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haar_force_phase_768/20260728_003429/20260728_003429.log

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+2026/07/28 00:34:30 - mmengine - INFO - 
+------------------------------------------------------------
+System environment:
+    sys.platform: linux
+    Python: 3.11.15 (main, Jul 18 2026, 17:02:49) [Clang 22.1.3 ]
+    CUDA available: True
+    MUSA available: False
+    numpy_random_seed: 42
+    GPU 0: NVIDIA RTX PRO 6000 Blackwell Server Edition
+    CUDA_HOME: /usr/local/cuda
+    NVCC: Cuda compilation tools, release 13.1, V13.1.115
+    GCC: cc (Debian 14.2.0-19) 14.2.0
+    PyTorch: 2.13.0+cu130
+    PyTorch compiling details: PyTorch built with:
+  - GCC 13.3
+  - C++ Version: 202002
+  - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications
+  - Intel(R) MKL-DNN v3.12.0 (Git Hash 80afa71049cd69a3df32adcccb623b12cd7baa22)
+  - OpenMP 201511 (a.k.a. OpenMP 4.5)
+  - LAPACK is enabled (usually provided by MKL)
+  - NNPACK is enabled
+  - CPU capability usage: AVX512
+  - CUDA Runtime 13.0
+  - NVCC architecture flags: -gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_100,code=sm_100;-gencode;arch=compute_120,code=sm_120
+  - CuDNN 92.0  (built against CUDA 13.2)
+  - Magma 2.6.1
+  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=cf30153c4c131c8164ee7798e5022d810682e2cb, CUDA_FLAGS= -DLIBCUDACXX_ENABLE_SIMPLIFIED_COMPLEX_OPERATIONS -Xfatbin -compress-all -DONNX_NAMESPACE=onnx_torch -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90 -gencode arch=compute_100,code=sm_100 -gencode arch=compute_120,code=sm_120 -Xcudafe --diag_suppress=cc_clobber_ignored,--diag_suppress=field_without_dll_interface,--diag_suppress=base_class_has_different_dll_interface,--diag_suppress=dll_interface_conflict_none_assumed,--diag_suppress=dll_interface_conflict_dllexport_assumed,--diag_suppress=bad_friend_decl --expt-relaxed-constexpr --expt-extended-lambda -Xfatbin -compress-all --threads 2 -compress-mode=size -Wno-deprecated-gpu-targets --expt-extended-lambda -DCUB_WRAPPED_NAMESPACE=at_cuda_detail -DDISABLE_CUSPARSE_DEPRECATED -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -DC10_NODEPRECATED, CUDA_VERSION=13.0, CUDNN_VERSION=9.20.0, CXX_COMPILER=/opt/rh/gcc-toolset-13/root/usr/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DHAS_CUPTI -DUSE_FBGEMM -DUSE_MSLK -DUSE_PYTORCH_QNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-dangling-reference -Wno-error=dangling-reference -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.13.0, USE_CUDA=1, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, 
+
+    TorchVision: 0.28.0+cu130
+    OpenCV: 4.11.0
+    MMEngine: 0.10.7
+
+Runtime environment:
+    cudnn_benchmark: False
+    dist_cfg: {'backend': 'nccl'}
+    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
+    seed: 42
+    Distributed launcher: none
+    Distributed training: False
+    GPU number: 1
+------------------------------------------------------------
+
+2026/07/28 00:34:30 - mmengine - INFO - Config:
+auto_scale_lr = dict(base_batch_size=16, enable=True)
+backend_args = None
+custom_imports = dict(imports=[
+    'projects.pahr',
+])
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=1,
+        max_keep_ckpts=1,
+        rule='greater',
+        save_best='coco/bbox_mAP_75',
+        save_last=True,
+        type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = None
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+model = dict(
+    backbone=dict(
+        depth=50,
+        frozen_stages=1,
+        init_cfg=dict(
+            checkpoint='open-mmlab://detectron/resnet50_caffe',
+            type='Pretrained'),
+        norm_cfg=dict(requires_grad=False, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        style='caffe',
+        type='ResNet'),
+    bbox_head=dict(
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(loss_weight=1.0, type='IoULoss'),
+        loss_centerness=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        loss_cls=dict(
+            alpha=0.25,
+            gamma=2.0,
+            loss_weight=1.0,
+            type='FocalLoss',
+            use_sigmoid=True),
+        num_classes=1,
+        stacked_convs=4,
+        strides=[
+            8,
+            16,
+            32,
+            64,
+            128,
+        ],
+        type='FCOSHead'),
+    data_preprocessor=dict(
+        bgr_to_rgb=False,
+        mean=[
+            102.9801,
+            115.9465,
+            122.7717,
+        ],
+        pad_size_divisor=32,
+        std=[
+            1.0,
+            1.0,
+            1.0,
+        ],
+        type='DetDataPreprocessor'),
+    loss_offset_weight=0.1,
+    loss_pos_weight=0.1,
+    measurement_center_gate_floor=0.0,
+    measurement_center_weight=0.1,
+    measurement_channels=16,
+    measurement_fixed_strength=None,
+    measurement_phase_weight=0.1,
+    measurement_size_blend=0.5,
+    measurement_size_weight=0.1,
+    measurement_tiny_limit=24.0,
+    neck=dict(
+        add_extra_convs='on_output',
+        correction_gain=0.0,
+        correction_gate_floor=0.0,
+        detach_position_gate=False,
+        detail_channels=64,
+        gate_power=1.0,
+        guide_channels=0,
+        in_channels=[
+            256,
+            512,
+            1024,
+            2048,
+        ],
+        locator_channels=64,
+        num_outs=5,
+        out_channels=256,
+        relu_before_extra_convs=True,
+        start_level=1,
+        target_correction_ratio=0.0,
+        type='PAHRFPN',
+        use_output_gate=False),
+    phase_gate_floor=1.0,
+    phase_strength=1.0,
+    position_stride=8,
+    test_cfg=dict(
+        max_per_img=100,
+        min_bbox_size=0,
+        nms=dict(iou_threshold=0.5, type='nms'),
+        nms_pre=1000,
+        score_thr=0.05),
+    tiny_max_sqrt_area=16.0,
+    type='PAHRFCOS',
+    use_phase_shift=True,
+    use_tiny_measurement=False)
+optim_wrapper = dict(
+    clip_grad=dict(max_norm=35, norm_type=2),
+    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0001),
+    paramwise_cfg=dict(bias_decay_mult=0.0, bias_lr_mult=2.0),
+    type='OptimWrapper')
+pahr_variant = dict(
+    correction_gain=0.0,
+    detail_lr_mult=1.0,
+    giou=False,
+    guide_channels=0,
+    image_size=768,
+    measurement_center_gate_floor=0.0,
+    measurement_fixed_strength=None,
+    measurement_size_blend=0.5,
+    name='haar_force_phase_768',
+    phase_gate_floor=1.0,
+    phase_shift=True,
+    phase_strength=1.0,
+    scaled_schedule=True,
+    target_correction_ratio=0.0,
+    use_output_gate=False,
+    use_tiny_measurement=False,
+    v3_gate=False)
+param_scheduler = [
+    dict(
+        begin=0,
+        by_epoch=False,
+        end=500,
+        factor=0.3333333333333333,
+        type='ConstantLR'),
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=10,
+        gamma=0.1,
+        milestones=[
+            7,
+            9,
+        ],
+        type='MultiStepLR'),
+]
+randomness = dict(seed=42)
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        meta_keys=(
+            'img_id',
+            'img_path',
+            'ori_shape',
+            'img_shape',
+            'scale_factor',
+        ),
+        type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=10, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=8,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/train.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='DetLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = '/marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768'
+
+2026/07/28 00:34:30 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
+2026/07/28 00:34:30 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DetVisualizationHook               
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DetVisualizationHook               
