Spaces:
Running
on
T4
Running
on
T4
File size: 11,129 Bytes
186701e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 |
from typing import List, Tuple, Union
import numpy as np
from config import ModelType
from numpy import ndarray
def softmax(x: ndarray, axis: int = -1) -> ndarray:
e_x = np.exp(x - np.max(x, axis=axis, keepdims=True))
y = e_x / e_x.sum(axis=axis, keepdims=True)
return y
def sigmoid(x: ndarray) -> ndarray:
return 1. / (1. + np.exp(-x))
class Decoder:
def __init__(self, model_type: ModelType, model_only: bool = False):
self.model_type = model_type
self.model_only = model_only
self.boxes_pro = []
self.scores_pro = []
self.labels_pro = []
self.is_logging = False
def __call__(self,
feats: Union[List, Tuple],
conf_thres: float,
num_labels: int = 80,
**kwargs) -> Tuple:
if not self.is_logging:
print('Only support decode in batch==1')
self.is_logging = True
self.boxes_pro.clear()
self.scores_pro.clear()
self.labels_pro.clear()
if self.model_only:
# transpose channel to last dim for easy decoding
feats = [
np.ascontiguousarray(feat[0].transpose(1, 2, 0))
for feat in feats
]
else:
# ax620a horizonX3 transpose channel to last dim by default
feats = [np.ascontiguousarray(feat) for feat in feats]
if self.model_type == ModelType.YOLOV5:
self.__yolov5_decode(feats, conf_thres, num_labels, **kwargs)
elif self.model_type == ModelType.YOLOX:
self.__yolox_decode(feats, conf_thres, num_labels, **kwargs)
elif self.model_type in (ModelType.PPYOLOE, ModelType.PPYOLOEP):
self.__ppyoloe_decode(feats, conf_thres, num_labels, **kwargs)
elif self.model_type == ModelType.YOLOV6:
self.__yolov6_decode(feats, conf_thres, num_labels, **kwargs)
elif self.model_type == ModelType.YOLOV7:
self.__yolov7_decode(feats, conf_thres, num_labels, **kwargs)
elif self.model_type == ModelType.RTMDET:
self.__rtmdet_decode(feats, conf_thres, num_labels, **kwargs)
elif self.model_type == ModelType.YOLOV8:
self.__yolov8_decode(feats, conf_thres, num_labels, **kwargs)
else:
raise NotImplementedError
return self.boxes_pro, self.scores_pro, self.labels_pro
def __yolov5_decode(self,
feats: List[ndarray],
conf_thres: float,
num_labels: int = 80,
**kwargs):
anchors: Union[List, Tuple] = kwargs.get(
'anchors',
[[(10, 13), (16, 30),
(33, 23)], [(30, 61), (62, 45),
(59, 119)], [(116, 90), (156, 198), (373, 326)]])
for i, feat in enumerate(feats):
stride = 8 << i
feat_h, feat_w, _ = feat.shape
anchor = anchors[i]
feat = sigmoid(feat)
feat = feat.reshape((feat_h, feat_w, len(anchor), -1))
box_feat, conf_feat, score_feat = np.split(feat, [4, 5], -1)
hIdx, wIdx, aIdx, _ = np.where(conf_feat > conf_thres)
num_proposal = hIdx.size
if not num_proposal:
continue
score_feat = score_feat[hIdx, wIdx, aIdx] * conf_feat[hIdx, wIdx,
aIdx]
boxes = box_feat[hIdx, wIdx, aIdx]
labels = score_feat.argmax(-1)
scores = score_feat.max(-1)
indices = np.where(scores > conf_thres)[0]
if len(indices) == 0:
continue
for idx in indices:
a_w, a_h = anchor[aIdx[idx]]
x, y, w, h = boxes[idx]
x = (x * 2.0 - 0.5 + wIdx[idx]) * stride
y = (y * 2.0 - 0.5 + hIdx[idx]) * stride
w = (w * 2.0)**2 * a_w
h = (h * 2.0)**2 * a_h
x0 = x - w / 2
y0 = y - h / 2
self.scores_pro.append(float(scores[idx]))
self.boxes_pro.append(
np.array([x0, y0, w, h], dtype=np.float32))
self.labels_pro.append(int(labels[idx]))
def __yolox_decode(self,
feats: List[ndarray],
conf_thres: float,
num_labels: int = 80,
**kwargs):
for i, feat in enumerate(feats):
stride = 8 << i
score_feat, box_feat, conf_feat = np.split(
feat, [num_labels, num_labels + 4], -1)
conf_feat = sigmoid(conf_feat)
hIdx, wIdx, _ = np.where(conf_feat > conf_thres)
num_proposal = hIdx.size
if not num_proposal:
continue
score_feat = sigmoid(score_feat[hIdx, wIdx]) * conf_feat[hIdx,
wIdx]
boxes = box_feat[hIdx, wIdx]
labels = score_feat.argmax(-1)
