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#!/usr/bin/env python
# coding: utf-8
import numpy as np
import math
import tensorflow.keras.backend as K
import tensorflow as tf
def xywh_to_x1y1x2y2(boxes):
return tf.concat([boxes[..., :2] - boxes[..., 2:] * 0.5, boxes[..., :2] + boxes[..., 2:] * 0.5], axis=-1)
# x,y,w,h
def bbox_iou(boxes1, boxes2):
boxes1_area = boxes1[..., 2] * boxes1[..., 3] # w * h
boxes2_area = boxes2[..., 2] * boxes2[..., 3]
# (x, y, w, h) -> (x0, y0, x1, y1)
boxes1 = xywh_to_x1y1x2y2(boxes1)
boxes2 = xywh_to_x1y1x2y2(boxes2)
# coordinates of intersection
top_left = tf.maximum(boxes1[..., :2], boxes2[..., :2])
bottom_right = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])
intersection_xy = tf.maximum(bottom_right - top_left, 0.0)
intersection_area = intersection_xy[..., 0] * intersection_xy[..., 1]
union_area = boxes1_area + boxes2_area - intersection_area
return 1.0 * intersection_area / (union_area + tf.keras.backend.epsilon())
def bbox_giou(boxes1, boxes2):
boxes1_area = boxes1[..., 2] * boxes1[..., 3] # w*h
boxes2_area = boxes2[..., 2] * boxes2[..., 3]
# (x, y, w, h) -> (x0, y0, x1, y1)
boxes1 = xywh_to_x1y1x2y2(boxes1)
boxes2 = xywh_to_x1y1x2y2(boxes2)
top_left = tf.maximum(boxes1[..., :2], boxes2[..., :2])
bottom_right = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])
intersection_xy = tf.maximum(bottom_right - top_left, 0.0)
intersection_area = intersection_xy[..., 0] * intersection_xy[..., 1]
union_area = boxes1_area + boxes2_area - intersection_area
iou = 1.0 * intersection_area / (union_area + tf.keras.backend.epsilon())
enclose_top_left = tf.minimum(boxes1[..., :2], boxes2[..., :2])
enclose_bottom_right = tf.maximum(boxes1[..., 2:], boxes2[..., 2:])
enclose_xy = enclose_bottom_right - enclose_top_left
enclose_area = enclose_xy[..., 0] * enclose_xy[..., 1]
giou = iou - tf.math.divide_no_nan(enclose_area - union_area, enclose_area)
return giou
def bbox_ciou(boxes1, boxes2):
'''
ciou = iou - p2/c2 - av
:param boxes1: (8, 13, 13, 3, 4) pred_xywh
:param boxes2: (8, 13, 13, 3, 4) label_xywh
:return:
'''
boxes1_x0y0x1y1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
boxes2_x0y0x1y1 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
boxes1_x0y0x1y1 = tf.concat([tf.minimum(boxes1_x0y0x1y1[..., :2], boxes1_x0y0x1y1[..., 2:]),
tf.maximum(boxes1_x0y0x1y1[..., :2], boxes1_x0y0x1y1[..., 2:])], axis=-1)
boxes2_x0y0x1y1 = tf.concat([tf.minimum(boxes2_x0y0x1y1[..., :2], boxes2_x0y0x1y1[..., 2:]),
tf.maximum(boxes2_x0y0x1y1[..., :2], boxes2_x0y0x1y1[..., 2:])], axis=-1)
# area
boxes1_area = (boxes1_x0y0x1y1[..., 2] - boxes1_x0y0x1y1[..., 0]) * (
boxes1_x0y0x1y1[..., 3] - boxes1_x0y0x1y1[..., 1])
boxes2_area = (boxes2_x0y0x1y1[..., 2] - boxes2_x0y0x1y1[..., 0]) * (
boxes2_x0y0x1y1[..., 3] - boxes2_x0y0x1y1[..., 1])
# top-left and bottom-right coord, shape: (8, 13, 13, 3, 2)
left_up = tf.maximum(boxes1_x0y0x1y1[..., :2], boxes2_x0y0x1y1[..., :2])
right_down = tf.minimum(boxes1_x0y0x1y1[..., 2:], boxes2_x0y0x1y1[..., 2:])
# intersection area and iou
inter_section = tf.maximum(right_down - left_up, 0.0)
inter_area = inter_section[..., 0] * inter_section[..., 1]
union_area = boxes1_area + boxes2_area - inter_area
iou = inter_area / (union_area + 1e-9)
# top-left and bottom-right coord of the enclosing rectangle, shape: (8, 13, 13, 3, 2)
enclose_left_up = tf.minimum(boxes1_x0y0x1y1[..., :2], boxes2_x0y0x1y1[..., :2])
enclose_right_down = tf.maximum(boxes1_x0y0x1y1[..., 2:], boxes2_x0y0x1y1[..., 2:])
# diagnal ** 2
enclose_wh = enclose_right_down - enclose_left_up
enclose_c2 = K.pow(enclose_wh[..., 0], 2) + K.pow(enclose_wh[..., 1], 2)
# center distances between two rectangles
p2 = K.pow(boxes1[..., 0] - boxes2[..., 0], 2) + K.pow(boxes1[..., 1] - boxes2[..., 1], 2)
# add av
atan1 = tf.atan(boxes1[..., 2] / (boxes1[..., 3] + 1e-9))
atan2 = tf.atan(boxes2[..., 2] / (boxes2[..., 3] + 1e-9))
v = 4.0 * K.pow(atan1 - atan2, 2) / (math.pi ** 2)
a = v / (1 - iou + v)
ciou = iou - 1.0 * p2 / enclose_c2 - 1.0 * a * v
return ciou
def yolo_loss(args, num_classes, iou_loss_thresh, anchors):
conv_lbbox = args[2] # (?, ?, ?, 3*(num_classes+5))
conv_mbbox = args[1] # (?, ?, ?, 3*(num_classes+5))
conv_sbbox = args[0] # (?, ?, ?, 3*(num_classes+5))
label_sbbox = args[3] # (?, ?, ?, 3, num_classes+5)