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.conv1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.conv1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.conv2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.conv3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.downsample.0.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.downsample.1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.0.downsample.1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.1.conv1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.1.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.1.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.1.conv2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.1.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.1.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.1.conv3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.1.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.1.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.2.conv1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.2.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.2.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.2.conv2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.2.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.2.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.2.conv3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.2.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer1.2.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.0.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.0.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.0.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.0.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.0.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.0.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.0.downsample.1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.0.downsample.1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.1.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.1.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.1.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.1.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.1.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.1.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.2.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.2.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.2.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.2.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.2.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.2.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.3.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.3.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.3.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.3.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.3.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer2.3.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.0.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.0.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.0.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.0.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.0.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.0.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.0.downsample.1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.0.downsample.1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.1.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.1.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.1.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.1.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.1.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.1.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.2.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.2.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.2.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.2.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.2.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.2.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.3.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.3.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.3.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.3.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.3.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.3.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.4.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.4.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.4.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.4.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.4.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.4.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.5.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.5.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.5.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.5.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.5.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer3.5.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.0.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.0.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.0.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.0.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.0.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.0.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.0.downsample.1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.0.downsample.1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.1.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.1.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.1.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.1.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.1.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.1.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.2.bn1.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.2.bn1.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.2.bn2.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.2.bn2.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.2.bn3.weight is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - WARNING - backbone.layer4.2.bn3.bias is skipped since its requires_grad=False
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.lateral_convs.0.conv.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.lateral_convs.0.conv.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.lateral_convs.1.conv.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.lateral_convs.1.conv.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.lateral_convs.2.conv.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.lateral_convs.2.conv.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.0.conv.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.0.conv.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.1.conv.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.1.conv.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.2.conv.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.2.conv.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.3.conv.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.3.conv.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.4.conv.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.fpn_convs.4.conv.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.locator.0.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.locator.0.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.locator.1.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.locator.1.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.locator.3.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.locator.3.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.detail_mixer.0.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.detail_mixer.0.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.detail_mixer.1.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.detail_mixer.1.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.detail_mixer.3.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- neck.detail_mixer.3.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.cls_convs.0.gn.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.cls_convs.1.gn.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.cls_convs.2.gn.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.cls_convs.3.gn.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.reg_convs.0.gn.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.reg_convs.1.gn.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.reg_convs.2.gn.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.reg_convs.3.gn.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.conv_cls.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.conv_cls.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.conv_reg.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.conv_reg.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.conv_centerness.bias:lr=0.02
+2026/07/28 00:34:31 - mmengine - INFO - paramwise_options -- bbox_head.conv_centerness.bias:weight_decay=0.0
+2026/07/28 00:34:31 - mmengine - INFO - LR is set based on batch size of 16 and the current batch size is 8. Scaling the original LR by 0.5.
