidence_loss_sum = K.sum(confidence_loss)
classification_loss_sum = K.sum(classification_loss)
coordinates_loss_sum = K.sum(coordinates_loss)
total_loss = 0.5 * (
confidence_loss_sum + classification_loss_sum + coordinates_loss_sum)
if print_loss:
total_loss = tf.Print(
total_loss, [
total_loss, confidence_loss_sum, classification_loss_sum,
coordinates_loss_sum
],
message='yolo_loss, conf_loss, class_loss, box_coord_loss:')
return total_loss
def yolo(inputs, anchors, num_classes):
"""Generate a complete YOLO_v2 localization model."""
num_anchors = len(anchors)
body = yolo_body(inputs, num_anchors, num_classes)
outputs = yolo_head(body.output, anchors, num_classes)
return outputs
def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold=.6):
"""Filter YOLO boxes based on object and class confidence."""
box_scores = box_confidence * box_class_probs
box_classes = K.argmax(box_scores, axis=-1)
box_class_scores = K.max(box_scores, axis=-1)
prediction_mask = box_class_scores >= threshold
# TODO: Expose tf.boolean_mask to Keras backend?
boxes = tf.boolean_mask(boxes, prediction_mask)
scores = tf.boolean_mask(box_class_scores, prediction_mask)
classes = tf.boolean_mask(box_classes, prediction_mask)
return boxes, scores, classes
def yolo_eval(yolo_outputs,
image_shape,
max_boxes=10,
score_threshold=.6,
iou_threshold=.5):
"""Evaluate YOLO model on given input batch and return filtered boxes."""
box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs
boxes = yolo_boxes_to_corners(box_xy, box_wh)
boxes, scores, classes = yolo_filter_boxes(
box_confidence, boxes, 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 preprocess_true_boxes(true_boxes, anchors, image_size):
"""Find detector in YOLO where ground truth box should appear.
Parameters
----------
true_boxes : array
List of ground truth boxes in form of relative x, y, w, h, class.
Relative coordinates are in the range [0, 1] indicating a percentage
of the original image dimensions.
anchors : array
List of anchors in form of w, h.
Anchors are assumed to be in the range [0, conv_size] where conv_size
is the spatial dimension of the final convolutional features.
image_size : array-like
List of image dimensions in form of h, w in pixels.
Returns
-------
detectors_mask : array
0/1 mask for detectors in [conv_height, conv_width, num_anchors, 1]
that should be compared with a matching ground truth box.
matching_true_boxes: array
Same shape as detectors_mask with the corresponding ground truth box
adjusted for comparison with predicted parameters at training time.
"""
height, width = image_size
num_anchors = len(anchors)
# Downsampling factor of 5x 2-stride max_pools == 32.
# TODO: Remove hardcoding of downscaling calculations.
assert height % 32 == 0, 'Image sizes in YOLO_v2 must be multiples of 32.'
assert width % 32 == 0, 'Image sizes in YOLO_v2 must be multiples of 32.'
conv_height = height // 32
conv_width = width // 32
num_box_params = true_boxes.shape[1]
detectors_mask = np.zeros(
(conv_height, conv_width, num_anchors, 1), dtype=np.float32)
matching_true_boxes = np.zeros(
(conv_height, conv_width, num_anchors, num_box_params),
dtype=np.float32)
for box in true_boxes:
# scale box to convolutional feature spatial dimensions
box_class = box[4:5]
box = box[0:4] * np.array(
[conv_width, conv_height, conv_width, conv_height])
i = np.floor(box[1]).astype('int')
j = min(np.floor(box[0]).astype('int'),1)
best_iou = 0
best_anchor = 0
for k, anchor in enumerate(anchors):
# Find IOU between box shifted to origin and anchor box.
box_maxes = box[2:4] / 2.
box_mins = -box_maxes
anchor_maxes = (anchor / 2.)
anchor_mins = -anchor_maxes
intersect_mins = np.maximum(box_mins, anchor_mins)
intersect_maxes = np.minimum(box_maxes, anchor_maxes)
intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.)
intersect_area = intersect_wh[0] * intersect_wh[1]
box_area = box[2] * box[3]
anchor_area = anchor[0] * anchor[1]
iou = intersect_area / (box_area + anchor_area - intersect_area)
if iou > best_iou:
best_iou = iou
best_anchor = k
if best_iou > 0:
detectors_mask[i, j, best_anchor] = 1
adjusted_box = np.array(
[
box[0] - j, box[1] - i,
np.log(box[2] / anchors[best_anchor][0]),
np.log(box[3] / anchors[best_anchor][1]), box_class
],
dtype=np.float32)
matching_true_boxes[i, j, best_anchor] = adjusted_box
return detectors_mask, matching_true_boxes
