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bevdet_changes.patch
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bevdet_changes.patch
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diff --git a/configs/bevdepth/bevdepth-r50-adv.py b/configs/bevdepth/bevdepth-r50-adv.py
new file mode 100644
index 0000000..250c49c
--- /dev/null
+++ b/configs/bevdepth/bevdepth-r50-adv.py
@@ -0,0 +1,267 @@
+# Copyright (c) Phigent Robotics. All rights reserved.
+
+_base_ = ['../_base_/datasets/nus-3d.py',
+ '../_base_/default_runtime.py']
+# Global
+# If point cloud range is changed, the models should also change their point
+# cloud range accordingly
+point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
+# For nuScenes we usually do 10-class detection
+class_names = [
+ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
+ 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
+]
+
+data_config={
+ 'cams': ['CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT',
+ 'CAM_BACK_LEFT', 'CAM_BACK', 'CAM_BACK_RIGHT'],
+ 'Ncams': 6,
+ 'input_size': (256, 704),
+ 'src_size': (900, 1600),
+
+ # Augmentation
+ 'resize': (-0.06, 0.11),
+ 'rot': (-5.4, 5.4),
+ 'flip': True,
+ 'crop_h': (0.0, 0.0),
+ 'resize_test':0.04,
+}
+
+# Model
+grid_config={
+ 'xbound': [-51.2, 51.2, 0.8],
+ 'ybound': [-51.2, 51.2, 0.8],
+ 'zbound': [-10.0, 10.0, 20.0],
+ 'dbound': [1.0, 60.0, 1.0],}
+
+voxel_size = [0.1, 0.1, 0.2]
+
+numC_Trans=64
+
+model = dict(
+ type='BEVDepth',
+ img_backbone=dict(
+ pretrained='torchvision://resnet50',
+ type='ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(2, 3),
+ frozen_stages=-1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ norm_eval=False,
+ with_cp=True,
+ style='pytorch'),
+ img_neck=dict(
+ type='FPNForBEVDet',
+ in_channels=[1024, 2048],
+ out_channels=512,
+ num_outs=1,
+ start_level=0,
+ out_ids=[0]),
+ img_view_transformer=dict(type='ViewTransformerLSSBEVDepth',
+ loss_depth_weight=100.0,
+ grid_config=grid_config,
+ data_config=data_config,
+ numC_Trans=numC_Trans,
+ extra_depth_net=dict(type='ResNetForBEVDet',
+ numC_input=256,
+ num_layer=[3,],
+ num_channels=[256,],
+ stride=[1,])),
+ img_bev_encoder_backbone = dict(type='ResNetForBEVDet', numC_input=numC_Trans),
+ img_bev_encoder_neck = dict(type='FPN_LSS',
+ in_channels=numC_Trans*8+numC_Trans*2,
+ out_channels=256),
+ pts_bbox_head=dict(
+ type='CenterHead',
+ task_specific=True,
+ in_channels=256,
+ tasks=[
+ dict(num_class=1, class_names=['car']),
+ dict(num_class=2, class_names=['truck', 'construction_vehicle']),
+ dict(num_class=2, class_names=['bus', 'trailer']),
+ dict(num_class=1, class_names=['barrier']),
+ dict(num_class=2, class_names=['motorcycle', 'bicycle']),
+ dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),
+ ],
+ common_heads=dict(
+ reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
+ share_conv_channel=64,
+ bbox_coder=dict(
+ type='CenterPointBBoxCoder',
+ pc_range=point_cloud_range[:2],
+ post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
+ max_num=500,
+ score_threshold=0.1,
+ out_size_factor=8,
+ voxel_size=voxel_size[:2],
+ code_size=9),
+ separate_head=dict(
+ type='SeparateHead', init_bias=-2.19, final_kernel=3),
+ loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
+ loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
+ norm_bbox=True),
+ # model training and testing settings
+ train_cfg=dict(
+ pts=dict(
+ point_cloud_range=point_cloud_range,
+ grid_size=[1024, 1024, 40],
+ voxel_size=voxel_size,
+ out_size_factor=8,
+ dense_reg=1,
+ gaussian_overlap=0.1,
+ max_objs=500,
+ min_radius=2,
+ code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2])),
+ test_cfg=dict(
+ pts=dict(
+ pc_range=point_cloud_range[:2],
+ post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
+ max_per_img=500,
+ max_pool_nms=False,
+ min_radius=[4, 12, 10, 1, 0.85, 0.175],
+ score_threshold=0.1,
+ out_size_factor=8,
+ voxel_size=voxel_size[:2],
+ # nms_type='circle',
+ pre_max_size=1000,
+ post_max_size=83,