+2026/07/28 00:34:31 - mmengine - INFO - load model from: open-mmlab://detectron/resnet50_caffe
+2026/07/28 00:34:31 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://detectron/resnet50_caffe
+2026/07/28 00:34:31 - mmengine - WARNING - The model and loaded state dict do not match exactly
+
+unexpected key in source state_dict: conv1.bias, layer1.0.conv1.bias, layer1.0.conv2.bias, layer1.0.conv3.bias, layer1.0.downsample.0.bias, layer1.1.conv1.bias, layer1.1.conv2.bias, layer1.1.conv3.bias, layer1.2.conv1.bias, layer1.2.conv2.bias, layer1.2.conv3.bias, layer2.0.conv1.bias, layer2.0.conv2.bias, layer2.0.conv3.bias, layer2.0.downsample.0.bias, layer2.1.conv1.bias, layer2.1.conv2.bias, layer2.1.conv3.bias, layer2.2.conv1.bias, layer2.2.conv2.bias, layer2.2.conv3.bias, layer2.3.conv1.bias, layer2.3.conv2.bias, layer2.3.conv3.bias, layer3.0.conv1.bias, layer3.0.conv2.bias, layer3.0.conv3.bias, layer3.0.downsample.0.bias, layer3.1.conv1.bias, layer3.1.conv2.bias, layer3.1.conv3.bias, layer3.2.conv1.bias, layer3.2.conv2.bias, layer3.2.conv3.bias, layer3.3.conv1.bias, layer3.3.conv2.bias, layer3.3.conv3.bias, layer3.4.conv1.bias, layer3.4.conv2.bias, layer3.4.conv3.bias, layer3.5.conv1.bias, layer3.5.conv2.bias, layer3.5.conv3.bias, layer4.0.conv1.bias, layer4.0.conv2.bias, layer4.0.conv3.bias, layer4.0.downsample.0.bias, layer4.1.conv1.bias, layer4.1.conv2.bias, layer4.1.conv3.bias, layer4.2.conv1.bias, layer4.2.conv2.bias, layer4.2.conv3.bias
+
+Name of parameter - Initialization information
+
+backbone.conv1.weight - torch.Size([64, 3, 7, 7]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.bn1.weight - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.bn1.bias - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.bn1.weight - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.bn1.bias - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.bn2.weight - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.bn2.bias - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.bn3.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.bn3.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.downsample.1.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.0.downsample.1.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.1.bn1.weight - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.1.bn1.bias - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.1.bn2.weight - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.1.bn2.bias - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.1.bn3.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.1.bn3.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.2.bn1.weight - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.2.bn1.bias - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.2.bn2.weight - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.2.bn2.bias - torch.Size([64]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.2.bn3.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer1.2.bn3.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.bn1.weight - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.bn1.bias - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.bn2.weight - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.bn2.bias - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.bn3.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.bn3.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.downsample.1.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.0.downsample.1.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.1.bn1.weight - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.1.bn1.bias - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.1.bn2.weight - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.1.bn2.bias - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.1.bn3.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.1.bn3.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.2.bn1.weight - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.2.bn1.bias - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.2.bn2.weight - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.2.bn2.bias - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.2.bn3.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.2.bn3.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.3.bn1.weight - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.3.bn1.bias - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.3.bn2.weight - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.3.bn2.bias - torch.Size([128]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.3.bn3.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer2.3.bn3.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.downsample.1.weight - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.0.downsample.1.bias - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.1.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.1.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.1.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.1.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.1.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.1.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.2.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.2.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.2.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.2.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.2.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.2.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.3.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.3.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.3.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.3.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.3.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.3.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.4.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.4.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.4.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.4.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.4.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.4.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.5.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.5.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.5.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.5.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.5.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer3.5.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.bn1.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.bn1.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.bn2.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.bn2.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.bn3.weight - torch.Size([2048]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.bn3.bias - torch.Size([2048]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.downsample.1.weight - torch.Size([2048]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.0.downsample.1.bias - torch.Size([2048]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.1.bn1.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.1.bn1.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.1.bn2.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.1.bn2.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.1.bn3.weight - torch.Size([2048]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.1.bn3.bias - torch.Size([2048]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.2.bn1.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.2.bn1.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.2.bn2.weight - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.2.bn2.bias - torch.Size([512]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.2.bn3.weight - torch.Size([2048]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+backbone.layer4.2.bn3.bias - torch.Size([2048]): 
+PretrainedInit: load from open-mmlab://detectron/resnet50_caffe 
+
+neck.lateral_convs.0.conv.weight - torch.Size([256, 512, 1, 1]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.lateral_convs.0.conv.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+neck.lateral_convs.1.conv.weight - torch.Size([256, 1024, 1, 1]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.lateral_convs.1.conv.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+neck.lateral_convs.2.conv.weight - torch.Size([256, 2048, 1, 1]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.lateral_convs.2.conv.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.0.conv.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.1.conv.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.2.conv.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.3.conv.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+neck.fpn_convs.4.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.4.conv.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+neck.locator.0.weight - torch.Size([64, 1024, 1, 1]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.locator.0.bias - torch.Size([64]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.locator.1.weight - torch.Size([64, 1, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.locator.1.bias - torch.Size([64]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.locator.3.weight - torch.Size([12, 64, 1, 1]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.locator.3.bias - torch.Size([12]): 
+Initialized by user-defined `init_weights` in PAHRFPN  
+
+neck.detail_mixer.0.weight - torch.Size([64, 1036, 1, 1]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.detail_mixer.0.bias - torch.Size([64]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.detail_mixer.1.weight - torch.Size([64, 1, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.detail_mixer.1.bias - torch.Size([64]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.detail_mixer.3.weight - torch.Size([768, 64, 1, 1]): 
+Initialized by user-defined `init_weights` in PAHRFPN  
+
+neck.detail_mixer.3.bias - torch.Size([768]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.cls_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.cls_convs.0.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.cls_convs.0.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.cls_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.cls_convs.1.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.cls_convs.1.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.cls_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.cls_convs.2.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.cls_convs.2.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.cls_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.cls_convs.3.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.cls_convs.3.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.reg_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.reg_convs.0.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.reg_convs.0.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.reg_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.reg_convs.1.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.reg_convs.1.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.reg_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.reg_convs.2.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.reg_convs.2.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.reg_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.reg_convs.3.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.reg_convs.3.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.conv_cls.weight - torch.Size([1, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=-4.59511985013459 
+
+bbox_head.conv_cls.bias - torch.Size([1]): 
+NormalInit: mean=0, std=0.01, bias=-4.59511985013459 
+
+bbox_head.conv_reg.weight - torch.Size([4, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.conv_reg.bias - torch.Size([4]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.conv_centerness.weight - torch.Size([1, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.conv_centerness.bias - torch.Size([1]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.scales.0.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.scales.1.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.scales.2.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.scales.3.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+
+bbox_head.scales.4.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of PAHRFCOS  
+2026/07/28 00:34:31 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
+2026/07/28 00:34:31 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
+2026/07/28 00:34:31 - mmengine - INFO - Checkpoints will be saved to /marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768.
