update

onnx2rknn/datasets_gift.txt

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+./gift_data/01_20220915-184726_e9ade0d6-fc05-4fec-8031-0a576f8603e4_6974844373504_5_01.jpg
+./gift_data/01_20220916-162541_f81f6704-5a94-4d40-b628-2ebecd406050_8000500384794_2_01.jpg
+./gift_data/01_20220919-180140_e1d7626f-37ac-46bc-8267-01562fecdc79_6972526870914_7_01.jpg
+./gift_data/01_20220919-180140_e1d7626f-37ac-46bc-8267-01562fecdc79_6972526870914_7_01.jpg

onnx2rknn/onnx2rknn_demo_v10n_224x224_sigmoid.py

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+import os
+import urllib
+import traceback
+import time
+import sys
+import numpy as np
+import cv2
+from rknn.api import RKNN
+from math import exp
+
+
+ONNX_MODEL = '../ckpts/20250514/best_gift_v10n_rk.onnx'
+#RKNN_MODEL = './rk21b/best_0375.rknn'
+RKNN_MODEL = '../ckpts/20250514/best_gift_v10n.rknn'
+#RKNN_MODEL = './rk23b/best_v10s_width0375_1205.rknn'
+
+DATASET = './datasets_gift.txt'
+
+QUANTIZE_ON = True
+
+
+#CLASSES = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light']
+#CLASSES = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']
+CLASSES = ['tag', 'bandage']
+
+meshgrid = []
+
+class_num = len(CLASSES)
+head_num = 3
+strides = [8, 16, 32]
+map_size = [[28, 28], [14, 14], [7, 7]]
+object_thresh = 0.3
+
+input_height = 224
+input_width = 224
+
+topK = 50
+
+
+class DetectBox:
+    def __init__(self, classId, score, xmin, ymin, xmax, ymax):
+        self.classId = classId
+        self.score = score
+        self.xmin = xmin
+        self.ymin = ymin
+        self.xmax = xmax
+        self.ymax = ymax
+
+
+def GenerateMeshgrid():
+    for index in range(head_num):
+        for i in range(map_size[index][0]):
+            for j in range(map_size[index][1]):
+                meshgrid.append(j + 0.5)
+                meshgrid.append(i + 0.5)
+
+
+def TopK(detectResult):
+    if len(detectResult) <= topK:
+        return detectResult
+    else:
+        predBoxs = []
+        sort_detectboxs = sorted(detectResult, key=lambda x: x.score, reverse=True)
+        for i in range(topK):
+            predBoxs.append(sort_detectboxs[i])
+        return predBoxs
+
+
+def sigmoid(x):
+    return 1 / (1 + exp(-x))
+
+
+def postprocess(out, img_h, img_w):
+    print('postprocess ... ')
+
+    detectResult = []
+    output = []
+    for i in range(len(out)):
+        output.append(out[i].reshape((-1)))
+
+    scale_h = img_h / input_height
+    scale_w = img_w / input_width
+
+    gridIndex = -2
+    cls_index = 0
+    cls_max = 0
+
+    for index in range(head_num):
+        reg = output[index * 2 + 0]
+        cls = output[index * 2 + 1]
+
+        for h in range(map_size[index][0]):
+            for w in range(map_size[index][1]):
+                gridIndex += 2
+
+                if 1 == class_num:
+                    # cls_max = sigmoid(cls[0 * map_size[index][0] * map_size[index][1] + h * map_size[index][1] + w])
+                    cls_max = cls[0 * map_size[index][0] * map_size[index][1] + h * map_size[index][1] + w]
+                    cls_index = 0
+                else:
+                    for cl in range(class_num):
+                        cls_val = cls[cl * map_size[index][0] * map_size[index][1] + h * map_size[index][1] + w]
+                        if 0 == cl:
+                            cls_max = cls_val
+                            cls_index = cl