+ # nms_thr=0.2,
+
+ # Scale-NMS
+ nms_type=['rotate', 'rotate', 'rotate', 'circle', 'rotate', 'rotate'],
+ nms_thr=[0.2, 0.2, 0.2, 0.2, 0.2, 0.5],
+ nms_rescale_factor=[1.0, [0.7, 0.7], [0.4, 0.55], 1.1, [1.0, 1.0], [4.5, 9.0]]
+ )))
+
+
+# Data
+dataset_type = 'NuScenesDataset'
+data_root = 'data/nuscenes/'
+file_client_args = dict(backend='disk')
+
+
+train_pipeline = [
+ dict(type='LoadMultiViewImageFromFiles_BEVDet', is_train=True, data_config=data_config),
+ dict(
+ type='LoadPointsFromFile',
+ coord_type='LIDAR',
+ load_dim=5,
+ use_dim=5,
+ file_client_args=file_client_args),
+ dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+ dict(
+ type='GlobalRotScaleTrans',
+ rot_range=[-0.3925, 0.3925],
+ scale_ratio_range=[0.95, 1.05],
+ translation_std=[0, 0, 0],
+ update_img2lidar=True),
+ dict(
+ type='RandomFlip3D',
+ sync_2d=False,
+ flip_ratio_bev_horizontal=0.5,
+ flip_ratio_bev_vertical=0.5,
+ update_img2lidar=True),
+ dict(type='PointToMultiViewDepth', grid_config=grid_config),
+ dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+ dict(type='ObjectNameFilter', classes=class_names),
+ dict(type='DefaultFormatBundle3D', class_names=class_names),
+ dict(type='Collect3D', keys=['img_inputs', 'gt_bboxes_3d', 'gt_labels_3d'],
+ meta_keys=('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', 'pcd_rotation', 'pts_filename',
+ 'transformation_3d_flow', 'img_info'))
+]
+
+test_pipeline = [
+ dict(type='LoadMultiViewImageFromFiles_BEVDet', data_config=data_config),
+ # load lidar points for --show in test.py only
+ dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+ dict(
+ type='LoadPointsFromFile',
+ coord_type='LIDAR',
+ load_dim=5,
+ use_dim=5,
+ file_client_args=file_client_args),
+ dict(type='PointToMultiViewDepth', grid_config=grid_config),
+ dict(
+ type='MultiScaleFlipAug3D',
+ img_scale=(1333, 800),
+ pts_scale_ratio=1,
+ flip=False,
+ transforms=[
+ dict(
+ type='DefaultFormatBundle3D',
+ class_names=class_names,
+ with_label=False),
+ dict(type='Collect3D', keys=['points','img_inputs', 'gt_bboxes_3d', 'gt_labels_3d'],
+ meta_keys=('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', 'pcd_rotation', 'pts_filename',
+ 'transformation_3d_flow', 'img_info', 'imgs_org'))
+ ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+ dict(type='LoadMultiViewImageFromFiles_BEVDet', data_config=data_config),
+ dict(
+ type='LoadPointsFromFile',
+ coord_type='LIDAR',
+ load_dim=5,
+ use_dim=5,
+ file_client_args=file_client_args),
+ dict(type='PointToMultiViewDepth', grid_config=grid_config),
+ dict(
+ type='DefaultFormatBundle3D',
+ class_names=class_names,
+ with_label=False),
+ dict(type='Collect3D', keys=['img_inputs'])
+]
+
+input_modality = dict(
+ use_lidar=False,
+ use_camera=True,
+ use_radar=False,
+ use_map=False,
+ use_external=False)
+
+data = dict(
+ samples_per_gpu=8,
+ workers_per_gpu=4,
+ train=dict(
+ type='CBGSDataset',
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ ann_file=data_root + 'nuscenes_infos_train.pkl',
+ pipeline=train_pipeline,
+ classes=class_names,
+ test_mode=False,
+ use_valid_flag=True,
+ modality=input_modality,
+ # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+ # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+ box_type_3d='LiDAR',
+ img_info_prototype='bevdet')),
+ val=dict(pipeline=test_pipeline, classes=class_names,
+ modality=input_modality, img_info_prototype='bevdet'),
+ test=dict(pipeline=test_pipeline, classes=class_names,
+ modality=input_modality, img_info_prototype='bevdet'))
+
+# Optimizer
+optimizer = dict(type='AdamW', lr=2e-4, weight_decay=0.01)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=500,
+ warmup_ratio=0.001,
+ step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
\ No newline at end of file
diff --git a/configs/bevdet/bevdet-r50-adv.py b/configs/bevdet/bevdet-r50-adv.py
new file mode 100644
index 0000000..2d1e394
--- /dev/null
+++ b/configs/bevdet/bevdet-r50-adv.py
@@ -0,0 +1,253 @@
+# Copyright (c) Phigent Robotics. All rights reserved.
