importnumpyasnpimporttensorrtastrtimportonnxruntimeimportpycuda.driverascudaimportpycuda.autoinitdefnormalize(image:np.ndarray)->np.ndarray:"""Normalize the image to the given mean and standard deviation"""image=image.astype(np.float32)mean=(0.485,0.456,0.406)std=(0.229,0.224,0.225)image/=255.0im...
[0]) # Reshape to a two dimentional ndarray pred = np.reshape(output[1:], (-1, LEN_ONE_RESULT))[:num, :] pred = pred[:, :6] # Do nms boxes = self.non_max_suppression(pred, origin_h, origin_w, conf_thres=CONF_THRESH, nms_thres=IOU_THRESHOLD) result_boxes = boxes[:, :...
test_set = img_xml_union[int(len(img_xml_union) * (1- test_size)):]returntrain_set, test_setdefconvert_annotation(img_xml_set, classes, save_path, is_train=True): os.makedirs(os.path.join(save_path,'images','train'ifis_trainelse'val'), exist_ok=True) img_root = os.path.joi...
value,_enums.dtype._from(dtype).to(np.dtype)ifdtypeisnotNoneelseNone can make the compilation succeed. But I'm not sure if you'd like to solve it in the other way. HolyWuadded thebugSomething isn't workinglabelJun 9, 2024 narendasanmentioned this issueJul 1, 2024 ...
result_classid: finally classid, a tensor, each element is the classid correspoing to box """ # Get the num of boxes detected num = int(output[0]) # Reshape to a two dimentional ndarray pred = np.reshape(output[1:], (-1, 6))[:num, :] ...
搞了一下Jetson nano和YOLOv5,网上的资料大多重复也有许多的坑,在配置过程中摸爬滚打了好几天,出坑后决定写下这份教程供自己备忘。 事先声明,这篇文章的许多内容本身并不是原创,而是将配置过程中的文献进行了搜集整理,但是所有步骤都1:1复刻我的配置过程,包括其中的出错和解决途径,但是每个人的设备和网络上的包...
scores: ndarray, labels: ndarray, iou_thres: float, agnostic: bool = False): num_boxes = boxes.shape[0] order = np.argsort(scores)[::-1] boxes = boxes[order] @@ -135,7 +137,7 @@ def det_postprocess(data: Tuple[ndarray, ndarray, ndarray, ndarray]): ...
get_bias_for_gate(self: tensorrt.tensorrt.IRNNv2Layer, layer_index: int, gate: tensorrt.tensorrt.RNNGateType, is_w: bool) → numpy.ndarray Get the bias parameters for an individual gate in the RNN. Parameters layer_index –The index of the layer that contains this gate. gate –The...
result_classid: finally classid, a tensor, each element is the classid correspoing to box """ # Get the num of boxes detected num = int(output[0]) # Reshape to a two dimentional ndarray pred = np.reshape(output[1:], (-1, 6))[:num, :] ...
It is an error to set this on an RNN layer that is not configured with RNNOperation.LSTM . get_bias_for_gate(self: tensorrt.tensorrt.IRNNv2Layer, layer_index: int, gate: tensorrt.tensorrt.RNNGateType, is_w: bool) → numpy.ndarray Get the bias parameters for an individual gate in...