+                        else:
+                            if cls_val > cls_max:
+                                cls_max = cls_val
+                                cls_index = cl
+                    # cls_max = sigmoid(cls_max)
+                    cls_max = cls_max
+
+                if cls_max > object_thresh:
+                    regdfl = []
+                    for lc in range(4):
+                        sfsum = 0
+                        locval = 0
+                        for df in range(16):
+                            temp = exp(reg[((lc * 16) + df) * map_size[index][0] * map_size[index][1] + h * map_size[index][1] + w])
+                            reg[((lc * 16) + df) * map_size[index][0] * map_size[index][1] + h * map_size[index][ 1] + w] = temp
+                            sfsum += temp
+
+                        for df in range(16):
+                            sfval = reg[((lc * 16) + df) * map_size[index][0] * map_size[index][1] + h * map_size[index][
+                                1] + w] / sfsum
+                            locval += sfval * df
+                        regdfl.append(locval)
+
+                    x1 = (meshgrid[gridIndex + 0] - regdfl[0]) * strides[index]
+                    y1 = (meshgrid[gridIndex + 1] - regdfl[1]) * strides[index]
+                    x2 = (meshgrid[gridIndex + 0] + regdfl[2]) * strides[index]
+                    y2 = (meshgrid[gridIndex + 1] + regdfl[3]) * strides[index]
+
+                    xmin = x1 * scale_w
+                    ymin = y1 * scale_h
+                    xmax = x2 * scale_w
+                    ymax = y2 * scale_h
+
+                    xmin = xmin if xmin > 0 else 0
+                    ymin = ymin if ymin > 0 else 0
+                    xmax = xmax if xmax < img_w else img_w
+                    ymax = ymax if ymax < img_h else img_h
+
+                    box = DetectBox(cls_index, cls_max, xmin, ymin, xmax, ymax)
+                    detectResult.append(box)
+    # topK
+    print('before topK num is:', len(detectResult))
+    predBox = TopK(detectResult)
+
+    return predBox
+
+
+
+def export_rknn_inference(img):
+    # 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]], quantized_algorithm='normal', quantized_method='channel', target_platform='rk3588')
+    print('done')
+
+    # Load ONNX model
+    print('--> Loading model')
+    ret = rknn.load_onnx(model=ONNX_MODEL)
+    if ret != 0:
+        print('Load model failed!')
+        exit(ret)
+    print('done')
+
+    # Build model
+    print('--> Building model')
+    ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET, rknn_batch_size=1)
+    if ret != 0:
+        print('Build model failed!')
+        exit(ret)
+    print('done')
+
+    # Export RKNN model
+    print('--> Export rknn model')
+    ret = rknn.export_rknn(RKNN_MODEL)
+    if ret != 0:
+        print('Export rknn model failed!')
+        exit(ret)
+    print('done')
+
+    # Init runtime environment
+    print('--> Init runtime environment')
+    ret = rknn.init_runtime()
+    # ret = rknn.init_runtime(target='rk3566')
+    if ret != 0:
+        print('Init runtime environment failed!')
+        exit(ret)
+    print('done')
+
+    # Inference
+    print('--> Running model')
+    outputs = rknn.inference(inputs=[img])
+    rknn.release()
+    print('done')
+
+    return outputs
+
+
+if __name__ == '__main__':
+    print('This is main ...')