+
+_base_ = ['../_base_/datasets/nus-3d.py',
+ '../_base_/default_runtime.py']
+# Global
+# If point cloud range is changed, the models should also change their point
+# cloud range accordingly
+point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
+# For nuScenes we usually do 10-class detection
+class_names = [
+ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
+ 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
+]
+
+data_config={
+ 'cams': ['CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT',
+ 'CAM_BACK_LEFT', 'CAM_BACK', 'CAM_BACK_RIGHT'],
+ 'Ncams': 6,
+ 'input_size': (256, 704),
+ 'src_size': (900, 1600),
+
+ # Augmentation
+ 'resize': (-0.06, 0.11),
+ 'rot': (-5.4, 5.4),
+ 'flip': True,
+ 'crop_h': (0.0, 0.0),
+ 'resize_test':0.04,
+}
+
+# Model
+grid_config={
+ 'xbound': [-51.2, 51.2, 0.8],
+ 'ybound': [-51.2, 51.2, 0.8],
+ 'zbound': [-10.0, 10.0, 20.0],
+ 'dbound': [1.0, 60.0, 1.0],}
+
+voxel_size = [0.1, 0.1, 0.2]
+
+numC_Trans=64
+
+model = dict(
+ type='BEVDet',
+ img_backbone=dict(
+ pretrained='torchvision://resnet50',
+ type='ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(2, 3),
+ frozen_stages=-1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ norm_eval=False,
+ with_cp=True,
+ style='pytorch'),
+ img_neck=dict(
+ type='FPNForBEVDet',
+ in_channels=[1024, 2048],
+ out_channels=512,
+ num_outs=1,
+ start_level=0,
+ out_ids=[0]),
+ img_view_transformer=dict(type='ViewTransformerLiftSplatShoot',
+ grid_config=grid_config,
+ data_config=data_config,
+ numC_Trans=numC_Trans),
+ img_bev_encoder_backbone = dict(type='ResNetForBEVDet', numC_input=numC_Trans),
+ img_bev_encoder_neck = dict(type='FPN_LSS',
+ in_channels=numC_Trans*8+numC_Trans*2,
+ out_channels=256),
+ pts_bbox_head=dict(
+ type='CenterHead',
+ in_channels=256,
+ tasks=[
+ dict(num_class=1, class_names=['car']),
+ dict(num_class=2, class_names=['truck', 'construction_vehicle']),
+ dict(num_class=2, class_names=['bus', 'trailer']),
+ dict(num_class=1, class_names=['barrier']),
+ dict(num_class=2, class_names=['motorcycle', 'bicycle']),
+ dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),
+ ],
+ common_heads=dict(
+ reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
+ share_conv_channel=64,
+ bbox_coder=dict(
+ type='CenterPointBBoxCoder',
+ pc_range=point_cloud_range[:2],
+ post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
+ max_num=500,
+ score_threshold=0.1,
+ out_size_factor=8,
+ voxel_size=voxel_size[:2],
+ code_size=9),
+ separate_head=dict(
+ type='SeparateHead', init_bias=-2.19, final_kernel=3),
+ loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
+ loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
+ norm_bbox=True),
+ # model training and testing settings
+ train_cfg=dict(
+ pts=dict(
+ point_cloud_range=point_cloud_range,
+ grid_size=[1024, 1024, 40],
+ voxel_size=voxel_size,
+ out_size_factor=8,
+ dense_reg=1,
+ gaussian_overlap=0.1,
+ max_objs=500,
+ min_radius=2,
+ code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2])),
+ test_cfg=dict(
+ pts=dict(
+ pc_range=point_cloud_range[:2],
+ post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
+ max_per_img=500,
+ max_pool_nms=False,
+ min_radius=[4, 12, 10, 1, 0.85, 0.175],
+ score_threshold=0.1,
+ out_size_factor=8,
+ voxel_size=voxel_size[:2],
+ # nms_type='circle',
+ pre_max_size=1000,
+ post_max_size=83,
+ # nms_thr=0.2,
+
+ # Scale-NMS
+ nms_type=['rotate', 'rotate', 'rotate', 'circle', 'rotate', 'rotate'],
+ nms_thr=[0.2, 0.2, 0.2, 0.2, 0.2, 0.5],
+ nms_rescale_factor=[1.0, [0.7, 0.7], [0.4, 0.55], 1.1, [1.0, 1.0], [4.5, 9.0]]
+ )))
+
+
+# Data
+dataset_type = 'NuScenesDataset'
+data_root = 'data/nuscenes/'
+file_client_args = dict(backend='disk')
+
+
+train_pipeline = [
+ dict(type='LoadMultiViewImageFromFiles_BEVDet', is_train=True, data_config=data_config),
