add

configs/rsprompter/rsprompter_query-nwpu-peft-512.py

@@ -0,0 +1,170 @@
+_base_ = ['_base_/rsprompter_query.py']
+
+work_dir = './work_dirs/rsprompter/rsprompter_query-nwpu'
+
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=5),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=4, save_best='coco/bbox_mAP', rule='greater', save_last=True),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    # visualization=dict(type='DetVisualizationHook', draw=True, interval=1, test_out_dir='vis_data')
+)
+
+vis_backends = [dict(type='LocalVisBackend'),
+                dict(type='WandbVisBackend', init_kwargs=dict(project='rsprompter-nwpu', group='rsprompter-query', name="rsprompter_query-nwpu"))
+                ]
+visualizer = dict(
+    type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+num_classes = 10
+prompt_shape = (70, 5)  # (per img pointset, per pointset point)
+
+#### should be changed when using different pretrain model
+
+# sam base model
+hf_sam_pretrain_name = "facebook/sam-vit-base"
+hf_sam_pretrain_ckpt_path = "pretrain_models/huggingface/hub/models--facebook--sam-vit-base/snapshots/b5fc59950038394bae73f549a55a9b46bc6f3d96/pytorch_model.bin"
+# # sam large model
+# hf_sam_pretrain_name = "facebook/sam-vit-large"
+# hf_sam_pretrain_ckpt_path = "pretrain_models/huggingface/hub/models--facebook--sam-vit-large/snapshots/70009d56dac23ebb3265377257158b1d6ed4c802/pytorch_model.bin"
+# # sam huge model
+# hf_sam_pretrain_name = "facebook/sam-vit-huge"
+# hf_sam_pretrain_ckpt_path = "pretrain_models/huggingface/hub/models--facebook--sam-vit-huge/snapshots/89080d6dcd9a900ebd712b13ff83ecf6f072e798/pytorch_model.bin"
+
+model = dict(
+    decoder_freeze=False,
+    shared_image_embedding=dict(
+        hf_pretrain_name=hf_sam_pretrain_name,
+        init_cfg=dict(type='Pretrained', checkpoint=hf_sam_pretrain_ckpt_path),
+    ),
+    backbone=dict(
+        hf_pretrain_name=hf_sam_pretrain_name,
+        init_cfg=dict(type='Pretrained', checkpoint=hf_sam_pretrain_ckpt_path)),
+    neck=dict(
+        feature_aggregator=dict(
+            in_channels=hf_sam_pretrain_name,
+            hidden_channels=32,
+            select_layers=range(1, 13, 2),  #### should be changed when using different pretrain model, base: range(1, 13, 2), large: range(1, 25, 2), huge: range(1, 33, 2)
+        ),
+    ),
+    panoptic_head=dict(
+        decoder_plus=True,
+        mask_decoder=dict(
+            hf_pretrain_name=hf_sam_pretrain_name,
+            init_cfg=dict(type='Pretrained', checkpoint=hf_sam_pretrain_ckpt_path)
+        ),
+        per_pointset_point=prompt_shape[1],
+        with_sincos=True,
+        num_things_classes=num_classes,
+        num_queries=prompt_shape[0],
+        loss_cls=dict(
+            class_weight=[1.0] * num_classes + [0.1])
+    ),
+    panoptic_fusion_head=dict(
+        num_things_classes=num_classes
+    ),
+    test_cfg=dict(
+        max_per_image=prompt_shape[0],
+    )
+)
+
+dataset_type = 'NWPUInsSegDataset'
+
+#### should be changed align with your code root and data root
+code_root = '/mnt/home/xx/codes/RSPrompter'
+data_root = '/mnt/home/xx/data/NWPU'
+
+batch_size_per_gpu = 1
+num_workers = 8
+persistent_workers = True
+train_dataloader = dict(
+    batch_size=batch_size_per_gpu,
+    num_workers=num_workers,
+    persistent_workers=persistent_workers,
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=code_root + '/data/NWPU/annotations/NWPU_instances_train.json',
+        data_prefix=dict(img='imgs'),
+    )
+)
+
+val_dataloader = dict(
+    batch_size=batch_size_per_gpu,
+    num_workers=num_workers,
+    persistent_workers=persistent_workers,
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=code_root + '/data/NWPU/annotations/NWPU_instances_val.json',
+        data_prefix=dict(img='imgs'),
+    )
+)
+
+test_dataloader = val_dataloader
+resume = False
+load_from = None
+
+base_lr = 0.0001
+max_epochs = 600
+
+train_cfg = dict(max_epochs=max_epochs)
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=50),
+    dict(
+        type='CosineAnnealingLR',
+        eta_min=base_lr * 0.001,
+        begin=1,
+        end=max_epochs,
+        T_max=max_epochs,
+        by_epoch=True
+    )
+]
+
+#### DeepSpeed Configs
+runner_type = 'FlexibleRunner'
+strategy = dict(
+    type='DeepSpeedStrategy',
+    fp16=dict(
+        enabled=True,
+        auto_cast=False,
+        fp16_master_weights_and_grads=False,
+        loss_scale=0,
+        loss_scale_window=500,
+        hysteresis=2,
+        min_loss_scale=1,
+        initial_scale_power=15,
+    ),
+    gradient_clipping=0.1,
+    inputs_to_half=['inputs'],
+    zero_optimization=dict(
+        stage=2,
+        allgather_partitions=True,
+        allgather_bucket_size=2e8,
+        reduce_scatter=True,
+        reduce_bucket_size='auto',
+        overlap_comm=True,
+        contiguous_gradients=True,
+    ),
+)
+optim_wrapper = dict(
+    type='DeepSpeedOptimWrapper',
+    optimizer=dict(
+        type='AdamW',
+        lr=base_lr,
+        weight_decay=0.05
+    )
+)
+
+# #### AMP training config
+# runner_type = 'Runner'
+# optim_wrapper = dict(
+#     type='AmpOptimWrapper',
+#     dtype='float16',
+#     optimizer=dict(
+#         type='AdamW',
+#         lr=base_lr,
+#         weight_decay=0.05)
+# )