yolov3网络结构笔记
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yolov3网络结构笔记
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從yolo3_darknet53_coco(gluoncv/model_zoo/yolo/yolo3.py?)調試獲得，供參考
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--------------------------------------------------------------------------------Layer (type) Output Shape Param #
================================================================================Input (1, 3, 512, 703) 0Conv2D-1 (1, 32, 512, 703) 864BatchNorm-2 (1, 32, 512, 703) 128LeakyReLU-3 (1, 32, 512, 703) 0Conv2D-4 (1, 64, 256, 352) 18432BatchNorm-5 (1, 64, 256, 352) 256LeakyReLU-6 (1, 64, 256, 352) 0Conv2D-7 (1, 32, 256, 352) 2048BatchNorm-8 (1, 32, 256, 352) 128LeakyReLU-9 (1, 32, 256, 352) 0Conv2D-10 (1, 64, 256, 352) 18432BatchNorm-11 (1, 64, 256, 352) 256LeakyReLU-12 (1, 64, 256, 352) 0
DarknetBasicBlockV3-13 (1, 64, 256, 352) 0Conv2D-14 (1, 128, 128, 176) 73728BatchNorm-15 (1, 128, 128, 176) 512LeakyReLU-16 (1, 128, 128, 176) 0Conv2D-17 (1, 64, 128, 176) 8192BatchNorm-18 (1, 64, 128, 176) 256LeakyReLU-19 (1, 64, 128, 176) 0Conv2D-20 (1, 128, 128, 176) 73728BatchNorm-21 (1, 128, 128, 176) 512LeakyReLU-22 (1, 128, 128, 176) 0
DarknetBasicBlockV3-23 (1, 128, 128, 176) 0Conv2D-24 (1, 64, 128, 176) 8192BatchNorm-25 (1, 64, 128, 176) 256LeakyReLU-26 (1, 64, 128, 176) 0Conv2D-27 (1, 128, 128, 176) 73728BatchNorm-28 (1, 128, 128, 176) 512LeakyReLU-29 (1, 128, 128, 176) 0
DarknetBasicBlockV3-30 (1, 128, 128, 176) 0Conv2D-31 (1, 256, 64, 88) 294912BatchNorm-32 (1, 256, 64, 88) 1024LeakyReLU-33 (1, 256, 64, 88) 0Conv2D-34 (1, 128, 64, 88) 32768BatchNorm-35 (1, 128, 64, 88) 512LeakyReLU-36 (1, 128, 64, 88) 0Conv2D-37 (1, 256, 64, 88) 294912BatchNorm-38 (1, 256, 64, 88) 1024LeakyReLU-39 (1, 256, 64, 88) 0
DarknetBasicBlockV3-40 (1, 256, 64, 88) 0Conv2D-41 (1, 128, 64, 88) 32768BatchNorm-42 (1, 128, 64, 88) 512LeakyReLU-43 (1, 128, 64, 88) 0Conv2D-44 (1, 256, 64, 88) 294912BatchNorm-45 (1, 256, 64, 88) 1024LeakyReLU-46 (1, 256, 64, 88) 0
DarknetBasicBlockV3-47 (1, 256, 64, 88) 0Conv2D-48 (1, 128, 64, 88) 32768BatchNorm-49 (1, 128, 64, 88) 512LeakyReLU-50 (1, 128, 64, 88) 0Conv2D-51 (1, 256, 64, 88) 294912BatchNorm-52 (1, 256, 64, 88) 1024LeakyReLU-53 (1, 256, 64, 88) 0
DarknetBasicBlockV3-54 (1, 256, 64, 88) 0Conv2D-55 (1, 128, 64, 88) 32768BatchNorm-56 (1, 128, 64, 88) 512LeakyReLU-57 (1, 128, 64, 88) 0Conv2D-58 (1, 256, 64, 88) 294912BatchNorm-59 (1, 256, 64, 88) 1024LeakyReLU-60 (1, 256, 64, 88) 0
DarknetBasicBlockV3-61 (1, 256, 64, 88) 0Conv2D-62 (1, 128, 64, 88) 32768BatchNorm-63 (1, 128, 64, 88) 512LeakyReLU-64 (1, 128, 64, 88) 0Conv2D-65 (1, 256, 64, 88) 294912BatchNorm-66 (1, 256, 64, 88) 1024LeakyReLU-67 (1, 256, 64, 88) 0
DarknetBasicBlockV3-68 (1, 256, 64, 88) 0Conv2D-69 (1, 128, 64, 88) 32768BatchNorm-70 (1, 128, 64, 88) 512LeakyReLU-71 (1, 128, 64, 88) 0Conv2D-72 (1, 256, 64, 88) 294912BatchNorm-73 (1, 256, 64, 88) 1024LeakyReLU-74 (1, 256, 64, 88) 0