+ dict(
+ type='LoadPointsFromFile',
+ dummy=True,
+ coord_type='LIDAR',
+ load_dim=5,
+ use_dim=5,
+ file_client_args=file_client_args),
+ dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+ dict(
+ type='GlobalRotScaleTrans',
+ rot_range=[-0.3925, 0.3925],
+ scale_ratio_range=[0.95, 1.05],
+ translation_std=[0, 0, 0],
+ update_img2lidar=True),
+ dict(
+ type='RandomFlip3D',
+ sync_2d=False,
+ flip_ratio_bev_horizontal=0.5,
+ flip_ratio_bev_vertical=0.5,
+ update_img2lidar=True),
+ dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+ dict(type='ObjectNameFilter', classes=class_names),
+ dict(type='DefaultFormatBundle3D', class_names=class_names),
+ dict(type='Collect3D', keys=['img_inputs', 'gt_bboxes_3d', 'gt_labels_3d'],
+ meta_keys=('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', 'pcd_rotation', 'pts_filename',
+ 'transformation_3d_flow', 'img_info'))
+]
+
+test_pipeline = [
+ dict(type='LoadMultiViewImageFromFiles_BEVDet', data_config=data_config),
+ # load lidar points for --show in test.py only
+ dict(
+ type='LoadPointsFromFile',
+ coord_type='LIDAR',
+ load_dim=5,
+ use_dim=5,
+ file_client_args=file_client_args),
+ dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+
+ dict(
+ type='MultiScaleFlipAug3D',
+ img_scale=(1333, 800),
+ pts_scale_ratio=1,
+ flip=False,
+ transforms=[
+ dict(
+ type='DefaultFormatBundle3D',
+ class_names=class_names,
+ with_label=False),
+ dict(type='Collect3D', keys=['points','img_inputs', 'gt_bboxes_3d', 'gt_labels_3d'],
+ meta_keys=('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', 'pcd_rotation', 'pts_filename',
+ 'transformation_3d_flow', 'img_info', 'imgs_org'))
+ ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+ dict(type='LoadMultiViewImageFromFiles_BEVDet', data_config=data_config),
+ dict(
+ type='DefaultFormatBundle3D',
+ class_names=class_names,
+ with_label=False),
+ dict(type='Collect3D', keys=['img_inputs'])
+]
+
+input_modality = dict(
+ use_lidar=False,
+ use_camera=True,
+ use_radar=False,
+ use_map=False,
+ use_external=False)
+
+data = dict(
+ samples_per_gpu=8,
+ workers_per_gpu=4,
+ train=dict(
+ type='CBGSDataset',
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ ann_file=data_root + 'nuscenes_infos_train.pkl',
+ pipeline=train_pipeline,
+ classes=class_names,
+ test_mode=False,
+ use_valid_flag=True,
+ modality=input_modality,
+ # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+ # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+ box_type_3d='LiDAR',
+ img_info_prototype='bevdet')),
+ val=dict(pipeline=test_pipeline, classes=class_names,
+ modality=input_modality, img_info_prototype='bevdet'),
+ test=dict(pipeline=test_pipeline, classes=class_names,
+ modality=input_modality, img_info_prototype='bevdet'))
+
+# Optimizer
+optimizer = dict(type='AdamW', lr=2e-4, weight_decay=0.01)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=500,
+ warmup_ratio=0.001,
+ step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
\ No newline at end of file
diff --git a/extend/__init__.py b/extend/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/extend/custom_func.py b/extend/custom_func.py
new file mode 100644
index 0000000..523f09c
--- /dev/null
+++ b/extend/custom_func.py
@@ -0,0 +1,97 @@
+# pnly for bevdet bevdepth
+import torch
+import torchvision
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+from torchvision.utils import save_image
+import copy
+
+def custom_data_preprocess(data):
+ for _key in data:
+ data[_key] = data[_key][0]
+ return data
+
+def custom_data_postprocess_eval(data):
+ del data['gt_bboxes_3d']
+ del data['gt_labels_3d']
+ for _key in data:
+ data[_key] = [data[_key]]
+ return data
+
+def custom_data_work(data):
+
+ metas = data['img_metas']._data[0][0]
+ img_path_list = metas['img_info']