scores = score_feat.max(-1)
indices = np.where(scores > conf_thres)[0]
if len(indices) == 0:
continue
for idx in indices:
score = scores[idx]
label = labels[idx]
x, y, w, h = boxes[idx]
x = (x + wIdx[idx]) * stride
y = (y + hIdx[idx]) * stride
w = np.exp(w) * stride
h = np.exp(h) * stride
x0 = x - w / 2
y0 = y - h / 2
self.scores_pro.append(float(score))
self.boxes_pro.append(
np.array([x0, y0, w, h], dtype=np.float32))
self.labels_pro.append(int(label))
def __ppyoloe_decode(self,
feats: List[ndarray],
conf_thres: float,
num_labels: int = 80,
**kwargs):
reg_max: int = kwargs.get('reg_max', 17)
dfl = np.arange(0, reg_max, dtype=np.float32)
for i, feat in enumerate(feats):
stride = 8 << i
score_feat, box_feat = np.split(feat, [
num_labels,
], -1)
score_feat = sigmoid(score_feat)
_argmax = score_feat.argmax(-1)
_max = score_feat.max(-1)
indices = np.where(_max > conf_thres)
hIdx, wIdx = indices
num_proposal = hIdx.size
if not num_proposal:
continue
scores = _max[hIdx, wIdx]
boxes = box_feat[hIdx, wIdx].reshape(num_proposal, 4, reg_max)
boxes = softmax(boxes, -1) @ dfl
labels = _argmax[hIdx, wIdx]
for k in range(num_proposal):
score = scores[k]
label = labels[k]
x0, y0, x1, y1 = boxes[k]
x0 = (wIdx[k] + 0.5 - x0) * stride
y0 = (hIdx[k] + 0.5 - y0) * stride
x1 = (wIdx[k] + 0.5 + x1) * stride
y1 = (hIdx[k] + 0.5 + y1) * stride
w = x1 - x0
h = y1 - y0
self.scores_pro.append(float(score))
self.boxes_pro.append(
np.array([x0, y0, w, h], dtype=np.float32))
self.labels_pro.append(int(label))
def __yolov6_decode(self,
feats: List[ndarray],
conf_thres: float,
num_labels: int = 80,
**kwargs):
for i, feat in enumerate(feats):
stride = 8 << i
score_feat, box_feat = np.split(feat, [
num_labels,
], -1)
score_feat = sigmoid(score_feat)
_argmax = score_feat.argmax(-1)
_max = score_feat.max(-1)
indices = np.where(_max > conf_thres)
hIdx, wIdx = indices
num_proposal = hIdx.size
if not num_proposal:
continue
scores = _max[hIdx, wIdx]
boxes = box_feat[hIdx, wIdx]
labels = _argmax[hIdx, wIdx]
for k in range(num_proposal):
score = scores[k]
label = labels[k]
x0, y0, x1, y1 = boxes[k]
x0 = (wIdx[k] + 0.5 - x0) * stride
y0 = (hIdx[k] + 0.5 - y0) * stride
x1 = (wIdx[k] + 0.5 + x1) * stride
y1 = (hIdx[k] + 0.5 + y1) * stride
w = x1 - x0
h = y1 - y0
self.scores_pro.append(float(score))
self.boxes_pro.append(
np.array([x0, y0, w, h], dtype=np.float32))
self.labels_pro.append(int(label))
def __yolov7_decode(self,
feats: List[ndarray],
conf_thres: float,
num_labels: int = 80,
**kwargs):
anchors: Union[List, Tuple] = kwargs.get(
'anchors',
[[(12, 16), (19, 36),
(40, 28)], [(36, 75), (76, 55),
(72, 146)], [(142, 110), (192, 243), (459, 401)]])
self.__yolov5_decode(feats, conf_thres, num_labels, anchors=anchors)
def __rtmdet_decode(self,
feats: List[ndarray],
conf_thres: float,
num_labels: int = 80,
**kwargs):
for i, feat in enumerate(feats):
stride = 8 << i
score_feat, box_feat = np.split(feat, [
num_labels,
], -1)
score_feat = sigmoid(score_feat)
_argmax = score_feat.argmax(-1)
_max = score_feat.max(-1)
indices = np.where(_max > conf_thres)
hIdx, wIdx = indices
num_proposal = hIdx.size
if not num_proposal:
continue
scores = _max[hIdx, wIdx]
boxes = box_feat[hIdx, wIdx]
labels = _argmax[hIdx, wIdx]
for k in range(num_proposal):
score = scores[k]
label = labels[k]
x0, y0, x1, y1 = boxes[k]
x0 = (wIdx[k] - x0) * stride
y0 = (hIdx[k] - y0) * stride
x1 = (wIdx[k] + x1) * stride
y1 = (hIdx[k] + y1) * stride
w = x1 - x0
h = y1 - y0
self.scores_pro.append(float(score))
self.boxes_pro.append(
np.array([x0, y0, w, h], dtype=np.float32))
self.labels_pro.append(int(label))
def __yolov8_decode(self,
feats: List[ndarray],
conf_thres: float,
num_labels: int = 80,
**kwargs):
reg_max: int = kwargs.get('reg_max', 16)
self.__ppyoloe_decode(feats, conf_thres, num_labels, reg_max=reg_max)
|