+2026/07/28 00:34:40 - mmengine - INFO - Epoch(train)  [1][ 50/158]  base_lr: 3.3333e-03 lr: 1.6667e-03  eta: 0:04:29  time: 0.1761  data_time: 0.0241  memory: 6269  grad_norm: 52.9135  loss: 3.3626  loss_cls: 0.6375  loss_bbox: 1.4167  loss_centerness: 0.6474  loss_pos: 0.6498  loss_offset: 0.0111
+2026/07/28 00:34:47 - mmengine - INFO - Epoch(train)  [1][100/158]  base_lr: 3.3333e-03 lr: 1.6667e-03  eta: 0:03:51  time: 0.1374  data_time: 0.0163  memory: 6269  grad_norm: 14.5566  loss: 1.9002  loss_cls: 0.3247  loss_bbox: 0.7378  loss_centerness: 0.6225  loss_pos: 0.2078  loss_offset: 0.0074
+2026/07/28 00:34:53 - mmengine - INFO - Epoch(train)  [1][150/158]  base_lr: 3.3333e-03 lr: 1.6667e-03  eta: 0:03:34  time: 0.1369  data_time: 0.0165  memory: 6269  grad_norm: 14.0412  loss: 1.8411  loss_cls: 0.2647  loss_bbox: 0.8165  loss_centerness: 0.6039  loss_pos: 0.1501  loss_offset: 0.0058
+2026/07/28 00:34:55 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:34:55 - mmengine - INFO - Saving checkpoint at 1 epochs
+2026/07/28 00:34:56 - mmengine - INFO - Epoch(val)  [1][ 50/584]    eta: 0:00:13  time: 0.0258  data_time: 0.0089  memory: 6269  
+2026/07/28 00:34:57 - mmengine - INFO - Epoch(val)  [1][100/584]    eta: 0:00:09  time: 0.0142  data_time: 0.0021  memory: 392  
+2026/07/28 00:34:58 - mmengine - INFO - Epoch(val)  [1][150/584]    eta: 0:00:07  time: 0.0136  data_time: 0.0022  memory: 392  
+2026/07/28 00:34:58 - mmengine - INFO - Epoch(val)  [1][200/584]    eta: 0:00:06  time: 0.0136  data_time: 0.0020  memory: 392  
+2026/07/28 00:34:59 - mmengine - INFO - Epoch(val)  [1][250/584]    eta: 0:00:05  time: 0.0127  data_time: 0.0018  memory: 392  
+2026/07/28 00:35:00 - mmengine - INFO - Epoch(val)  [1][300/584]    eta: 0:00:04  time: 0.0127  data_time: 0.0016  memory: 392  
+2026/07/28 00:35:00 - mmengine - INFO - Epoch(val)  [1][350/584]    eta: 0:00:03  time: 0.0138  data_time: 0.0021  memory: 392  
+2026/07/28 00:35:01 - mmengine - INFO - Epoch(val)  [1][400/584]    eta: 0:00:02  time: 0.0136  data_time: 0.0018  memory: 392  
+2026/07/28 00:35:02 - mmengine - INFO - Epoch(val)  [1][450/584]    eta: 0:00:01  time: 0.0129  data_time: 0.0018  memory: 392  
+2026/07/28 00:35:02 - mmengine - INFO - Epoch(val)  [1][500/584]    eta: 0:00:01  time: 0.0132  data_time: 0.0022  memory: 392  
+2026/07/28 00:35:03 - mmengine - INFO - Epoch(val)  [1][550/584]    eta: 0:00:00  time: 0.0132  data_time: 0.0020  memory: 392  
+2026/07/28 00:35:04 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:35:04 - mmengine - INFO - bbox_mAP_copypaste: 0.073 0.373 0.002 0.074 0.129 -1.000
+2026/07/28 00:35:04 - mmengine - INFO - Epoch(val) [1][584/584]    coco/bbox_mAP: 0.0730  coco/bbox_mAP_50: 0.3730  coco/bbox_mAP_75: 0.0020  coco/bbox_mAP_s: 0.0740  coco/bbox_mAP_m: 0.1290  coco/bbox_mAP_l: -1.0000  data_time: 0.0026  time: 0.0144
+2026/07/28 00:35:05 - mmengine - INFO - The best checkpoint with 0.0020 coco/bbox_mAP_75 at 1 epoch is saved to best_coco_bbox_mAP_75_epoch_1.pth.
+2026/07/28 00:35:12 - mmengine - INFO - Epoch(train)  [2][ 50/158]  base_lr: 3.3333e-03 lr: 1.6667e-03  eta: 0:03:22  time: 0.1408  data_time: 0.0188  memory: 6269  grad_norm: 10.4869  loss: 1.5134  loss_cls: 0.1912  loss_bbox: 0.5907  loss_centerness: 0.5958  loss_pos: 0.1290  loss_offset: 0.0067
+2026/07/28 00:35:19 - mmengine - INFO - Epoch(train)  [2][100/158]  base_lr: 3.3333e-03 lr: 1.6667e-03  eta: 0:03:11  time: 0.1356  data_time: 0.0157  memory: 6269  grad_norm: 9.8751  loss: 1.4814  loss_cls: 0.1872  loss_bbox: 0.5649  loss_centerness: 0.5889  loss_pos: 0.1350  loss_offset: 0.0054
+2026/07/28 00:35:26 - mmengine - INFO - Epoch(train)  [2][150/158]  base_lr: 3.3333e-03 lr: 1.6667e-03  eta: 0:03:02  time: 0.1354  data_time: 0.0163  memory: 6269  grad_norm: 10.1705  loss: 1.4278  loss_cls: 0.1601  loss_bbox: 0.5656  loss_centerness: 0.5858  loss_pos: 0.1118  loss_offset: 0.0045
+2026/07/28 00:35:27 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:35:27 - mmengine - INFO - Saving checkpoint at 2 epochs
+2026/07/28 00:35:28 - mmengine - INFO - Epoch(val)  [2][ 50/584]    eta: 0:00:07  time: 0.0139  data_time: 0.0023  memory: 6269  
+2026/07/28 00:35:29 - mmengine - INFO - Epoch(val)  [2][100/584]    eta: 0:00:06  time: 0.0128  data_time: 0.0018  memory: 392  
+2026/07/28 00:35:30 - mmengine - INFO - Epoch(val)  [2][150/584]    eta: 0:00:05  time: 0.0126  data_time: 0.0020  memory: 392  
+2026/07/28 00:35:30 - mmengine - INFO - Epoch(val)  [2][200/584]    eta: 0:00:05  time: 0.0130  data_time: 0.0018  memory: 392  
+2026/07/28 00:35:31 - mmengine - INFO - Epoch(val)  [2][250/584]    eta: 0:00:04  time: 0.0133  data_time: 0.0018  memory: 392  
