Merge pull request #52 from mhd-medfa/main

feat: Introduce Co-DINO with SwinL Backbone (LVIS) Inference Config Files

README.md

@@ -60,7 +60,7 @@ In this paper, we present a novel collaborative hybrid assignments training sche
 | Co-DINO | Swin-L | 12 | LSJ | COCO | 59.3 | [config](https://github.com/Sense-X/Co-DETR/blob/main/projects/configs/co_dino/co_dino_5scale_lsj_swin_large_1x_coco.py) | [model](https://drive.google.com/drive/folders/1nAXOkzqrEgz-YnXxIEs4d5j9li_kmrnv?usp=sharing) |
 | Co-DINO | Swin-L | 24 | LSJ | COCO | 60.4 | [config](https://github.com/Sense-X/Co-DETR/blob/main/projects/configs/co_dino/co_dino_5scale_lsj_swin_large_2x_coco.py) | [model](https://drive.google.com/drive/folders/1nAXOkzqrEgz-YnXxIEs4d5j9li_kmrnv?usp=sharing) |
 | Co-DINO | Swin-L | 36 | LSJ | COCO | 60.7 | [config](https://github.com/Sense-X/Co-DETR/blob/main/projects/configs/co_dino/co_dino_5scale_lsj_swin_large_3x_coco.py) | [model](https://drive.google.com/drive/folders/1nAXOkzqrEgz-YnXxIEs4d5j9li_kmrnv?usp=sharing) |
-| Co-DINO | Swin-L | 36 | LSJ | LVIS | 56.9 | config | [model](https://drive.google.com/drive/folders/1nAXOkzqrEgz-YnXxIEs4d5j9li_kmrnv?usp=sharing) |
+| Co-DINO | Swin-L | 36 | LSJ | LVIS | 56.9 | [config](https://github.com/Sense-X/Co-DETR/blob/main/projects/configs/co_dino/co_dino_5scale_swin_large_3x_lvis.py) | [model](https://drive.google.com/drive/folders/1nAXOkzqrEgz-YnXxIEs4d5j9li_kmrnv?usp=sharing) |
 
 ### Co-Deformable-DETR
 

mmdet/apis/inference.py

@@ -9,14 +9,14 @@
 from mmcv.parallel import collate, scatter
 from mmcv.runner import load_checkpoint
 
-from mmdet.core import get_classes
+from mmdet.core import get_classes, DatasetEnum
 from mmdet.datasets import replace_ImageToTensor
 from mmdet.datasets.pipelines import Compose
 from mmdet.models import build_detector
 from projects import *
 
 
-def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None):
+def init_detector(config, checkpoint=None, dataset=DatasetEnum.COCO, device='cuda:0', cfg_options=None):
     """Initialize a detector from config file.
 
     Args:
@@ -49,9 +49,9 @@ def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None):
             model.CLASSES = checkpoint['meta']['CLASSES']
         else:
             warnings.simplefilter('once')
-            warnings.warn('Class names are not saved in the checkpoint\'s '
-                          'meta data, use COCO classes by default.')
-            model.CLASSES = get_classes('coco')
+            warnings.warn(f'Class names are not saved in the checkpoint\'s '
+                          f'meta data, use {dataset.value} classes.')
+            model.CLASSES = get_classes(dataset)
     model.cfg = config  # save the config in the model for convenience
     model.to(device)
     model.eval()

mmdet/core/evaluation/__init__.py

@@ -2,7 +2,7 @@
 from .class_names import (cityscapes_classes, coco_classes, dataset_aliases,
                           get_classes, imagenet_det_classes,
                           imagenet_vid_classes, oid_challenge_classes,
-                          oid_v6_classes, voc_classes)
+                          oid_v6_classes, voc_classes, DatasetEnum)
 from .eval_hooks import DistEvalHook, EvalHook
 from .mean_ap import average_precision, eval_map, print_map_summary
 from .panoptic_utils import INSTANCE_OFFSET
@@ -15,5 +15,5 @@
     'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map',
     'print_map_summary', 'eval_recalls', 'print_recall_summary',
     'plot_num_recall', 'plot_iou_recall', 'oid_v6_classes',
-    'oid_challenge_classes', 'INSTANCE_OFFSET'
+    'oid_challenge_classes', 'INSTANCE_OFFSET', 'DatasetEnum',
 ]

projects/configs/co_dino/co_dino_5scale_lsj_r50_1x_lvis.py

@@ -0,0 +1,67 @@
+_base_ = [
+    'co_dino_5scale_r50_1x_lvis.py'
+]
+
+model = dict(with_attn_mask=False)
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+image_size = (1536, 1536)
+load_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=image_size,
+        ratio_range=(0.1, 2.0),
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(
+        type='RandomCrop',
+        crop_type='absolute_range',
+        crop_size=image_size,
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Pad', size=image_size, pad_val=dict(img=(114, 114, 114))),
+]
+train_pipeline = [
+    dict(type='CopyPaste', max_num_pasted=100),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=image_size,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Pad', size=image_size, pad_val=dict(img=(114, 114, 114))),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+dataset_type = 'LVISV1Dataset'
+data_root = 'data/lvis_v1/'
+img_data_root = 'data/coco/'
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='MultiImageMixDataset',
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'lvis_v1_train.json',
+            img_prefix=img_data_root,
+            filter_empty_gt=False,
+            pipeline=load_pipeline),
+        pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))