label_mbbox = args[4] # (?, ?, ?, 3, num_classes+5)
label_lbbox = args[5] # (?, ?, ?, 3, num_classes+5)
true_bboxes = args[6] # (?, 50, 4)
pred_sbbox = decode(conv_sbbox, anchors[0], 8, num_classes)
pred_mbbox = decode(conv_mbbox, anchors[1], 16, num_classes)
pred_lbbox = decode(conv_lbbox, anchors[2], 32, num_classes)
sbbox_ciou_loss, sbbox_conf_loss, sbbox_prob_loss = loss_layer(conv_sbbox, pred_sbbox, label_sbbox, true_bboxes, 8, num_classes, iou_loss_thresh)
mbbox_ciou_loss, mbbox_conf_loss, mbbox_prob_loss = loss_layer(conv_mbbox, pred_mbbox, label_mbbox, true_bboxes, 16, num_classes, iou_loss_thresh)
lbbox_ciou_loss, lbbox_conf_loss, lbbox_prob_loss = loss_layer(conv_lbbox, pred_lbbox, label_lbbox, true_bboxes, 32, num_classes, iou_loss_thresh)
ciou_loss = (lbbox_ciou_loss + sbbox_ciou_loss + mbbox_ciou_loss) * 3.54
conf_loss = (lbbox_conf_loss + sbbox_conf_loss + mbbox_conf_loss) * 64.3
prob_loss = (lbbox_prob_loss + sbbox_prob_loss + mbbox_prob_loss) * 1
return ciou_loss+conf_loss+prob_loss
def loss_layer(conv, pred, label, bboxes, stride, num_class, iou_loss_thresh):
conv_shape = tf.shape(conv)
batch_size = conv_shape[0]
output_size = conv_shape[1]
input_size = stride * output_size
conv = tf.reshape(conv, (batch_size, output_size, output_size,
3, 5 + num_class))
conv_raw_prob = conv[:, :, :, :, 5:]
conv_raw_conf = conv[:, :, :, :, 4:5]
pred_xywh = pred[:, :, :, :, 0:4]
pred_conf = pred[:, :, :, :, 4:5]
label_xywh = label[:, :, :, :, 0:4]
respond_bbox = label[:, :, :, :, 4:5]
label_prob = label[:, :, :, :, 5:]
# Coordinate loss
ciou = tf.expand_dims(bbox_giou(pred_xywh, label_xywh), axis=-1) # (8, 13, 13, 3, 1)
# ciou = tf.expand_dims(bbox_ciou(pred_xywh, label_xywh), axis=-1) # (8, 13, 13, 3, 1)
input_size = tf.cast(input_size, tf.float32)
# loss weight of the gt bbox: 2-(gt area/img area)
bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2)
ciou_loss = respond_bbox * bbox_loss_scale * (1 - ciou) # iou loss for respond bbox
# Classification loss for respond bbox
prob_loss = respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_prob, logits=conv_raw_prob)
expand_pred_xywh = pred_xywh[:, :, :, :, np.newaxis, :] # (?, grid_h, grid_w, 3, 1, 4)
expand_bboxes = bboxes[:, np.newaxis, np.newaxis, np.newaxis, :, :] # (?, 1, 1, 1, 70, 4)
iou = bbox_iou(expand_pred_xywh, expand_bboxes) # IoU between all pred bbox and all gt (?, grid_h, grid_w, 3, 70)
max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1), axis=-1) # max iou: (?, grid_h, grid_w, 3, 1)
# ignore the bbox which is not respond bbox and max iou < threshold
respond_bgd = (1.0 - respond_bbox) * tf.cast(max_iou < iou_loss_thresh, tf.float32)
# Confidence loss
conf_focal = tf.pow(respond_bbox - pred_conf, 2)
conf_loss = conf_focal * (
respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)
+
respond_bgd * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)
)
ciou_loss = tf.reduce_mean(tf.reduce_sum(ciou_loss, axis=[1, 2, 3, 4]))
conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1, 2, 3, 4]))
prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1, 2, 3, 4]))
return ciou_loss, conf_loss, prob_loss
def decode(conv_output, anchors, stride, num_class):
conv_shape = tf.shape(conv_output)
batch_size = conv_shape[0]
output_size = conv_shape[1]
anchor_per_scale = len(anchors)
conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, anchor_per_scale, 5 + num_class))
conv_raw_dxdy = conv_output[:, :, :, :, 0:2]
conv_raw_dwdh = conv_output[:, :, :, :, 2:4]
conv_raw_conf = conv_output[:, :, :, :, 4:5]
conv_raw_prob = conv_output[:, :, :, :, 5:]
y = tf.tile(tf.range(output_size, dtype=tf.int32)[:, tf.newaxis], [1, output_size])
x = tf.tile(tf.range(output_size, dtype=tf.int32)[tf.newaxis, :], [output_size, 1])
xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1)
xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :], [batch_size, 1, 1, anchor_per_scale, 1])
xy_grid = tf.cast(xy_grid, tf.float32)
pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * stride
pred_wh = (tf.exp(conv_raw_dwdh) * anchors)
pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)
pred_conf = tf.sigmoid(conv_raw_conf)
pred_prob = tf.sigmoid(conv_raw_prob)
return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1)
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