DarknetBasicBlockV3-75 (1, 256, 64, 88) 0Conv2D-76 (1, 128, 64, 88) 32768BatchNorm-77 (1, 128, 64, 88) 512LeakyReLU-78 (1, 128, 64, 88) 0Conv2D-79 (1, 256, 64, 88) 294912BatchNorm-80 (1, 256, 64, 88) 1024LeakyReLU-81 (1, 256, 64, 88) 0
DarknetBasicBlockV3-82 (1, 256, 64, 88) 0Conv2D-83 (1, 128, 64, 88) 32768BatchNorm-84 (1, 128, 64, 88) 512LeakyReLU-85 (1, 128, 64, 88) 0Conv2D-86 (1, 256, 64, 88) 294912BatchNorm-87 (1, 256, 64, 88) 1024LeakyReLU-88 (1, 256, 64, 88) 0
DarknetBasicBlockV3-89 (1, 256, 64, 88) 0
darknet53.features[:15]
--------------------------------------------------------------------------------------------------Conv2D-90 (1, 512, 32, 44) 1179648BatchNorm-91 (1, 512, 32, 44) 2048LeakyReLU-92 (1, 512, 32, 44) 0Conv2D-93 (1, 256, 32, 44) 131072BatchNorm-94 (1, 256, 32, 44) 1024LeakyReLU-95 (1, 256, 32, 44) 0Conv2D-96 (1, 512, 32, 44) 1179648BatchNorm-97 (1, 512, 32, 44) 2048LeakyReLU-98 (1, 512, 32, 44) 0
DarknetBasicBlockV3-99 (1, 512, 32, 44) 0Conv2D-100 (1, 256, 32, 44) 131072BatchNorm-101 (1, 256, 32, 44) 1024LeakyReLU-102 (1, 256, 32, 44) 0Conv2D-103 (1, 512, 32, 44) 1179648BatchNorm-104 (1, 512, 32, 44) 2048LeakyReLU-105 (1, 512, 32, 44) 0
DarknetBasicBlockV3-106 (1, 512, 32, 44) 0Conv2D-107 (1, 256, 32, 44) 131072BatchNorm-108 (1, 256, 32, 44) 1024LeakyReLU-109 (1, 256, 32, 44) 0Conv2D-110 (1, 512, 32, 44) 1179648BatchNorm-111 (1, 512, 32, 44) 2048LeakyReLU-112 (1, 512, 32, 44) 0
DarknetBasicBlockV3-113 (1, 512, 32, 44) 0Conv2D-114 (1, 256, 32, 44) 131072BatchNorm-115 (1, 256, 32, 44) 1024LeakyReLU-116 (1, 256, 32, 44) 0Conv2D-117 (1, 512, 32, 44) 1179648BatchNorm-118 (1, 512, 32, 44) 2048LeakyReLU-119 (1, 512, 32, 44) 0
DarknetBasicBlockV3-120 (1, 512, 32, 44) 0Conv2D-121 (1, 256, 32, 44) 131072BatchNorm-122 (1, 256, 32, 44) 1024LeakyReLU-123 (1, 256, 32, 44) 0Conv2D-124 (1, 512, 32, 44) 1179648BatchNorm-125 (1, 512, 32, 44) 2048LeakyReLU-126 (1, 512, 32, 44) 0
DarknetBasicBlockV3-127 (1, 512, 32, 44) 0Conv2D-128 (1, 256, 32, 44) 131072BatchNorm-129 (1, 256, 32, 44) 1024LeakyReLU-130 (1, 256, 32, 44) 0Conv2D-131 (1, 512, 32, 44) 1179648BatchNorm-132 (1, 512, 32, 44) 2048LeakyReLU-133 (1, 512, 32, 44) 0
DarknetBasicBlockV3-134 (1, 512, 32, 44) 0Conv2D-135 (1, 256, 32, 44) 131072BatchNorm-136 (1, 256, 32, 44) 1024LeakyReLU-137 (1, 256, 32, 44) 0Conv2D-138 (1, 512, 32, 44) 1179648BatchNorm-139 (1, 512, 32, 44) 2048LeakyReLU-140 (1, 512, 32, 44) 0
DarknetBasicBlockV3-141 (1, 512, 32, 44) 0Conv2D-142 (1, 256, 32, 44) 131072BatchNorm-143 (1, 256, 32, 44) 1024LeakyReLU-144 (1, 256, 32, 44) 0Conv2D-145 (1, 512, 32, 44) 1179648BatchNorm-146 (1, 512, 32, 44) 2048LeakyReLU-147 (1, 512, 32, 44) 0
DarknetBasicBlockV3-148 (1, 512, 32, 44) 0
darknet53.features[15:24]