+    GenerateMeshgrid()
+
+    img_path = './test/fg_mat.png'
+    src_img = cv2.imread(img_path)
+    img_h, img_w = src_img.shape[:2]
+    
+    
+    input_img = cv2.resize(src_img, (input_width, input_height))
+    input_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)
+    
+    input_img = np.expand_dims(input_img, 0)
+
+    outputs = export_rknn_inference(input_img)
+
+    out = []
+    for i in range(len(outputs)):
+        out.append(outputs[i])
+
+    predbox = postprocess(out, img_h, img_w)
+
+    print(len(predbox))
+
+    for i in range(len(predbox)):
+        xmin = int(predbox[i].xmin)
+        ymin = int(predbox[i].ymin)
+        xmax = int(predbox[i].xmax)
+        ymax = int(predbox[i].ymax)
+        classId = predbox[i].classId
+        score = predbox[i].score
+
+        cv2.rectangle(src_img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+        title = CLASSES[classId] + ":%.2f" % (score)
+        cv2.putText(src_img, title, (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2, cv2.LINE_AA)
+
+    cv2.imwrite('./test_rknn_result_cls10.jpg', src_img)
+

onnx2rknn/onnx2rknn_demo_v10n_224x224_sigmoid.py.bak

@@ -0,0 +1,242 @@
+import os
+import urllib
+import traceback
+import time
+import sys
+import numpy as np
+import cv2
+from rknn.api import RKNN
+from math import exp
+
+
+ONNX_MODEL = '../ckpts/best_gift_v10n_rk.onnx'
+#RKNN_MODEL = './rk21b/best_0375.rknn'
+RKNN_MODEL = '../ckpts/best_gift_v10n_rk.rknn'
+#RKNN_MODEL = './rk23b/best_v10s_width0375_1205.rknn'
+
+DATASET = './datasets_gift.txt'
+
+QUANTIZE_ON = True
+
+
+
+CLASSES = ['tag', 'bandage']
+
+meshgrid = []
+
+class_num = len(CLASSES)
+head_num = 3
+strides = [8, 16, 32]
+map_size = [[28, 28], [14, 14], [7, 7]]
+object_thresh = 0.3
+
+input_height = 224
+input_width = 224
+
+topK = 50
+
+
+class DetectBox:
+    def __init__(self, classId, score, xmin, ymin, xmax, ymax):
+        self.classId = classId
+        self.score = score
+        self.xmin = xmin
+        self.ymin = ymin
+        self.xmax = xmax
+        self.ymax = ymax
+
+
+def GenerateMeshgrid():
+    for index in range(head_num):
+        for i in range(map_size[index][0]):
+            for j in range(map_size[index][1]):
+                meshgrid.append(j + 0.5)
+                meshgrid.append(i + 0.5)
+
+
+def TopK(detectResult):
+    if len(detectResult) <= topK:
+        return detectResult
+    else:
+        predBoxs = []
+        sort_detectboxs = sorted(detectResult, key=lambda x: x.score, reverse=True)
+        for i in range(topK):
+            predBoxs.append(sort_detectboxs[i])
+        return predBoxs
+
+
+def sigmoid(x):
+    return 1 / (1 + exp(-x))
+
+
+def postprocess(out, img_h, img_w):
+    print('postprocess ... ')
+
+    detectResult = []
+    output = []
+    for i in range(len(out)):
+        output.append(out[i].reshape((-1)))
+
+    scale_h = img_h / input_height
+    scale_w = img_w / input_width
+
+    gridIndex = -2
+    cls_index = 0
+    cls_max = 0
+
+    for index in range(head_num):
+        reg = output[index * 2 + 0]
+        cls = output[index * 2 + 1]
+
+        for h in range(map_size[index][0]):
+            for w in range(map_size[index][1]):
+                gridIndex += 2
+
+                if 1 == class_num:
+                    # cls_max = sigmoid(cls[0 * map_size[index][0] * map_size[index][1] + h * map_size[index][1] + w])
+                    cls_max = cls[0 * map_size[index][0] * map_size[index][1] + h * map_size[index][1] + w]
+                    cls_index = 0
+                else:
+                    for cl in range(class_num):
+                        cls_val = cls[cl * map_size[index][0] * map_size[index][1] + h * map_size[index][1] + w]
+                        if 0 == cl:
+                            cls_max = cls_val
+                            cls_index = cl
+                        else:
+                            if cls_val > cls_max:
+                                cls_max = cls_val
+                                cls_index = cl
+                    # cls_max = sigmoid(cls_max)
+                    cls_max = cls_max
+
+                if cls_max > object_thresh:
+                    # print(cls_max)
