28 agnostic=self.args.agnostic_nms, 29 max_det=self.args.max_det, 30 classes=self.args.classes) 32 if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list 33 orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) ...
'pre_nms_top_n': Dict[str, int], 'post_nms_top_n': Dict[str, int], } def __init__(self, anchor_generator, head, # fg_iou_thresh, bg_iou_thresh, batch_size_per_image, positive_fraction, # pre_nms_top_n, post_nms_top_n, nms_thresh): ...
🐛 Describe the bug Torchvision ops are not loaded properly, import torchvision import torch class M(torch.nn.Module): def __init__(self): super().__init__() pass def forward(self, x, count): out = torchvision.ops.batched_nms(x[0], x[1], ...
所有好我删除CUDA和安装。和所有的工作，如果SOEM需要我的代码：来自ultralytics import YOLO ifname== ...
之前的文章目标检测|SSD原理与实现已经详细介绍了SSD检测算法的原理以及实现,不过里面只给出了inference的代码,这个更新版基于SSD的torchvision版本从代码实现的角度对SSD的各个部分给出深入的解读(包括数据增强,训练)。 特征提取器(Backbone Feature Extractor)
_log_api_usage_once("torchvision.ops.boxes", "batched_nms") In this case, I would rather we use single string as the param for _log_api_usage_once _log_api_usage_once("torchvision.ops.boxes.batched_nms") For class, it could be _log_api_usage_once(self.__class__.__qualname...
🐛 Bug I am converting the model into FP16. Using torch.cuda.amp.autocast. But it throws me an error: ~/Documents/test/seg/models/archs/mask_rcnn.py in mixed_precision_one_batch(self, i, b) 186 with autocast(): 187 self.model.train() --> ...
For example, The snippet above can be replaced with the one below, which uses a batched concat. I confirmed that ONNX export of maskrcnn works with this change applied. This is also what TVM prefers. @torch.jit._script_if_tracing def _onnx_paste_masks_in_image_loop(masks, boxes, ...