--------------------------------------------------------------------------------------------------Conv2D-149 (1, 1024, 16, 22) 4718592BatchNorm-150 (1, 1024, 16, 22) 4096LeakyReLU-151 (1, 1024, 16, 22) 0Conv2D-152 (1, 512, 16, 22) 524288BatchNorm-153 (1, 512, 16, 22) 2048LeakyReLU-154 (1, 512, 16, 22) 0Conv2D-155 (1, 1024, 16, 22) 4718592BatchNorm-156 (1, 1024, 16, 22) 4096LeakyReLU-157 (1, 1024, 16, 22) 0
DarknetBasicBlockV3-158 (1, 1024, 16, 22) 0Conv2D-159 (1, 512, 16, 22) 524288BatchNorm-160 (1, 512, 16, 22) 2048LeakyReLU-161 (1, 512, 16, 22) 0Conv2D-162 (1, 1024, 16, 22) 4718592BatchNorm-163 (1, 1024, 16, 22) 4096LeakyReLU-164 (1, 1024, 16, 22) 0
DarknetBasicBlockV3-165 (1, 1024, 16, 22) 0Conv2D-166 (1, 512, 16, 22) 524288BatchNorm-167 (1, 512, 16, 22) 2048LeakyReLU-168 (1, 512, 16, 22) 0Conv2D-169 (1, 1024, 16, 22) 4718592BatchNorm-170 (1, 1024, 16, 22) 4096LeakyReLU-171 (1, 1024, 16, 22) 0
DarknetBasicBlockV3-172 (1, 1024, 16, 22) 0Conv2D-173 (1, 512, 16, 22) 524288BatchNorm-174 (1, 512, 16, 22) 2048LeakyReLU-175 (1, 512, 16, 22) 0Conv2D-176 (1, 1024, 16, 22) 4718592BatchNorm-177 (1, 1024, 16, 22) 4096LeakyReLU-178 (1, 1024, 16, 22) 0
DarknetBasicBlockV3-179 (1, 1024, 16, 22) 0
darknet53.features[24:]
--------------------------------------------------------------------------------------------------Conv2D-180 (1, 512, 16, 22) 524288BatchNorm-181 (1, 512, 16, 22) 2048LeakyReLU-182 (1, 512, 16, 22) 0Conv2D-183 (1, 1024, 16, 22) 4718592BatchNorm-184 (1, 1024, 16, 22) 4096LeakyReLU-185 (1, 1024, 16, 22) 0Conv2D-186 (1, 512, 16, 22) 524288BatchNorm-187 (1, 512, 16, 22) 2048LeakyReLU-188 (1, 512, 16, 22) 0Conv2D-189 (1, 1024, 16, 22) 4718592BatchNorm-190 (1, 1024, 16, 22) 4096LeakyReLU-191 (1, 1024, 16, 22) 0Conv2D-192 (1, 512, 16, 22) 524288BatchNorm-193 (1, 512, 16, 22) 2048LeakyReLU-194 (1, 512, 16, 22) 0
YOLODetectionBlockV3.body
--------------------------------------------------------------------------------------------------Conv2D-195 (1, 1024, 16, 22) 4718592BatchNorm-196 (1, 1024, 16, 22) 4096LeakyReLU-197 (1, 1024, 16, 22) 0
YOLODetectionBlockV3.tip
--------------------------------------------------------------------------------------------------
YOLODetectionBlockV3-198 (1, 512, 16, 22), (1, 1024, 16, 22) 0
--------------------------------------------------------------------------------------------------Conv2D-199 (1, 255, 16, 22) 261375
YOLOOutputV3.prediction
--------------------------------------------------------------------------------------------------YOLOOutputV3-200 (1, 84480, 6) 32774
YOLOOutputV3
--------------------------------------------------------------------------------------------------Conv2D-201 (1, 256, 16, 22) 131072BatchNorm-202 (1, 256, 16, 22) 1024LeakyReLU-203 (1, 256, 16, 22) 0
YOLOV3.transitions[0]----->_conv2d