+ img_org_np = metas['imgs_org']
+ img_processed = data['img_inputs'][0].clone()
+ gt_labels_3d = data['gt_labels_3d']._data[0][0]
+
+ cams = ['CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT',
+ 'CAM_BACK_LEFT', 'CAM_BACK', 'CAM_BACK_RIGHT']
+
+ img_path_list_new = []
+ for cams_i in cams:
+ img_path = img_path_list[cams_i]['data_path']
+ img_path_list_new.append(img_path)
+
+ return metas, img_path_list_new, img_org_np, img_processed, gt_labels_3d
+
+def custom_result_postprocess(result):
+ return result
+
+
+def custom_img_read_from_img_org(img_org_np, device):
+ img_org_np_255_rgb_hwcn_uint8 = img_org_np # mmcv 读取 BGR 转 numpy
+ list_ = []
+ for i in range(len(img_org_np_255_rgb_hwcn_uint8)):
+ img_org_tensor_rgb_255_hwcn = torch.from_numpy(img_org_np_255_rgb_hwcn_uint8[i]).float()
+ img_org_tensor_rgb_255 = img_org_tensor_rgb_255_hwcn.permute(2,0,1)
+ img_org_tensor_rgb = (img_org_tensor_rgb_255/255.).to(device) # 6chw
+ list_.append(img_org_tensor_rgb)
+ img_tensor_rgb_6chw_0to1 = torch.stack(list_)
+ return img_tensor_rgb_6chw_0to1
+
+
+def custom_differentiable_transform(img_tensor_rgb_6chw_0to1, img_metas):
+ """Alternative Data Preparation for Original Model
+
+ Args:
+ img_tensor (torch.tensor): (6xCxHxW), tensors of original imgs
+ """
+ assert len(img_tensor_rgb_6chw_0to1.shape) == 4
+ # assert img_tensor_rgb_6chw_0to1.shape[0] == 6
+ assert img_tensor_rgb_6chw_0to1.shape[1] == 3
+ assert img_tensor_rgb_6chw_0to1.max() <= 1.
+ assert img_tensor_rgb_6chw_0to1.min() >= 0.
+ assert img_tensor_rgb_6chw_0to1.dtype == torch.float32
+ assert img_tensor_rgb_6chw_0to1.is_cuda
+ #[6,3,900,1600]
+ img_tensor_01 = img_tensor_rgb_6chw_0to1
+
+ device = img_tensor_rgb_6chw_0to1.device
+ mean = [0.485, 0.456, 0.406]
+ std = [0.229, 0.224, 0.225]
+ mean = torch.tensor(mean).to(device)[None,None,:,None,None]
+ std = torch.tensor(std).to(device)[None,None,:,None,None]
+
+ ############ resize norm crop
+ ######## resize
+ img_tensor_255_resize = F.interpolate(img_tensor_01, (432, 768), mode='bilinear', align_corners=False)
+ #crop
+ crop_size = (32, 176, 736, 432)
+ img_tensor_255_resize_crop = img_tensor_255_resize[
+ ...,
+ crop_size[1]:crop_size[3],
+ crop_size[0]:crop_size[2]
+ ].to(device)
+ ######## norm
+ img_tensor_norm = (img_tensor_255_resize_crop - mean)/std
+
+ return img_tensor_norm
+
+
+def custom_image_data_give(data, image_ready):
+ data_copy = copy.deepcopy(data)
+ data_copy['img_inputs'][0] = image_ready
+ return data_copy
+
diff --git a/extend_common b/extend_common
new file mode 120000
index 0000000..0633dd9
--- /dev/null
+++ b/extend_common
@@ -0,0 +1 @@
+../extend_common/
\ No newline at end of file
diff --git a/mmdet3d/apis_common b/mmdet3d/apis_common
new file mode 120000
index 0000000..b44e193
--- /dev/null
+++ b/mmdet3d/apis_common
@@ -0,0 +1 @@
+../../apis_common/
\ No newline at end of file
diff --git a/mmdet3d/datasets/nuscenes_dataset.py b/mmdet3d/datasets/nuscenes_dataset.py
index 3276eb7..dadd816 100644
--- a/mmdet3d/datasets/nuscenes_dataset.py
+++ b/mmdet3d/datasets/nuscenes_dataset.py
@@ -13,6 +13,8 @@ from ..core.bbox import Box3DMode, Coord3DMode, LiDARInstance3DBoxes
from .custom_3d import Custom3DDataset
from .pipelines import Compose
+import time
+import orjson
@DATASETS.register_module()
class NuScenesDataset(Custom3DDataset):
@@ -141,7 +143,7 @@ class NuScenesDataset(Custom3DDataset):
ann_file=ann_file,
pipeline=pipeline,
classes=classes,
- modality=modality,
+ modality=modality, #只使用camera
box_type_3d=box_type_3d,
filter_empty_gt=filter_empty_gt,
test_mode=test_mode)
@@ -162,8 +164,8 @@ class NuScenesDataset(Custom3DDataset):
self.img_info_prototype = img_info_prototype
self.speed_mode = speed_mode
- self.max_interval = max_interval
- self.min_interval = min_interval
+ self.max_interval = max_interval #3