+2026/07/28 00:35:31 - mmengine - INFO - Epoch(val)  [2][300/584]    eta: 0:00:03  time: 0.0126  data_time: 0.0015  memory: 392  
+2026/07/28 00:35:32 - mmengine - INFO - Epoch(val)  [2][350/584]    eta: 0:00:03  time: 0.0130  data_time: 0.0020  memory: 392  
+2026/07/28 00:35:33 - mmengine - INFO - Epoch(val)  [2][400/584]    eta: 0:00:02  time: 0.0138  data_time: 0.0018  memory: 392  
+2026/07/28 00:35:34 - mmengine - INFO - Epoch(val)  [2][450/584]    eta: 0:00:01  time: 0.0141  data_time: 0.0019  memory: 392  
+2026/07/28 00:35:34 - mmengine - INFO - Epoch(val)  [2][500/584]    eta: 0:00:01  time: 0.0129  data_time: 0.0021  memory: 392  
+2026/07/28 00:35:35 - mmengine - INFO - Epoch(val)  [2][550/584]    eta: 0:00:00  time: 0.0134  data_time: 0.0019  memory: 392  
+2026/07/28 00:35:35 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:35:36 - mmengine - INFO - bbox_mAP_copypaste: 0.145 0.598 0.010 0.147 0.145 -1.000
+2026/07/28 00:35:36 - mmengine - INFO - Epoch(val) [2][584/584]    coco/bbox_mAP: 0.1450  coco/bbox_mAP_50: 0.5980  coco/bbox_mAP_75: 0.0100  coco/bbox_mAP_s: 0.1470  coco/bbox_mAP_m: 0.1450  coco/bbox_mAP_l: -1.0000  data_time: 0.0019  time: 0.0132
+2026/07/28 00:35:36 - mmengine - INFO - The previous best checkpoint /marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768/best_coco_bbox_mAP_75_epoch_1.pth is removed
+2026/07/28 00:35:37 - mmengine - INFO - The best checkpoint with 0.0100 coco/bbox_mAP_75 at 2 epoch is saved to best_coco_bbox_mAP_75_epoch_2.pth.
+2026/07/28 00:35:44 - mmengine - INFO - Epoch(train)  [3][ 50/158]  base_lr: 3.3333e-03 lr: 1.6667e-03  eta: 0:02:52  time: 0.1372  data_time: 0.0174  memory: 6269  grad_norm: 8.0198  loss: 1.3507  loss_cls: 0.1490  loss_bbox: 0.5103  loss_centerness: 0.5856  loss_pos: 0.1010  loss_offset: 0.0048
+2026/07/28 00:35:51 - mmengine - INFO - Epoch(train)  [3][100/158]  base_lr: 3.3333e-03 lr: 1.6667e-03  eta: 0:02:44  time: 0.1352  data_time: 0.0158  memory: 6269  grad_norm: 9.7452  loss: 1.3973  loss_cls: 0.1556  loss_bbox: 0.5386  loss_centerness: 0.5824  loss_pos: 0.1153  loss_offset: 0.0054
+2026/07/28 00:35:58 - mmengine - INFO - Epoch(train)  [3][150/158]  base_lr: 3.3333e-03 lr: 1.6667e-03  eta: 0:02:36  time: 0.1330  data_time: 0.0150  memory: 6269  grad_norm: 8.5555  loss: 1.3167  loss_cls: 0.1433  loss_bbox: 0.4837  loss_centerness: 0.5792  loss_pos: 0.1057  loss_offset: 0.0049
+2026/07/28 00:35:59 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:35:59 - mmengine - INFO - Saving checkpoint at 3 epochs
+2026/07/28 00:36:00 - mmengine - INFO - Epoch(val)  [3][ 50/584]    eta: 0:00:06  time: 0.0131  data_time: 0.0023  memory: 6269  
+2026/07/28 00:36:01 - mmengine - INFO - Epoch(val)  [3][100/584]    eta: 0:00:06  time: 0.0128  data_time: 0.0018  memory: 392  
+2026/07/28 00:36:01 - mmengine - INFO - Epoch(val)  [3][150/584]    eta: 0:00:05  time: 0.0128  data_time: 0.0020  memory: 392  
+2026/07/28 00:36:02 - mmengine - INFO - Epoch(val)  [3][200/584]    eta: 0:00:04  time: 0.0127  data_time: 0.0018  memory: 392  
+2026/07/28 00:36:03 - mmengine - INFO - Epoch(val)  [3][250/584]    eta: 0:00:04  time: 0.0124  data_time: 0.0017  memory: 392  
+2026/07/28 00:36:03 - mmengine - INFO - Epoch(val)  [3][300/584]    eta: 0:00:03  time: 0.0123  data_time: 0.0015  memory: 392  
+2026/07/28 00:36:04 - mmengine - INFO - Epoch(val)  [3][350/584]    eta: 0:00:03  time: 0.0138  data_time: 0.0021  memory: 392  
+2026/07/28 00:36:04 - mmengine - INFO - Epoch(val)  [3][400/584]    eta: 0:00:02  time: 0.0133  data_time: 0.0016  memory: 392  
+2026/07/28 00:36:05 - mmengine - INFO - Epoch(val)  [3][450/584]    eta: 0:00:01  time: 0.0140  data_time: 0.0020  memory: 392  
+2026/07/28 00:36:06 - mmengine - INFO - Epoch(val)  [3][500/584]    eta: 0:00:01  time: 0.0142  data_time: 0.0022  memory: 392  
+2026/07/28 00:36:07 - mmengine - INFO - Epoch(val)  [3][550/584]    eta: 0:00:00  time: 0.0140  data_time: 0.0020  memory: 392  
+2026/07/28 00:36:07 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:36:08 - mmengine - INFO - bbox_mAP_copypaste: 0.178 0.608 0.035 0.178 0.194 -1.000
+2026/07/28 00:36:08 - mmengine - INFO - Epoch(val) [3][584/584]    coco/bbox_mAP: 0.1780  coco/bbox_mAP_50: 0.6080  coco/bbox_mAP_75: 0.0350  coco/bbox_mAP_s: 0.1780  coco/bbox_mAP_m: 0.1940  coco/bbox_mAP_l: -1.0000  data_time: 0.0019  time: 0.0133
+2026/07/28 00:36:08 - mmengine - INFO - The previous best checkpoint /marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768/best_coco_bbox_mAP_75_epoch_2.pth is removed
+2026/07/28 00:36:08 - mmengine - INFO - The best checkpoint with 0.0350 coco/bbox_mAP_75 at 3 epoch is saved to best_coco_bbox_mAP_75_epoch_3.pth.