--------------------------------------------------------------------------------------------------Conv2D-204 (1, 256, 32, 44) 196608BatchNorm-205 (1, 256, 32, 44) 1024LeakyReLU-206 (1, 256, 32, 44) 0Conv2D-207 (1, 512, 32, 44) 1179648BatchNorm-208 (1, 512, 32, 44) 2048LeakyReLU-209 (1, 512, 32, 44) 0Conv2D-210 (1, 256, 32, 44) 131072BatchNorm-211 (1, 256, 32, 44) 1024LeakyReLU-212 (1, 256, 32, 44) 0Conv2D-213 (1, 512, 32, 44) 1179648BatchNorm-214 (1, 512, 32, 44) 2048LeakyReLU-215 (1, 512, 32, 44) 0Conv2D-216 (1, 256, 32, 44) 131072BatchNorm-217 (1, 256, 32, 44) 1024LeakyReLU-218 (1, 256, 32, 44) 0
YOLODetectionBlockV3.body
--------------------------------------------------------------------------------------------------Conv2D-219 (1, 512, 32, 44) 1179648BatchNorm-220 (1, 512, 32, 44) 2048LeakyReLU-221 (1, 512, 32, 44) 0
YOLODetectionBlockV3.tip
--------------------------------------------------------------------------------------------------
YOLODetectionBlockV3-222 (1, 256, 32, 44), (1, 512, 32, 44) 0
--------------------------------------------------------------------------------------------------Conv2D-223 (1, 255, 32, 44) 130815
YOLOOutputV3.prediction
--------------------------------------------------------------------------------------------------YOLOOutputV3-224 (1, 337920, 6) 32774
YOLOOutputV3
--------------------------------------------------------------------------------------------------Conv2D-225 (1, 128, 32, 44) 32768BatchNorm-226 (1, 128, 32, 44) 512LeakyReLU-227 (1, 128, 32, 44) 0
YOLOV3.transitions[1]----->_conv2d
--------------------------------------------------------------------------------------------------Conv2D-228 (1, 128, 64, 88) 49152BatchNorm-229 (1, 128, 64, 88) 512LeakyReLU-230 (1, 128, 64, 88) 0Conv2D-231 (1, 256, 64, 88) 294912BatchNorm-232 (1, 256, 64, 88) 1024LeakyReLU-233 (1, 256, 64, 88) 0Conv2D-234 (1, 128, 64, 88) 32768BatchNorm-235 (1, 128, 64, 88) 512LeakyReLU-236 (1, 128, 64, 88) 0Conv2D-237 (1, 256, 64, 88) 294912BatchNorm-238 (1, 256, 64, 88) 1024LeakyReLU-239 (1, 256, 64, 88) 0Conv2D-240 (1, 128, 64, 88) 32768BatchNorm-241 (1, 128, 64, 88) 512LeakyReLU-242 (1, 128, 64, 88) 0
YOLODetectionBlockV3.body
--------------------------------------------------------------------------------------------------Conv2D-243 (1, 256, 64, 88) 294912BatchNorm-244 (1, 256, 64, 88) 1024LeakyReLU-245 (1, 256, 64, 88) 0
YOLODetectionBlockV3.tip
--------------------------------------------------------------------------------------------------
YOLODetectionBlockV3-246 (1, 128, 64, 88), (1, 256, 64, 88) 0
--------------------------------------------------------------------------------------------------Conv2D-247 (1, 255, 64, 88) 65535
YOLOOutputV3.prediction
--------------------------------------------------------------------------------------------------YOLOOutputV3-248 (1, 1351680, 6) 32774
YOLOOutputV3
--------------------------------------------------------------------------------------------------YOLOV3-249 (1, 100, 1), (1, 100, 1), (1, 100, 4) 0
================================================================================
