update config

monocon/configs/_base_/datasets/kitti-mono3d-car-monocon.py

@@ -0,0 +1,109 @@
+# dataset settings
+dataset_type = 'KittiMonoDatasetMonoCon'
+data_root = 'data/kitti/'
+class_names = ['Car']
+input_modality = dict(
+    use_lidar=False,
+    use_camera=True,
+    use_radar=False,
+    use_map=False,
+    use_external=False)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D', to_float32=True, color_type='color'),
+    dict(
+        type='LoadAnnotations3DMonoCon',
+        with_bbox=True,
+        with_2D_kpts=True,
+        with_label=True,
+        with_attr_label=False,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='RandomShiftMonoCon', shift_ratio=0.5, max_shift_px=32),
+    dict(type='RandomFlipMonoCon', flip_ratio_bev_horizontal=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    # Note: keys ['gt_kpts_2d', 'gt_kpts_valid_mask'] is hard coded in DefaultFormatBundle
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(
+        type='Collect3D',
+        keys=[
+            'img', 'gt_bboxes', 'gt_labels', 'gt_bboxes_ignore', 'gt_bboxes_3d',
+            'gt_labels_3d', 'centers2d', 'depths', 'gt_kpts_2d', 'gt_kpts_valid_mask',
+        ],
+        meta_keys=('filename', 'ori_shape', 'img_shape', 'lidar2img',
+                   'pad_shape', 'scale_factor', 'flip',
+                   'cam_intrinsic', 'pcd_horizontal_flip',
+                   'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d',
+                   'img_norm_cfg', 'rect', 'Trv2c', 'P2', 'pcd_trans',
+                   'sample_idx', 'pcd_scale_factor', 'pcd_rotation',
+                   'pts_filename', 'transformation_3d_flow', 'cam_intrinsic_p0',)
+    ),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D'),
+    dict(
+        type='MultiScaleFlipAugMonoCon',
+        scale_factor=1.0,
+        flip=False,
+        transforms=[
+            dict(type='RandomFlipMonoCon'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_train_mono3d.coco.json',
+        info_file=data_root + 'kitti_infos_train.pkl',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=train_pipeline,
+        modality=input_modality,
+        min_height=25,
+        min_depth=2,
+        max_depth=65,
+        max_truncation=0.5,
+        max_occlusion=2,
+        box_type_3d='Camera'),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val_mono3d.coco.json',
+        info_file=data_root + 'kitti_infos_val.pkl',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        box_type_3d='Camera'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val_mono3d.coco.json',
+        info_file=data_root + 'kitti_infos_val.pkl',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        box_type_3d='Camera'))
+evaluation = dict(interval=5)