+2026/07/28 00:36:16 - mmengine - INFO - Epoch(train)  [4][ 50/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:02:27  time: 0.1379  data_time: 0.0180  memory: 6269  grad_norm: 11.8081  loss: 1.5043  loss_cls: 0.2276  loss_bbox: 0.5624  loss_centerness: 0.5840  loss_pos: 0.1258  loss_offset: 0.0044
+2026/07/28 00:36:23 - mmengine - INFO - Epoch(train)  [4][100/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:02:20  time: 0.1380  data_time: 0.0173  memory: 6269  grad_norm: 13.2713  loss: 2.1494  loss_cls: 0.5381  loss_bbox: 0.7705  loss_centerness: 0.6273  loss_pos: 0.2066  loss_offset: 0.0070
+2026/07/28 00:36:29 - mmengine - INFO - Epoch(train)  [4][150/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:02:13  time: 0.1353  data_time: 0.0161  memory: 6269  grad_norm: 9.1616  loss: 1.7058  loss_cls: 0.2932  loss_bbox: 0.6429  loss_centerness: 0.6197  loss_pos: 0.1446  loss_offset: 0.0053
+2026/07/28 00:36:30 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:36:30 - mmengine - INFO - Saving checkpoint at 4 epochs
+2026/07/28 00:36:32 - mmengine - INFO - Epoch(val)  [4][ 50/584]    eta: 0:00:09  time: 0.0172  data_time: 0.0035  memory: 6269  
+2026/07/28 00:36:33 - mmengine - INFO - Epoch(val)  [4][100/584]    eta: 0:00:07  time: 0.0142  data_time: 0.0020  memory: 392  
+2026/07/28 00:36:33 - mmengine - INFO - Epoch(val)  [4][150/584]    eta: 0:00:06  time: 0.0141  data_time: 0.0021  memory: 392  
+2026/07/28 00:36:34 - mmengine - INFO - Epoch(val)  [4][200/584]    eta: 0:00:05  time: 0.0136  data_time: 0.0019  memory: 392  
+2026/07/28 00:36:35 - mmengine - INFO - Epoch(val)  [4][250/584]    eta: 0:00:04  time: 0.0127  data_time: 0.0017  memory: 392  
+2026/07/28 00:36:35 - mmengine - INFO - Epoch(val)  [4][300/584]    eta: 0:00:04  time: 0.0130  data_time: 0.0016  memory: 392  
+2026/07/28 00:36:36 - mmengine - INFO - Epoch(val)  [4][350/584]    eta: 0:00:03  time: 0.0133  data_time: 0.0021  memory: 392  
+2026/07/28 00:36:37 - mmengine - INFO - Epoch(val)  [4][400/584]    eta: 0:00:02  time: 0.0132  data_time: 0.0017  memory: 392  
+2026/07/28 00:36:37 - mmengine - INFO - Epoch(val)  [4][450/584]    eta: 0:00:01  time: 0.0138  data_time: 0.0018  memory: 392  
+2026/07/28 00:36:38 - mmengine - INFO - Epoch(val)  [4][500/584]    eta: 0:00:01  time: 0.0137  data_time: 0.0021  memory: 392  
+2026/07/28 00:36:39 - mmengine - INFO - Epoch(val)  [4][550/584]    eta: 0:00:00  time: 0.0130  data_time: 0.0019  memory: 392  
+2026/07/28 00:36:40 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:36:41 - mmengine - INFO - bbox_mAP_copypaste: 0.010 0.062 0.000 0.010 0.000 -1.000
+2026/07/28 00:36:41 - mmengine - INFO - Epoch(val) [4][584/584]    coco/bbox_mAP: 0.0100  coco/bbox_mAP_50: 0.0620  coco/bbox_mAP_75: 0.0000  coco/bbox_mAP_s: 0.0100  coco/bbox_mAP_m: 0.0000  coco/bbox_mAP_l: -1.0000  data_time: 0.0020  time: 0.0138
+2026/07/28 00:36:48 - mmengine - INFO - Epoch(train)  [5][ 50/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:02:05  time: 0.1367  data_time: 0.0177  memory: 6270  grad_norm: 9.7885  loss: 1.6238  loss_cls: 0.2113  loss_bbox: 0.6864  loss_centerness: 0.5998  loss_pos: 0.1225  loss_offset: 0.0037
+2026/07/28 00:36:55 - mmengine - INFO - Epoch(train)  [5][100/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:01:57  time: 0.1357  data_time: 0.0159  memory: 6269  grad_norm: 8.3351  loss: 1.4521  loss_cls: 0.1642  loss_bbox: 0.5989  loss_centerness: 0.5839  loss_pos: 0.1003  loss_offset: 0.0048
+2026/07/28 00:37:02 - mmengine - INFO - Epoch(train)  [5][150/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:01:50  time: 0.1348  data_time: 0.0156  memory: 6269  grad_norm: 8.5120  loss: 1.4055  loss_cls: 0.1524  loss_bbox: 0.5590  loss_centerness: 0.5823  loss_pos: 0.1073  loss_offset: 0.0045
+2026/07/28 00:37:03 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:37:03 - mmengine - INFO - Saving checkpoint at 5 epochs
+2026/07/28 00:37:04 - mmengine - INFO - Epoch(val)  [5][ 50/584]    eta: 0:00:07  time: 0.0138  data_time: 0.0024  memory: 6269  
+2026/07/28 00:37:05 - mmengine - INFO - Epoch(val)  [5][100/584]    eta: 0:00:06  time: 0.0139  data_time: 0.0019  memory: 392  
+2026/07/28 00:37:05 - mmengine - INFO - Epoch(val)  [5][150/584]    eta: 0:00:05  time: 0.0137  data_time: 0.0021  memory: 392  