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打印日志如下，有用的沒用的都放這里了
darknet_version = v3
num_layers = 53
darknet layers = [1, 2, 8, 8, 4]
darknet channels = [32, 64, 128, 256, 512, 1024]
anchors = [116. 90. 156. 198. 373. 326.]
self._num_pred = 85
self._num_anchors = 3
all_pred = 255
anchors = [[[[116. 90.][156. 198.][373. 326.]]]]
self.anchors =
[[[[116. 90.][156. 198.][373. 326.]]]]
<NDArray 1x1x3x2 @cpu(0)>
anchors = [ 30. 61. 62. 45. 59. 119.]
self._num_pred = 85
self._num_anchors = 3
all_pred = 255
anchors = [[[[ 30. 61.][ 62. 45.][ 59. 119.]]]]
self.anchors =
[[[[ 30. 61.][ 62. 45.][ 59. 119.]]]]
<NDArray 1x1x3x2 @cpu(0)>
anchors = [10. 13. 16. 30. 33. 23.]
self._num_pred = 85
self._num_anchors = 3
all_pred = 255
anchors = [[[[10. 13.][16. 30.][33. 23.]]]]
self.anchors =
[[[[10. 13.][16. 30.][33. 23.]]]]
<NDArray 1x1x3x2 @cpu(0)>
Shape of pre-processed image: (1, 3, 512, 703)
x.shape = (1, 256, 64, 88)
x.shape = (1, 512, 32, 44)
x.shape = (1, 1024, 16, 22)
pred.shape = (1, 255, 16, 22)
pred.shape = (1, 255, 352)
pred.shape = (1, 352, 3, 85)
raw_box_centers.shape = (1, 352, 3, 2)
raw_box_scales.shape = (1, 352, 3, 2)
objness.shape = (1, 352, 3, 1)
class_pred.shape = (1, 352, 3, 80)
offsets.shape = (1, 1, 128, 128, 2)
slice_like offsets.shape = (1, 1, 16, 22, 2)
reshape offsets.shape = (1, 352, 1, 2)
box_centers.shape = (1, 352, 3, 2)
anchors.shape = (1, 1, 3, 2)
box_scales.shape = (1, 352, 3, 2)
confidence.shape = (1, 352, 3, 1)
class_score.shape = (1, 352, 3, 80)
bbox.shape = (1, 352, 3, 4)
bboxes.shape = (80, 1, 352, 3, 4)
scores.shape = (80, 1, 352, 3, 1)
ids.shape = (80, 1, 352, 3, 1)
detections = (80, 1, 352, 3, 6)
detections = (1, 84480, 6)
dets.shape = (1, 84480, 6)
self.transitions = <class 'mxnet.gluon.nn.basic_layers.HybridSequential'>
x.shape = (1, 256, 16, 22)
upsample.shape = (1, 256, 32, 44)
route_now.shape = (1, 512, 32, 44)
x.shape = (1, 768, 32, 44)
pred.shape = (1, 255, 32, 44)
pred.shape = (1, 255, 1408)
pred.shape = (1, 1408, 3, 85)
raw_box_centers.shape = (1, 1408, 3, 2)
raw_box_scales.shape = (1, 1408, 3, 2)
objness.shape = (1, 1408, 3, 1)
class_pred.shape = (1, 1408, 3, 80)
offsets.shape = (1, 1, 128, 128, 2)
slice_like offsets.shape = (1, 1, 32, 44, 2)
reshape offsets.shape = (1, 1408, 1, 2)
box_centers.shape = (1, 1408, 3, 2)
anchors.shape = (1, 1, 3, 2)
box_scales.shape = (1, 1408, 3, 2)
confidence.shape = (1, 1408, 3, 1)
class_score.shape = (1, 1408, 3, 80)
bbox.shape = (1, 1408, 3, 4)
bboxes.shape = (80, 1, 1408, 3, 4)
scores.shape = (80, 1, 1408, 3, 1)
ids.shape = (80, 1, 1408, 3, 1)
detections = (80, 1, 1408, 3, 6)
detections = (1, 337920, 6)
dets.shape = (1, 337920, 6)
self.transitions = <class 'mxnet.gluon.nn.basic_layers.HybridSequential'>
x.shape = (1, 128, 32, 44)
upsample.shape = (1, 128, 64, 88)
route_now.shape = (1, 256, 64, 88)
x.shape = (1, 384, 64, 88)
pred.shape = (1, 255, 64, 88)
pred.shape = (1, 255, 5632)
