Default: ('filename', 'ori_shape', 'img_shape', 'lidar2img', 'depth2img', 'cam2img', 'pad_shape', 'scale_factor', 'flip', 'pcd_horizontal_flip', 'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d', 'img_norm_cfg', 'pcd_trans', 'sample_idx', 'pcd_scale_factor', 'pc...
img_metas, # img_metas (list[dict]): 每个batch image的输入一些参数信息: 'img_shape', 'scale_factor', 'flip', # 还有可能包含'filename', 'ori_shape', 'pad_shape','img_norm_cfg'等 # 因为一般在数据增强时,每个batch的图像会用随机的剪裁比例,因此需要保存以上数据方便归一化。 gt_bboxes,...
interpolate(tmp, scale_factor=2) tmp = torch.cat((tmp, x), 1) detect = getattr(self, f'detect{i+2}') out = detect(tmp) outs.append(out) return tuple(outs) DetectionBlock DetectionBlock由六个卷积层组成1x1xn, 3x3x2n, 1x1xn, 3x3x2n, 1x1xn, 3x3x2n,第一个DetectionBlock的第...
本篇是MMdet逐行解读第五篇。从本篇开始介绍mmdet/models文件夹内容。首先介绍最常用的resnet50+fpn结构。 1、ResNet50 1.1. 构建一个resnet50 # 骨架网络构建 frommmdet.modelsimportbuild_backbone if__name__=='__main__': backbone=dict( type='ResNet', depth=50, num_stages=4, out_ind...
note that the bbox inference result did not divided by scale factor, divided by you self if needed.read demo/inference.py for more detailSupport Model/ModuleFaster R-CNN Cascade R-CNN Double-Head R-CNN Group Normalization Weight Standardization DCN SSD RetinaNet Libra R-CNN FCOS Fovea CARAFE ...
note that the bbox inference result did not divided by scale factor, divided by you self if needed. play demo in demo/inference.py getting_started.mdfor more detail How does it works? Most other project use pytorch=>ONNX=>tensorRT route, This repo convert pytorch=>tensorRT directly, avoid...
max_short_edge = min(img_scale)# 取值方式: 大值/长边 小值/短边 谁的比值小 按谁来计算缩放比例 scale_factor = min(max_long_edge / max(h, w), max_short_edge / min(h, w)) 假设我的真实图片大小是(400, 600),那么按照上面的方式1333/600 = 2.22, 800/400=2,显然,按照800的缩放系数...
process. At the same time, we provide the `dynamic_scale` parameter to dynamically change the output image size. Args: dataset (:obj:`CustomDataset`): The dataset to be mixed. pipeline (Sequence[dict]): Sequence of transform object or config dict to be composed. dynamic_scale (tuple...
本篇是MMdet逐行解读第五篇。从本篇开始介绍mmdet/models文件夹内容。首先介绍最常用的resnet50+fpn结构。 1、ResNet50 1.1. 构建一个resnet50 # 骨架网络构建 frommmdet.modelsimportbuild_backbone if__name__=='__main__': backbone=dict( ...
scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.2, patch_norm=True, out_indices=(1, 2, 3), with_cp=False, convert_weights=True, init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)), neck=dict( type='YOLOv5PAFPN', deepen_factor=deepen_factor, widen_factor...