+ self.min_interval = min_interval #0
self.prev_only = prev_only
self.next_only = next_only
self.test_adj = test_adj
@@ -263,6 +265,39 @@ class NuScenesDataset(Custom3DDataset):
lidar2img=lidar2img_rts,
))
elif self.img_info_prototype == 'bevdet':
+ # zzj api add
+
+ image_paths = []
+ lidar2img_rts = []
+
+ need_img_order = ['CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT',
+ 'CAM_BACK_LEFT', 'CAM_BACK', 'CAM_BACK_RIGHT']
+
+ for need_img_name in need_img_order:
+ for cam_type, cam_info in info['cams'].items():
+ if cam_type == need_img_name:
+ image_paths.append(cam_info['data_path'])
+ # obtain lidar to image transformation matrix
+ lidar2cam_r = np.linalg.inv(cam_info['sensor2lidar_rotation'])
+ lidar2cam_t = cam_info[
+ 'sensor2lidar_translation'] @ lidar2cam_r.T
+ lidar2cam_rt = np.eye(4)
+ lidar2cam_rt[:3, :3] = lidar2cam_r.T
+ lidar2cam_rt[3, :3] = -lidar2cam_t
+ intrinsic = cam_info['cam_intrinsic']
+ viewpad = np.eye(4)
+ viewpad[:intrinsic.shape[0], :intrinsic.shape[1]] = intrinsic
+ lidar2img_rt = (viewpad @ lidar2cam_rt.T)
+ lidar2img_rts.append(lidar2img_rt)
+
+ input_dict.update(
+ dict(
+ img_filename=image_paths,
+ lidar2img=lidar2img_rts,
+ ))
+
+
+
input_dict.update(dict(img_info=info['cams']))
elif self.img_info_prototype == 'bevdet_sequential':
if info ['prev'] is None or info['next'] is None:
@@ -286,6 +321,14 @@ class NuScenesDataset(Custom3DDataset):
select_id = min((self.max_interval+self.min_interval)//2,
len(info[adjacent])-1)
info_adj = info[adjacent][select_id]
+ # # zzj add relative_adj_id
+ # if adjacent == 'next':
+ # relative_adj_id = select_id + 1
+ # elif adjacent == 'prev':
+ # relative_adj_id = - (select_id + 1)
+ # else:
+ # relative_adj_id = 'error'
+ # info_adj['relative_adj_id'] = relative_adj_id
else:
if len(info[adjacent])<= self.min_interval:
select_id = len(info[adjacent])-1
@@ -300,7 +343,6 @@ class NuScenesDataset(Custom3DDataset):
curr=info,
adjacent=info_adj,
adjacent_type=adjacent))
-
if not self.test_mode:
annos = self.get_ann_info(index)
input_dict['ann_info'] = annos
@@ -316,6 +358,23 @@ class NuScenesDataset(Custom3DDataset):
input_dict['ann_info']['gt_bboxes_3d'] = LiDARInstance3DBoxes(bbox,
box_dim=bbox.shape[-1],
origin=(0.5, 0.5, 0.0))
+ # zzj add:
+ else:
+ annos = self.get_ann_info(index)
+ input_dict['ann_info'] = annos
+ if self.img_info_prototype == 'bevdet_sequential':
+ bbox = input_dict['ann_info']['gt_bboxes_3d'].tensor
+ if 'abs' in self.speed_mode:
+ bbox[:, 7:9] = bbox[:, 7:9] + torch.from_numpy(info['velo']).view(1,2).to(bbox)
+ if input_dict['adjacent_type'] == 'next' and not self.fix_direction:
+ bbox[:, 7:9] = -bbox[:, 7:9]
+ if 'dis' in self.speed_mode:
+ time = abs(input_dict['timestamp'] - 1e-6 * input_dict['adjacent']['timestamp'])
+ bbox[:, 7:9] = bbox[:, 7:9] * time
+ input_dict['ann_info']['gt_bboxes_3d'] = LiDARInstance3DBoxes(bbox,
+ box_dim=bbox.shape[-1],
+ origin=(0.5, 0.5, 0.0))
+
return input_dict
def get_ann_info(self, index):
@@ -436,8 +495,18 @@ class NuScenesDataset(Custom3DDataset):
mmcv.mkdir_or_exist(jsonfile_prefix)
res_path = osp.join(jsonfile_prefix, 'results_nusc.json')
- print('Results writes to', res_path)
- mmcv.dump(nusc_submissions, res_path)
+
+ # orjson.dumps
+ print('Results writes to', res_path,'by orjson')
+ start = time.time()
+ with open(res_path, "wb") as f:
+ f.write(orjson.dumps(nusc_submissions))
+ print("by orjson in", time.time()-start,'s')
+
+ # mmcv.dump
+ # print('Results writes to', res_path)
+ # mmcv.dump(nusc_submissions, res_path)
+
return res_path
def _evaluate_single(self,
diff --git a/mmdet3d/datasets/pipelines/loading.py b/mmdet3d/datasets/pipelines/loading.py
index 71adbe9..9ccbe69 100644
--- a/mmdet3d/datasets/pipelines/loading.py
+++ b/mmdet3d/datasets/pipelines/loading.py