pred.shape = (1, 5632, 3, 85)
raw_box_centers.shape = (1, 5632, 3, 2)
raw_box_scales.shape = (1, 5632, 3, 2)
objness.shape = (1, 5632, 3, 1)
class_pred.shape = (1, 5632, 3, 80)
offsets.shape = (1, 1, 128, 128, 2)
slice_like offsets.shape = (1, 1, 64, 88, 2)
reshape offsets.shape = (1, 5632, 1, 2)
box_centers.shape = (1, 5632, 3, 2)
anchors.shape = (1, 1, 3, 2)
box_scales.shape = (1, 5632, 3, 2)
confidence.shape = (1, 5632, 3, 1)
class_score.shape = (1, 5632, 3, 80)
bbox.shape = (1, 5632, 3, 4)
bboxes.shape = (80, 1, 5632, 3, 4)
scores.shape = (80, 1, 5632, 3, 1)
ids.shape = (80, 1, 5632, 3, 1)
detections = (80, 1, 5632, 3, 6)
detections = (1, 1351680, 6)
dets.shape = (1, 1351680, 6)
all_detections.shape = 3
all_detections[0].shape = (1, 84480, 6)
result.shape = (1, 1774080, 6)
after result.shape = (1, 1774080, 6)
slice result.shape = (1, 100, 6)
自己總結的計算過程如下：
img ——> darknet53 ——> YOLODetectionBlockV3.body ——> YOLODetectionBlockV3.tip ——> YOLOOutputV3.prediction ——>
(reshape, transpose, slice_axis) ——> [raw_box_centers(2), raw_box_scales(2), objness(1), class_pred(80)] ——>
[box_centers(offsets, _stride), box_scales(anchors), confidence, class_score(class_pred * confidence)] ——>
[bboxes(4), scores(1)] + ids(from arrange,1) ——> (reshape) ——> detections ——> box_nms ——> slice_axis ——>
(ids, scores, bboxes)class YOLOV3(gluon.HybridBlock): 添加的打印如下：
def hybrid_forward(self, F, x, *args):"""YOLOV3 network hybrid forward.Parameters----------F : mxnet.nd or mxnet.sym`F` is mxnet.sym if hybridized or mxnet.nd if not.x : mxnet.nd.NDArrayInput data.*args : optional, mxnet.nd.NDArrayDuring training, extra inputs are required:(gt_boxes, obj_t, centers_t, scales_t, weights_t, clas_t)These are generated by YOLOV3PrefetchTargetGenerator in dataloader transform function.Returns-------(tuple of) mxnet.nd.NDArrayDuring inference, return detections in shape (B, N, 6)with format (cid, score, xmin, ymin, xmax, ymax)During training, return losses only: (obj_loss, center_loss, scale_loss, cls_loss)."""all_box_centers = []all_box_scales = []all_objectness = []all_class_pred = []all_anchors = []all_offsets = []all_feat_maps = []all_detections = []routes = []for stage in self.stages:x = stage(x)routes.append(x)print("x.shape = ", x.shape)# the YOLO output layers are used in reverse order, i.e., from very deep layers to shallowfor i, block, output in zip(range(len(routes)), self.yolo_blocks, self.yolo_outputs):x, tip = block(x)dets = output(tip)print("dets.shape = ", dets.shape)all_detections.append(dets)if i >= len(routes) - 1:break# add transition layersprint("self.transitions = ", type(self.transitions))x = self.transitions[i](x)print("x.shape = ", x.shape)# upsample feature map reverse to shallow layersupsample = _upsample(x, stride=2)print("upsample.shape = ", upsample.shape)route_now = routes[::-1][i + 1]print("route_now.shape = ", route_now.shape)x = F.concat(upsample, route_now, dim=1)print("x.shape = ", x.shape)print("all_detections.shape = ", len(all_detections))print("all_detections[0].shape = ", all_detections[0].shape)# concat all detection results from different stagesresult = F.concat(*all_detections, dim=1)print("result.shape = ", result.shape)# apply nms per classif self.nms_thresh > 0 and self.nms_thresh < 1:result = F.contrib.box_nms(result, overlap_thresh=self.nms_thresh, valid_thresh=0.01,topk=self.nms_topk, id_index=0, score_index=1, coord_start=2, force_suppress=False)print("after result.shape = ", result.shape)if self.post_nms > 0:result = result.slice_axis(axis=1, begin=0, end=self.post_nms)print("slice result.shape = ", result.shape)ids = result.slice_axis(axis=-1, begin=0, end=1)scores = result.slice_axis(axis=-1, begin=1, end=2)bboxes = result.slice_axis(axis=-1, begin=2, end=None)return ids, scores, bboxes
class YOLOOutputV3(gluon.HybridBlock):添加的打印如下：
class YOLOOutputV3(gluon.HybridBlock):"""YOLO output layer V3.Parameters----------index : intIndex of the yolo output layer, to avoid naming conflicts only.num_class : intNumber of foreground objects.anchors : iterableThe anchor setting. Reference: https://arxiv.org/pdf/1804.02767.pdf.stride : intStride of feature map.alloc_size : tuple of int, default is (128, 128)For advanced users. Define `alloc_size` to generate large enough anchormaps, which will later saved in parameters. During inference, we support arbitraryinput image by cropping corresponding area of the anchor map. This allow usto export to symbol so we can run it in c++, Scalar, etc."""def __init__(self, index, num_class, anchors, stride,alloc_size=(128, 128), **kwargs):super(YOLOOutputV3, self).__init__(**kwargs)anchors = np.array(anchors).astype('float32')print("anchors = ", anchors)self._classes = num_classself._num_pred = 1 + 4 + num_class # 1 objness + 4 box + num_classself._num_anchors = anchors.size // 2self._stride = stridewith self.name_scope():print("self._num_pred = ", self._num_pred)print("self._num_anchors = ", self._num_anchors)all_pred = self._num_pred * self._num_anchorsprint("all_pred = %d" % all_pred)self.prediction = nn.Conv2D(all_pred, kernel_size=1, padding=0, strides=1)# anchors will be multiplied to predictionsanchors = anchors.reshape(1, 1, -1, 2)print("anchors = ", anchors)self.anchors = self.params.get_constant('anchor_%d'%(index), anchors)print("self.anchors = ", self.anchors.value)# offsets will be added to predictionsgrid_x = np.arange(alloc_size[1])grid_y = np.arange(alloc_size[0])grid_x, grid_y = np.meshgrid(grid_x, grid_y)# stack to (n, n, 2)offsets = np.concatenate((grid_x[:, :, np.newaxis], grid_y[:, :, np.newaxis]), axis=-1)# expand dims to (1, 1, n, n, 2) so it's easier for broadcastingoffsets = np.expand_dims(np.expand_dims(offsets, axis=0), axis=0)self.offsets = self.params.get_constant('offset_%d'%(index), offsets)def