add r50 and swin configs

projects/configs/hdetr/r50-hdetr_sam-vit-b.py

@@ -0,0 +1,81 @@
+_base_ = [
+    '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
+]
+
+plugin = True
+plugin_dir = 'projects/instance_segment_anything/'
+
+model = dict(
+    type='DetWrapperInstanceSAM',
+    det_wrapper_type='hdetr',
+    det_wrapper_cfg=dict(aux_loss=True,
+                         backbone='resnet50',
+                         num_classes=91,
+                         cache_mode=False,
+                         dec_layers=6,
+                         dec_n_points=4,
+                         dilation=False,
+                         dim_feedforward=2048,
+                         drop_path_rate=0.2,
+                         dropout=0.0,
+                         enc_layers=6,
+                         enc_n_points=4,
+                         focal_alpha=0.25,
+                         frozen_weights=None,
+                         hidden_dim=256,
+                         k_one2many=6,
+                         lambda_one2many=1.0,
+                         look_forward_twice=True,
+                         masks=False,
+                         mixed_selection=True,
+                         nheads=8,
+                         num_feature_levels=4,
+                         num_queries_one2many=1500,
+                         num_queries_one2one=300,
+                         position_embedding='sine',
+                         position_embedding_scale=6.283185307179586,
+                         remove_difficult=False,
+                         topk=100,
+                         two_stage=True,
+                         use_checkpoint=False,
+                         use_fp16=False,
+                         with_box_refine=True),
+    det_model_ckpt='ckpt/r50_hdetr.pth',
+    num_classes=80,
+    model_type='vit_b',
+    sam_checkpoint='ckpt/sam_vit_b_01ec64.pth',
+    use_sam_iou=True,
+)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+# test_pipeline, NOTE the Pad's size_divisor is different from the default
+# setting (size_divisor=32). While there is little effect on the performance
+# whether we use the default setting or use size_divisor=1.
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=1,
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))

projects/configs/hdetr/r50-hdetr_sam-vit-l.py

@@ -0,0 +1,81 @@
+_base_ = [
+    '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
+]
+
+plugin = True
+plugin_dir = 'projects/instance_segment_anything/'
+
+model = dict(
+    type='DetWrapperInstanceSAM',
+    det_wrapper_type='hdetr',
+    det_wrapper_cfg=dict(aux_loss=True,
+                         backbone='resnet50',
+                         num_classes=91,
+                         cache_mode=False,
+                         dec_layers=6,
+                         dec_n_points=4,
+                         dilation=False,
+                         dim_feedforward=2048,
+                         drop_path_rate=0.2,
+                         dropout=0.0,
+                         enc_layers=6,
+                         enc_n_points=4,
+                         focal_alpha=0.25,
+                         frozen_weights=None,
+                         hidden_dim=256,
+                         k_one2many=6,
+                         lambda_one2many=1.0,
+                         look_forward_twice=True,
+                         masks=False,
+                         mixed_selection=True,
+                         nheads=8,
+                         num_feature_levels=4,
+                         num_queries_one2many=1500,
+                         num_queries_one2one=300,
+                         position_embedding='sine',
+                         position_embedding_scale=6.283185307179586,
+                         remove_difficult=False,
+                         topk=100,
+                         two_stage=True,
+                         use_checkpoint=False,
+                         use_fp16=False,
+                         with_box_refine=True),
+    det_model_ckpt='ckpt/r50_hdetr.pth',
+    num_classes=80,
+    model_type='vit_l',
+    sam_checkpoint='ckpt/sam_vit_l_0b3195.pth',
+    use_sam_iou=True,
+)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+# test_pipeline, NOTE the Pad's size_divisor is different from the default
+# setting (size_divisor=32). While there is little effect on the performance
+# whether we use the default setting or use size_divisor=1.
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=1,
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))

projects/configs/hdetr/swin-t-hdetr_sam-vit-b.py

@@ -0,0 +1,81 @@
+_base_ = [
+    '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
+]
+
+plugin = True
+plugin_dir = 'projects/instance_segment_anything/'
+
+model = dict(
+    type='DetWrapperInstanceSAM',
+    det_wrapper_type='hdetr',
+    det_wrapper_cfg=dict(aux_loss=True,
+                         backbone='swin_tiny',
+                         num_classes=91,
+                         cache_mode=False,
+                         dec_layers=6,
+                         dec_n_points=4,
+                         dilation=False,
+                         dim_feedforward=2048,
+                         drop_path_rate=0.2,
+                         dropout=0.0,
+                         enc_layers=6,
+                         enc_n_points=4,
+                         focal_alpha=0.25,
+                         frozen_weights=None,
+                         hidden_dim=256,
+                         k_one2many=6,
+                         lambda_one2many=1.0,
+                         look_forward_twice=True,
+                         masks=False,
+                         mixed_selection=True,
+                         nheads=8,
+                         num_feature_levels=4,
+                         num_queries_one2many=1500,
+                         num_queries_one2one=300,
+                         position_embedding='sine',
+                         position_embedding_scale=6.283185307179586,
+                         remove_difficult=False,
+                         topk=100,
+                         two_stage=True,
+                         use_checkpoint=False,
+                         use_fp16=False,
+                         with_box_refine=True),
+    det_model_ckpt='ckpt/swin_t_hdetr.pth',
+    num_classes=80,
+    model_type='vit_b',
+    sam_checkpoint='ckpt/sam_vit_b_01ec64.pth',
+    use_sam_iou=True,
+)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+# test_pipeline, NOTE the Pad's size_divisor is different from the default
+# setting (size_divisor=32). While there is little effect on the performance
+# whether we use the default setting or use size_divisor=1.
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=1,
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))

projects/configs/hdetr/swin-t-hdetr_sam-vit-l.py

@@ -0,0 +1,81 @@
+_base_ = [
+    '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
+]
+
+plugin = True
+plugin_dir = 'projects/instance_segment_anything/'
+
+model = dict(
+    type='DetWrapperInstanceSAM',
+    det_wrapper_type='hdetr',
+    det_wrapper_cfg=dict(aux_loss=True,
+                         backbone='swin_tiny',
+                         num_classes=91,
+                         cache_mode=False,
+                         dec_layers=6,
+                         dec_n_points=4,
+                         dilation=False,
+                         dim_feedforward=2048,
+                         drop_path_rate=0.2,
+                         dropout=0.0,
+                         enc_layers=6,
+                         enc_n_points=4,
+                         focal_alpha=0.25,
+                         frozen_weights=None,
+                         hidden_dim=256,
+                         k_one2many=6,
+                         lambda_one2many=1.0,
+                         look_forward_twice=True,
+                         masks=False,
+                         mixed_selection=True,
+                         nheads=8,
+                         num_feature_levels=4,
+                         num_queries_one2many=1500,
+                         num_queries_one2one=300,
+                         position_embedding='sine',
+                         position_embedding_scale=6.283185307179586,
+                         remove_difficult=False,
+                         topk=100,
+                         two_stage=True,
+                         use_checkpoint=False,
+                         use_fp16=False,
+                         with_box_refine=True),
+    det_model_ckpt='ckpt/swin_t_hdetr.pth',
+    num_classes=80,
+    model_type='vit_l',
+    sam_checkpoint='ckpt/sam_vit_l_0b3195.pth',
+    use_sam_iou=True,
+)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+# test_pipeline, NOTE the Pad's size_divisor is different from the default
+# setting (size_divisor=32). While there is little effect on the performance
+# whether we use the default setting or use size_divisor=1.
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=1,
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))