当使用rknn_inputs_set(pass_through=1)和 rknn_inputs_map 时,表明在 NPU 推理之前的流程要用户处理。rknn_outputs_map获取输出后,用户也要做反量化得到 32位浮点结果。 量化和反量化用到的量化方式、量化数据类型以及量化参数,可以通过 rknn_query 接口查询。目前,RK1808/RK3399Pro/RV1109/RV1126 (EASY EAI...
然后说下add_image_preprocess_layer,EASY-EAI说能提升rknn_input_set的速度,但一般都会用零拷贝也就是map系的操作吧?开了这个选项的话他的输入会从1x3x416x416(NCHW)变成1x416x416x3(NHWC),然后在input后面加一层[0, 3, 1, 2]的Transpose。后面的处理没有变动,但rknn-toolkit转模型的时候报错,rknn-toolki...
np.ndarray), ('heatmaps should be numpy.ndarray') assert heatmaps.ndim == 4, 'batch_images should be 4-ndim' N, K, _, W = heatmaps.shape heatmaps_reshaped = heatmaps.reshape((N, K, -1)) idx = np.argmax(heatmaps_reshaped, 2).reshape((N, K, 1)) maxvals = np.ama...
len(CLASSES)headNum=3strides=[8,16,32]mapSize=[[80,80],[40,40],[20,20]]nmsThresh=0.5objectThresh=0.6input_imgH=640input_imgW=640classDetectBox:def__init__(self,classId,score,xmin,ymin,xmax,ymax,head):self.classId=classIdself.score=scoreself.xmin=xminself.ymin=yminself.xmax=xma...
⚫ inputs :定义算子输入,每个输入 名需要不同,每个输入项的配置只需要填写 type 。 ⚫ outputs :定义算子输出,每个输出 名需要不同,每个输入项的配置只需要填写 type 。 ⚫ params :定义算子参数,每个参数 名需要不同,每个参数项的配置只需填写 type (type 支持VX_TYPE_ARRAY 和标量类型,标量类型具体...
def process(input, mask, anchors): anchors = [anchors[i] for i in mask] grid_h, grid_w = map(int, input.shape[0:2]) box_confidence = sigmoid(input[..., 4]) box_confidence = np.expand_dims(box_confidence, axis=-1) box_class_probs = sigmoid(input[..., 5:]) box_xy = ...
-Lbuild/temp.linux-aarch64-3.6 -llapack -llapack -lblas -lblas -lpython3.6m -lgfortran -o build/lib.linux-aarch64-3.6/scipy/optimize/_trlib/_trlib.cpython-36m-aarch64-linux-gnu.so -Wl,--version-script=build/temp.linux-aarch64-3.6/link-version-scipy.optimize._trlib._trlib.map ...
label_map_path: "data/pill_case_label_map.txt" } eval_config { num_examples: 500 max_evals: 10 use_moving_averages: false } eval_input_reader { tf_record_input_reader { input_path: "data/pill_case.tfrecord" } label_map_path: "data/pill_case_label_map.txt" shu...
时都按R,G,B输入)rknn.configreorder_channel参数,’012’RGB’210’代表BGR,务必和训练时候图像通道顺序一致 在rknn.config中设置batch_size参数(建议设置batch_size=200)并且在dataset.txt中给出大于200 如果显存不够,可以设置batch_size1,epochs=200batch_size200top-1,top-5精度,检测网络比较数据集的mAP,...
weights_dict = torch.load(self.opt.weights_path,map_location=self.device) weights_dict = {k:v for k,v in weights_dict.items() if k in model_dict and np.shape(model_dict[k])==np.shape(v)} model_dict.update(weights_dict)