hybrid_forward(self, F, x, anchors, offsets):"""Hybrid Forward of YOLOV3Output layer.Parameters----------F : mxnet.nd or mxnet.sym`F` is mxnet.sym if hybridized or mxnet.nd if not.x : mxnet.nd.NDArrayInput feature map.anchors : mxnet.nd.NDArrayAnchors loaded from self, no need to supply.offsets : mxnet.nd.NDArrayOffsets loaded from self, no need to supply.Returns-------(tuple of) mxnet.nd.NDArrayDuring training, return (bbox, raw_box_centers, raw_box_scales, objness,class_pred, anchors, offsets).During inference, return detections."""# prediction flat to (batch, pred per pixel, height * width)pred = self.prediction(x)print("pred.shape = ", pred.shape)pred = pred.reshape((0, self._num_anchors * self._num_pred, -1))print("pred.shape = ", pred.shape)# transpose to (batch, height * width, num_anchor, num_pred)pred = pred.transpose(axes=(0, 2, 1)).reshape((0, -1, self._num_anchors, self._num_pred))print("pred.shape = ", pred.shape)# componentsraw_box_centers = pred.slice_axis(axis=-1, begin=0, end=2)print("raw_box_centers.shape = ", raw_box_centers.shape)raw_box_scales = pred.slice_axis(axis=-1, begin=2, end=4)print("raw_box_scales.shape = ", raw_box_scales.shape)objness = pred.slice_axis(axis=-1, begin=4, end=5)print("objness.shape = ", objness.shape)class_pred = pred.slice_axis(axis=-1, begin=5, end=None)print("class_pred.shape = ", class_pred.shape)# valid offsets, (1, 1, height, width, 2)print("offsets.shape = ", offsets.shape)offsets = F.slice_like(offsets, x * 0, axes=(2, 3))print("slice_like offsets.shape = ", offsets.shape)# reshape to (1, height*width, 1, 2)offsets = offsets.reshape((1, -1, 1, 2))print("reshape offsets.shape = ", offsets.shape)# print(offsets)box_centers = F.broadcast_add(F.sigmoid(raw_box_centers), offsets) * self._strideprint("box_centers.shape = ", box_centers.shape)print("anchors.shape = ", anchors.shape)box_scales = F.broadcast_mul(F.exp(raw_box_scales), anchors)print("box_scales.shape = ", box_scales.shape)confidence = F.sigmoid(objness)print("confidence.shape = ", confidence.shape)class_score = F.broadcast_mul(F.sigmoid(class_pred), confidence)print("class_score.shape = ", class_score.shape)wh = box_scales / 2.0bbox = F.concat(box_centers - wh, box_centers + wh, dim=-1)print("bbox.shape = ", bbox.shape)# prediction per classbboxes = F.tile(bbox, reps=(self._classes, 1, 1, 1, 1))print("bboxes.shape = ", bboxes.shape)scores = F.transpose(class_score, axes=(3, 0, 1, 2)).expand_dims(axis=-1)print("scores.shape = ", scores.shape)ids = F.broadcast_add(scores * 0, F.arange(0, self._classes).reshape((0, 1, 1, 1, 1)))print("ids.shape = ", ids.shape)detections = F.concat(ids, scores, bboxes, dim=-1)print("detections = ", detections.shape)# reshape to (B, xx, 6)detections = F.reshape(detections.transpose(axes=(1, 0, 2, 3, 4)), (0, -1, 6))print("detections = ", detections.shape)return detections?
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