@@ -217,6 +217,7 @@ class LoadMultiViewImageFromFiles_BEVDet(object):
def get_inputs(self,results, flip=None, scale=None):
imgs = []
+ imgs_org = []
rots = []
trans = []
intrins = []
@@ -227,6 +228,8 @@ class LoadMultiViewImageFromFiles_BEVDet(object):
cam_data = results['img_info'][cam]
filename = cam_data['data_path']
img = Image.open(filename)
+ img_org = np.asarray(img)
+ imgs_org.append(img_org)
post_rot = torch.eye(2)
post_tran = torch.zeros(2)
@@ -259,6 +262,8 @@ class LoadMultiViewImageFromFiles_BEVDet(object):
if not type(results['adjacent']) is list:
filename_adjacent = results['adjacent']['cams'][cam]['data_path']
img_adjacent = Image.open(filename_adjacent)
+ img_adjacent_org = np.asarray(img_adjacent)
+ imgs_org.append(img_adjacent_org)
img_adjacent = self.img_transform_core(img_adjacent,
resize_dims=resize_dims,
crop=crop,
@@ -269,6 +274,8 @@ class LoadMultiViewImageFromFiles_BEVDet(object):
for id in range(len(results['adjacent'])):
filename_adjacent = results['adjacent'][id]['cams'][cam]['data_path']
img_adjacent = Image.open(filename_adjacent)
+ img_adjacent_org = np.asarray(img_adjacent)
+ imgs_org.append(img_adjacent_org)
img_adjacent = self.img_transform_core(img_adjacent,
resize_dims=resize_dims,
crop=crop,
@@ -345,10 +352,12 @@ class LoadMultiViewImageFromFiles_BEVDet(object):
imgs, rots, trans, intrins, post_rots, post_trans = (torch.stack(imgs), torch.stack(rots), torch.stack(trans),
torch.stack(intrins), torch.stack(post_rots),
torch.stack(post_trans))
- return imgs, rots, trans, intrins, post_rots, post_trans
+ return imgs, rots, trans, intrins, post_rots, post_trans, imgs_org
def __call__(self, results):
- results['img_inputs'] = self.get_inputs(results)
+ output_ = self.get_inputs(results)
+ results['img_inputs'] = output_[:-1]
+ results['imgs_org'] = output_[-1]
return results
diff --git a/tools/test_fgsm_img_launcher.py b/tools/test_fgsm_img_launcher.py
new file mode 100644
index 0000000..4010c23
--- /dev/null
+++ b/tools/test_fgsm_img_launcher.py
@@ -0,0 +1,226 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import mmcv
+import os
+import torch
+import warnings
+from mmcv import Config, DictAction
+from mmcv.cnn import fuse_conv_bn
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
+ wrap_fp16_model)
+
+from mmdet3d.apis_common.test_fgsm_img import single_gpu_test
+from mmdet3d.datasets import build_dataloader, build_dataset
+from mmdet3d.models import build_model
+from mmdet.apis import multi_gpu_test, set_random_seed
+from mmdet.datasets import replace_ImageToTensor
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(
+ description='MMDet test (and eval) a model')
+ parser.add_argument('config', help='test config file path')
+ parser.add_argument('checkpoint', help='checkpoint file')
+ parser.add_argument('scattered_result_prefix', help='save scattered_result file dir')
+ parser.add_argument('eps255', help='eps of fgsm in 0-255')
+ parser.add_argument('--out', help='output result file in pickle format')
+ parser.add_argument(
+ '--fuse-conv-bn',
+ action='store_true',
+ help='Whether to fuse conv and bn, this will slightly increase'
+ 'the inference speed')
+ parser.add_argument(
+ '--format-only',
+ action='store_true',
+ help='Format the output results without perform evaluation. It is'
+ 'useful when you want to format the result to a specific format and '
+ 'submit it to the test server')
+ parser.add_argument(
+ '--eval',
+ type=str,
+ nargs='+',
+ help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
+ ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
+ parser.add_argument('--show', action='store_true', help='show results')
+ parser.add_argument(
+ '--show-dir', help='directory where results will be saved')
+ parser.add_argument(
+ '--gpu-collect',
+ action='store_true',
+ help='whether to use gpu to collect results.')
