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added train

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asxalex
2025-08-22 14:32:50 +08:00
parent 05a551d523
commit 4f26f54017
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train/anchors_yolov5.txt
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train/convert.py
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import sys
from rknn.api import RKNN
DATASET_PATH = '../../../datasets/COCO/coco_subset_20.txt'
DEFAULT_RKNN_PATH = '../model/yolov5.rknn'
DEFAULT_QUANT = True
def parse_arg():
if len(sys.argv) < 3:
print("Usage: python3 {} onnx_model_path [platform] [dtype(optional)] [output_rknn_path(optional)]".format(sys.argv[0]))
print(" platform choose from [rk3562, rk3566, rk3568, rk3576, rk3588, rv1103, rv1106, rv1126b, rv1109, rv1126, rk1808]")
print(" dtype choose from [i8, fp] for [rk3562, rk3566, rk3568, rk3576, rk3588, rv1103, rv1106, rv1126b]")
print(" dtype choose from [u8, fp] for [rv1109, rv1126, rk1808]")
exit(1)
model_path = sys.argv[1]
platform = sys.argv[2]
do_quant = DEFAULT_QUANT
if len(sys.argv) > 3:
model_type = sys.argv[3]
if model_type not in ['i8', 'u8', 'fp']:
print("ERROR: Invalid model type: {}".format(model_type))
exit(1)
elif model_type in ['i8', 'u8']:
do_quant = True
else:
do_quant = False
if len(sys.argv) > 4:
output_path = sys.argv[4]
else:
output_path = DEFAULT_RKNN_PATH
return model_path, platform, do_quant, output_path
if __name__ == '__main__':
model_path, platform, do_quant, output_path = parse_arg()
# Create RKNN object
rknn = RKNN(verbose=False)
# Pre-process config
print('--> Config model')
rknn.config(mean_values=[[0, 0, 0]], std_values=[
[255, 255, 255]], target_platform=platform)
print('done')
# Load model
print('--> Loading model')
ret = rknn.load_onnx(model=model_path)
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=do_quant, dataset=DATASET_PATH)
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export rknn model')
ret = rknn.export_rknn(output_path)
if ret != 0:
print('Export rknn model failed!')
exit(ret)
print('done')
# Release
rknn.release()
train/test.py
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import cv2
import os
import numpy as np
from rknnlite.api import RKNNLite
import sys
from copy import copy
OBJ_THRESH = 0.25
NMS_THRESH = 0.45
# The follew two param is for map test
# OBJ_THRESH = 0.001
# NMS_THRESH = 0.65
IMG_SIZE = (640, 640) # (width, height), such as (1280, 736)
CLASSES = ("person", "bicycle", "car","motorbike ","aeroplane ","bus ","train","truck ","boat","traffic light",
"fire hydrant","stop sign ","parking meter","bench","bird","cat","dog ","horse ","sheep","cow","elephant",
"bear","zebra ","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite",
"baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife ",
"spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza ","donut","cake","chair","sofa",
"pottedplant","bed","diningtable","toilet ","tvmonitor","laptop ","mouse ","remote ","keyboard ","cell phone","microwave ",
"oven ","toaster","sink","refrigerator ","book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush ")
coco_id_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
def get_real_box(self, box, in_format='xyxy'):
bbox = copy(box)
if self.enable_ltter_box == True:
# unletter_box result
if in_format=='xyxy':
bbox[:,0] -= self.letter_box_info_list[-1].dw
bbox[:,0] /= self.letter_box_info_list[-1].w_ratio
bbox[:,0] = np.clip(bbox[:,0], 0, self.letter_box_info_list[-1].origin_shape[1])
bbox[:,1] -= self.letter_box_info_list[-1].dh
bbox[:,1] /= self.letter_box_info_list[-1].h_ratio
bbox[:,1] = np.clip(bbox[:,1], 0, self.letter_box_info_list[-1].origin_shape[0])
bbox[:,2] -= self.letter_box_info_list[-1].dw
bbox[:,2] /= self.letter_box_info_list[-1].w_ratio
bbox[:,2] = np.clip(bbox[:,2], 0, self.letter_box_info_list[-1].origin_shape[1])
bbox[:,3] -= self.letter_box_info_list[-1].dh
bbox[:,3] /= self.letter_box_info_list[-1].h_ratio
bbox[:,3] = np.clip(bbox[:,3], 0, self.letter_box_info_list[-1].origin_shape[0])
return bbox
def filter_boxes(boxes, box_confidences, box_class_probs):
"""Filter boxes with object threshold.
"""
box_confidences = box_confidences.reshape(-1)
class_max_score = np.max(box_class_probs, axis=-1)
classes = np.argmax(box_class_probs, axis=-1)
_class_pos = np.where(class_max_score* box_confidences >= OBJ_THRESH)
scores = (class_max_score* box_confidences)[_class_pos]
boxes = boxes[_class_pos]
classes = classes[_class_pos]
return boxes, classes, scores
def nms_boxes(boxes, scores):
"""Suppress non-maximal boxes.
# Returns
keep: ndarray, index of effective boxes.