+                    regdfl = []
+                    for lc in range(4):
+                        sfsum = 0
+                        locval = 0
+                        for df in range(16):  # 16
+                            temp = exp(reg[((lc * 16) + df) * map_size[index][0] * map_size[index][1] + h * map_size[index][1] + w])
+                            reg[((lc * 16) + df) * map_size[index][0] * map_size[index][1] + h * map_size[index][ 1] + w] = temp
+                            sfsum += temp
+    #
+                        for df in range(16):  # 16
+                            sfval = reg[((lc * 16) + df) * map_size[index][0] * map_size[index][1] + h * map_size[index][
+                                1] + w] / sfsum
+                            locval += sfval * df
+                        regdfl.append(locval)
+    #
+                    x1 = (meshgrid[gridIndex + 0] - regdfl[0]) * strides[index]
+                    y1 = (meshgrid[gridIndex + 1] - regdfl[1]) * strides[index]
+                    x2 = (meshgrid[gridIndex + 0] + regdfl[2]) * strides[index]
+                    y2 = (meshgrid[gridIndex + 1] + regdfl[3]) * strides[index]
+
+                    xmin = x1 * scale_w
+                    ymin = y1 * scale_h
+                    xmax = x2 * scale_w
+                    ymax = y2 * scale_h
+
+                    xmin = xmin if xmin > 0 else 0
+                    ymin = ymin if ymin > 0 else 0
+                    xmax = xmax if xmax < img_w else img_w
+                    ymax = ymax if ymax < img_h else img_h
+
+                    box = DetectBox(cls_index, cls_max, xmin, ymin, xmax, ymax)
+                    detectResult.append(box)
+    # topK
+    print('before topK num is:', len(detectResult))
+    predBox = TopK(detectResult)
+
+    return predBox
+
+
+
+def export_rknn_inference(img):
+    # 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]], quantized_algorithm='normal', quantized_method='channel', target_platform='rk3588')
+    print('done')
+
+    # Load ONNX model
+    print('--> Loading model')
+    ret = rknn.load_onnx(model=ONNX_MODEL)
+    if ret != 0:
+        print('Load model failed!')
+        exit(ret)
+    print('done')
+
+    # Build model
+    print('--> Building model')
+    ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET, rknn_batch_size=1)
+    if ret != 0:
+        print('Build model failed!')
+        exit(ret)
+    print('done')
+
+    # Export RKNN model
+    print('--> Export rknn model')
+    ret = rknn.export_rknn(RKNN_MODEL)
+    if ret != 0:
+        print('Export rknn model failed!')
+        exit(ret)
+    print('done')
+
+    # Init runtime environment
+    print('--> Init runtime environment')
+    ret = rknn.init_runtime()
+    # ret = rknn.init_runtime(target='rk3566')
+    if ret != 0:
+        print('Init runtime environment failed!')
+        exit(ret)
+    print('done')
+
+    # Inference
+    print('--> Running model')
+    outputs = rknn.inference(inputs=[img])
+    rknn.release()
+    print('done')
+
+    return outputs
+
+
+if __name__ == '__main__':
+    print('This is main ...')
+    GenerateMeshgrid()
+
+    img_path = './test/1.jpg'
+    src_img = cv2.imread(img_path)
+    img_h, img_w = src_img.shape[:2]
+
+
+    input_img = cv2.resize(src_img, (input_width, input_height))
+    input_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)
+
+    input_img = np.expand_dims(input_img, 0)
+
+    outputs = export_rknn_inference(input_img)
+
+    out = []
+    for i in range(len(outputs)):
+        out.append(outputs[i])
+
+    predbox = postprocess(out, img_h, img_w)
+
+    print(len(predbox))
+
+    for i in range(len(predbox)):
+        xmin = int(predbox[i].xmin)
+        ymin = int(predbox[i].ymin)
+        xmax = int(predbox[i].xmax)
+        ymax = int(predbox[i].ymax)
+        classId = predbox[i].classId
+        score = predbox[i].score
+
+        cv2.rectangle(src_img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+        title = CLASSES[classId] + ":%.2f" % (score)
+        cv2.putText(src_img, title, (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2, cv2.LINE_AA)
+
+    cv2.imwrite('./test_rknn_result_cls10.jpg', src_img)
+