+ parser.add_argument(
+ '--tmpdir',
+ help='tmp directory used for collecting results from multiple '
+ 'workers, available when gpu-collect is not specified')
+ parser.add_argument('--seed', type=int, default=0, help='random seed')
+ parser.add_argument(
+ '--deterministic',
+ action='store_true',
+ help='whether to set deterministic options for CUDNN backend.')
+ parser.add_argument(
+ '--cfg-options',
+ nargs='+',
+ action=DictAction,
+ help='override some settings in the used config, the key-value pair '
+ 'in xxx=yyy format will be merged into config file. If the value to '
+ 'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+ 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+ 'Note that the quotation marks are necessary and that no white space '
+ 'is allowed.')
+ parser.add_argument(
+ '--options',
+ nargs='+',
+ action=DictAction,
+ help='custom options for evaluation, the key-value pair in xxx=yyy '
+ 'format will be kwargs for dataset.evaluate() function (deprecate), '
+ 'change to --eval-options instead.')
+ parser.add_argument(
+ '--eval-options',
+ nargs='+',
+ action=DictAction,
+ help='custom options for evaluation, the key-value pair in xxx=yyy '
+ 'format will be kwargs for dataset.evaluate() function')
+ parser.add_argument(
+ '--launcher',
+ choices=['none', 'pytorch', 'slurm', 'mpi'],
+ default='none',
+ help='job launcher')
+ parser.add_argument('--local_rank', type=int, default=0)
+ args = parser.parse_args()
+ if 'LOCAL_RANK' not in os.environ:
+ os.environ['LOCAL_RANK'] = str(args.local_rank)
+
+ if args.options and args.eval_options:
+ raise ValueError(
+ '--options and --eval-options cannot be both specified, '
+ '--options is deprecated in favor of --eval-options')
+ if args.options:
+ warnings.warn('--options is deprecated in favor of --eval-options')
+ args.eval_options = args.options
+ return args
+
+
+def main():
+ args = parse_args()
+
+ # assert args.out or args.eval or args.format_only or args.show \
+ # or args.show_dir, \
+ # ('Please specify at least one operation (save/eval/format/show the '
+ # 'results / save the results) with the argument "--out", "--eval"'
+ # ', "--format-only", "--show" or "--show-dir"')
+
+ if args.eval and args.format_only:
+ raise ValueError('--eval and --format_only cannot be both specified')
+
+ if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
+ raise ValueError('The output file must be a pkl file.')
+
+ cfg = Config.fromfile(args.config)
+ if args.cfg_options is not None:
+ cfg.merge_from_dict(args.cfg_options)
+ # import modules from string list.
+ if cfg.get('custom_imports', None):
+ from mmcv.utils import import_modules_from_strings
+ import_modules_from_strings(**cfg['custom_imports'])
+ # set cudnn_benchmark
+ if cfg.get('cudnn_benchmark', False):
+ torch.backends.cudnn.benchmark = True
+
+ cfg.model.pretrained = None
+ # in case the test dataset is concatenated
+ samples_per_gpu = 1
+ if isinstance(cfg.data.test, dict):
+ cfg.data.test.test_mode = True
+ samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
+ if samples_per_gpu > 1:
+ # Replace 'ImageToTensor' to 'DefaultFormatBundle'
+ cfg.data.test.pipeline = replace_ImageToTensor(
+ cfg.data.test.pipeline)
+ elif isinstance(cfg.data.test, list):
+ for ds_cfg in cfg.data.test:
+ ds_cfg.test_mode = True
+ samples_per_gpu = max(