+2026/07/28 00:37:06 - mmengine - INFO - Epoch(val)  [5][200/584]    eta: 0:00:05  time: 0.0132  data_time: 0.0020  memory: 392  
+2026/07/28 00:37:07 - mmengine - INFO - Epoch(val)  [5][250/584]    eta: 0:00:04  time: 0.0134  data_time: 0.0018  memory: 392  
+2026/07/28 00:37:07 - mmengine - INFO - Epoch(val)  [5][300/584]    eta: 0:00:03  time: 0.0131  data_time: 0.0016  memory: 392  
+2026/07/28 00:37:08 - mmengine - INFO - Epoch(val)  [5][350/584]    eta: 0:00:03  time: 0.0138  data_time: 0.0021  memory: 392  
+2026/07/28 00:37:09 - mmengine - INFO - Epoch(val)  [5][400/584]    eta: 0:00:02  time: 0.0139  data_time: 0.0021  memory: 392  
+2026/07/28 00:37:10 - mmengine - INFO - Epoch(val)  [5][450/584]    eta: 0:00:01  time: 0.0149  data_time: 0.0028  memory: 392  
+2026/07/28 00:37:10 - mmengine - INFO - Epoch(val)  [5][500/584]    eta: 0:00:01  time: 0.0147  data_time: 0.0027  memory: 392  
+2026/07/28 00:37:11 - mmengine - INFO - Epoch(val)  [5][550/584]    eta: 0:00:00  time: 0.0127  data_time: 0.0019  memory: 392  
+2026/07/28 00:37:11 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:37:12 - mmengine - INFO - bbox_mAP_copypaste: 0.222 0.677 0.053 0.224 0.126 -1.000
+2026/07/28 00:37:12 - mmengine - INFO - Epoch(val) [5][584/584]    coco/bbox_mAP: 0.2220  coco/bbox_mAP_50: 0.6770  coco/bbox_mAP_75: 0.0530  coco/bbox_mAP_s: 0.2240  coco/bbox_mAP_m: 0.1260  coco/bbox_mAP_l: -1.0000  data_time: 0.0021  time: 0.0137
+2026/07/28 00:37:12 - mmengine - INFO - The previous best checkpoint /marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768/best_coco_bbox_mAP_75_epoch_3.pth is removed
+2026/07/28 00:37:12 - mmengine - INFO - The best checkpoint with 0.0530 coco/bbox_mAP_75 at 5 epoch is saved to best_coco_bbox_mAP_75_epoch_5.pth.
+2026/07/28 00:37:20 - mmengine - INFO - Epoch(train)  [6][ 50/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:01:42  time: 0.1369  data_time: 0.0179  memory: 6269  grad_norm: 6.4680  loss: 1.3479  loss_cls: 0.1584  loss_bbox: 0.4915  loss_centerness: 0.5825  loss_pos: 0.1114  loss_offset: 0.0040
+2026/07/28 00:37:26 - mmengine - INFO - Epoch(train)  [6][100/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:01:35  time: 0.1347  data_time: 0.0155  memory: 6269  grad_norm: 7.4954  loss: 1.4362  loss_cls: 0.1510  loss_bbox: 0.5818  loss_centerness: 0.5807  loss_pos: 0.1180  loss_offset: 0.0046
+2026/07/28 00:37:33 - mmengine - INFO - Epoch(train)  [6][150/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:01:28  time: 0.1347  data_time: 0.0157  memory: 6269  grad_norm: 8.8060  loss: 1.4207  loss_cls: 0.1468  loss_bbox: 0.6033  loss_centerness: 0.5775  loss_pos: 0.0896  loss_offset: 0.0034
+2026/07/28 00:37:34 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:37:34 - mmengine - INFO - Saving checkpoint at 6 epochs
+2026/07/28 00:37:35 - mmengine - INFO - Epoch(val)  [6][ 50/584]    eta: 0:00:07  time: 0.0135  data_time: 0.0023  memory: 6269  
+2026/07/28 00:37:36 - mmengine - INFO - Epoch(val)  [6][100/584]    eta: 0:00:06  time: 0.0123  data_time: 0.0018  memory: 392  
+2026/07/28 00:37:37 - mmengine - INFO - Epoch(val)  [6][150/584]    eta: 0:00:05  time: 0.0127  data_time: 0.0020  memory: 392  
+2026/07/28 00:37:37 - mmengine - INFO - Epoch(val)  [6][200/584]    eta: 0:00:04  time: 0.0125  data_time: 0.0019  memory: 392  
+2026/07/28 00:37:38 - mmengine - INFO - Epoch(val)  [6][250/584]    eta: 0:00:04  time: 0.0121  data_time: 0.0017  memory: 392  
+2026/07/28 00:37:38 - mmengine - INFO - Epoch(val)  [6][300/584]    eta: 0:00:03  time: 0.0119  data_time: 0.0016  memory: 392  
+2026/07/28 00:37:39 - mmengine - INFO - Epoch(val)  [6][350/584]    eta: 0:00:02  time: 0.0128  data_time: 0.0020  memory: 392  
+2026/07/28 00:37:40 - mmengine - INFO - Epoch(val)  [6][400/584]    eta: 0:00:02  time: 0.0126  data_time: 0.0017  memory: 392  
+2026/07/28 00:37:40 - mmengine - INFO - Epoch(val)  [6][450/584]    eta: 0:00:01  time: 0.0131  data_time: 0.0019  memory: 392  
+2026/07/28 00:37:41 - mmengine - INFO - Epoch(val)  [6][500/584]    eta: 0:00:01  time: 0.0129  data_time: 0.0021  memory: 392  
+2026/07/28 00:37:42 - mmengine - INFO - Epoch(val)  [6][550/584]    eta: 0:00:00  time: 0.0129  data_time: 0.0019  memory: 392  
+2026/07/28 00:37:42 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:37:42 - mmengine - INFO - bbox_mAP_copypaste: 0.028 0.098 0.011 0.029 0.109 -1.000