"""
x = boxes[:, 0]
y = boxes[:, 1]
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
areas = w * h
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x[i], x[order[1:]])
yy1 = np.maximum(y[i], y[order[1:]])
xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
inter = w1 * h1
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= NMS_THRESH)[0]
order = order[inds + 1]
keep = np.array(keep)
return keep
def box_process(position, anchors):
grid_h, grid_w = position.shape[2:4]
col, row = np.meshgrid(np.arange(0, grid_w), np.arange(0, grid_h))
col = col.reshape(1, 1, grid_h, grid_w)
row = row.reshape(1, 1, grid_h, grid_w)
grid = np.concatenate((col, row), axis=1)
stride = np.array([IMG_SIZE[1]//grid_h, IMG_SIZE[0]//grid_w]).reshape(1,2,1,1)
col = col.repeat(len(anchors), axis=0)
row = row.repeat(len(anchors), axis=0)
anchors = np.array(anchors)
anchors = anchors.reshape(*anchors.shape, 1, 1)
box_xy = position[:,:2,:,:]*2 - 0.5
box_wh = pow(position[:,2:4,:,:]*2, 2) * anchors
box_xy += grid
box_xy *= stride
box = np.concatenate((box_xy, box_wh), axis=1)
# Convert [c_x, c_y, w, h] to [x1, y1, x2, y2]
xyxy = np.copy(box)
xyxy[:, 0, :, :] = box[:, 0, :, :] - box[:, 2, :, :]/ 2 # top left x
xyxy[:, 1, :, :] = box[:, 1, :, :] - box[:, 3, :, :]/ 2 # top left y
xyxy[:, 2, :, :] = box[:, 0, :, :] + box[:, 2, :, :]/ 2 # bottom right x
xyxy[:, 3, :, :] = box[:, 1, :, :] + box[:, 3, :, :]/ 2 # bottom right y
return xyxy
def post_process(input_data, anchors):
boxes, scores, classes_conf = [], [], []
# 1*255*h*w -> 3*85*h*w
input_data = [_in.reshape([len(anchors[0]),-1]+list(_in.shape[-2:])) for _in in input_data]
for i in range(len(input_data)):
boxes.append(box_process(input_data[i][:,:4,:,:], anchors[i]))
scores.append(input_data[i][:,4:5,:,:])
classes_conf.append(input_data[i][:,5:,:,:])
def sp_flatten(_in):
ch = _in.shape[1]
_in = _in.transpose(0,2,3,1)
return _in.reshape(-1, ch)
boxes = [sp_flatten(_v) for _v in boxes]
classes_conf = [sp_flatten(_v) for _v in classes_conf]
scores = [sp_flatten(_v) for _v in scores]
boxes = np.concatenate(boxes)
classes_conf = np.concatenate(classes_conf)
scores = np.concatenate(scores)
# filter according to threshold
boxes, classes, scores = filter_boxes(boxes, scores, classes_conf)
# nms
nboxes, nclasses, nscores = [], [], []
for c in set(classes):
inds = np.where(classes == c)
b = boxes[inds]
c = classes[inds]
s = scores[inds]
keep = nms_boxes(b, s)
if len(keep) != 0:
nboxes.append(b[keep])
nclasses.append(c[keep])
nscores.append(s[keep])
if not nclasses and not nscores:
return None, None, None
boxes = np.concatenate(nboxes)
classes = np.concatenate(nclasses)
scores = np.concatenate(nscores)
return boxes, classes, scores
def draw(image, boxes, scores, classes):
for box, score, cl in zip(boxes, scores, classes):
top, left, right, bottom = [int(_b) for _b in box]
print("%s @ (%d %d %d %d) %.3f" % (CLASSES[cl], top, left, right, bottom, score))
cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
(top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
INPUT_SIZE = 640
if __name__ == '__main__':
rknn_lite = RKNNLite()
model = sys.argv[1]
image_name = sys.argv[2]
target = None
# load RKNN model
print('--> Load RKNN model')
ret = rknn_lite.load_rknn(model)
if ret != 0:
print('Load RKNN model failed')
exit(ret)
print('done')
ori_img = cv2.imread(image_name)
img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
# init runtime environment
print('--> Init runtime environment')
ret = rknn_lite.init_runtime(target=target)
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
with open("anchors_yolov5.txt", 'r') as f:
values = [float(_v) for _v in f.readlines()]
anchors = np.array(values).reshape(3,-1,2).tolist()
print("use anchors from '{}', which is {}".format("anchors_yolov5.txt", anchors))
# Inference
print('--> Running model')
outputs = rknn_lite.inference(inputs=[img])
boxes, classes, scores = post_process(outputs, anchors)
img_p = ori_img.copy()
if boxes is not None:
draw(img_p, get_real_box(boxes), scores, classes)
if not os.path.exists('./result'):
os.mkdir('./result')
result_path = os.path.join('./result', image_name)
cv2.imwrite(result_path, img_p)
print('Detection result save to {}'.format(result_path))
rknn_lite.release()
train/train.md
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# yolov5 train
这里介绍rknn yolov5训练和转换。
## 1. 训练
源码使用[https://github.com/airockchip/yolov5](https://github.com/airockchip/yolov5)这个仓库。
训练过程与官方的yolov5训练方法一致，训练完成之后，使用该仓库的`export.py`进行转换：
```shell
# for detection model
python export.py --rknpu --weight yolov5s.pt
# for segmentation model
python export.py --rknpu --weight yolov5s-seg.pt
```
## 2. 转换
之后，使用这里的`convert.py`将onnx转换为`rknn`:
```shell
python convert.py <onnx_model> <TARGET_PLATFORM> <dtype(optional)> <output_rknn_path(optional)>
## 比如
python convert.py yolov5.onnx rk1808 u8 yolov5.rknn
```
## 3. 测试
将模型部署到1808上之后，使用下面的命令测试一张图片：
```shell
python test.py <rknn_model_name> <test_image>
```