+2026/07/28 00:37:42 - mmengine - INFO - Epoch(val) [6][584/584]    coco/bbox_mAP: 0.0280  coco/bbox_mAP_50: 0.0980  coco/bbox_mAP_75: 0.0110  coco/bbox_mAP_s: 0.0290  coco/bbox_mAP_m: 0.1090  coco/bbox_mAP_l: -1.0000  data_time: 0.0019  time: 0.0126
+2026/07/28 00:37:49 - mmengine - INFO - Epoch(train)  [7][ 50/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:01:20  time: 0.1362  data_time: 0.0174  memory: 6269  grad_norm: 7.0849  loss: 1.3075  loss_cls: 0.1292  loss_bbox: 0.5067  loss_centerness: 0.5763  loss_pos: 0.0916  loss_offset: 0.0037
+2026/07/28 00:37:50 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:37:56 - mmengine - INFO - Epoch(train)  [7][100/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:01:13  time: 0.1353  data_time: 0.0163  memory: 6269  grad_norm: 6.4600  loss: 1.3278  loss_cls: 0.1350  loss_bbox: 0.5196  loss_centerness: 0.5783  loss_pos: 0.0910  loss_offset: 0.0038
+2026/07/28 00:38:03 - mmengine - INFO - Epoch(train)  [7][150/158]  base_lr: 1.0000e-02 lr: 5.0000e-03  eta: 0:01:06  time: 0.1328  data_time: 0.0157  memory: 6269  grad_norm: 8.1389  loss: 1.4007  loss_cls: 0.1399  loss_bbox: 0.5909  loss_centerness: 0.5791  loss_pos: 0.0867  loss_offset: 0.0041
+2026/07/28 00:38:04 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:38:04 - mmengine - INFO - Saving checkpoint at 7 epochs
+2026/07/28 00:38:05 - mmengine - INFO - Epoch(val)  [7][ 50/584]    eta: 0:00:06  time: 0.0130  data_time: 0.0023  memory: 6269  
+2026/07/28 00:38:06 - mmengine - INFO - Epoch(val)  [7][100/584]    eta: 0:00:06  time: 0.0126  data_time: 0.0019  memory: 392  
+2026/07/28 00:38:06 - mmengine - INFO - Epoch(val)  [7][150/584]    eta: 0:00:05  time: 0.0130  data_time: 0.0020  memory: 392  
+2026/07/28 00:38:07 - mmengine - INFO - Epoch(val)  [7][200/584]    eta: 0:00:04  time: 0.0124  data_time: 0.0019  memory: 392  
+2026/07/28 00:38:07 - mmengine - INFO - Epoch(val)  [7][250/584]    eta: 0:00:04  time: 0.0125  data_time: 0.0017  memory: 392  
+2026/07/28 00:38:08 - mmengine - INFO - Epoch(val)  [7][300/584]    eta: 0:00:03  time: 0.0129  data_time: 0.0016  memory: 392  
+2026/07/28 00:38:09 - mmengine - INFO - Epoch(val)  [7][350/584]    eta: 0:00:02  time: 0.0127  data_time: 0.0020  memory: 392  
+2026/07/28 00:38:09 - mmengine - INFO - Epoch(val)  [7][400/584]    eta: 0:00:02  time: 0.0125  data_time: 0.0016  memory: 392  
+2026/07/28 00:38:10 - mmengine - INFO - Epoch(val)  [7][450/584]    eta: 0:00:01  time: 0.0128  data_time: 0.0019  memory: 392  
+2026/07/28 00:38:11 - mmengine - INFO - Epoch(val)  [7][500/584]    eta: 0:00:01  time: 0.0130  data_time: 0.0021  memory: 392  
+2026/07/28 00:38:11 - mmengine - INFO - Epoch(val)  [7][550/584]    eta: 0:00:00  time: 0.0152  data_time: 0.0019  memory: 392  
+2026/07/28 00:38:12 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:38:12 - mmengine - INFO - bbox_mAP_copypaste: 0.093 0.422 0.004 0.095 0.023 -1.000
+2026/07/28 00:38:12 - mmengine - INFO - Epoch(val) [7][584/584]    coco/bbox_mAP: 0.0930  coco/bbox_mAP_50: 0.4220  coco/bbox_mAP_75: 0.0040  coco/bbox_mAP_s: 0.0950  coco/bbox_mAP_m: 0.0230  coco/bbox_mAP_l: -1.0000  data_time: 0.0019  time: 0.0129
+2026/07/28 00:38:19 - mmengine - INFO - Epoch(train)  [8][ 50/158]  base_lr: 1.0000e-03 lr: 5.0000e-04  eta: 0:00:58  time: 0.1381  data_time: 0.0187  memory: 6269  grad_norm: 5.5559  loss: 1.1847  loss_cls: 0.1144  loss_bbox: 0.4238  loss_centerness: 0.5720  loss_pos: 0.0706  loss_offset: 0.0039
+2026/07/28 00:38:26 - mmengine - INFO - Epoch(train)  [8][100/158]  base_lr: 1.0000e-03 lr: 5.0000e-04  eta: 0:00:51  time: 0.1336  data_time: 0.0152  memory: 6269  grad_norm: 4.5785  loss: 1.1314  loss_cls: 0.1147  loss_bbox: 0.3717  loss_centerness: 0.5723  loss_pos: 0.0691  loss_offset: 0.0037
+2026/07/28 00:38:33 - mmengine - INFO - Epoch(train)  [8][150/158]  base_lr: 1.0000e-03 lr: 5.0000e-04  eta: 0:00:44  time: 0.1330  data_time: 0.0153  memory: 6269  grad_norm: 4.5976  loss: 1.1110  loss_cls: 0.1095  loss_bbox: 0.3610  loss_centerness: 0.5726  loss_pos: 0.0643  loss_offset: 0.0037
+2026/07/28 00:38:34 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:38:34 - mmengine - INFO - Saving checkpoint at 8 epochs
+2026/07/28 00:38:35 - mmengine - INFO - Epoch(val)  [8][ 50/584]    eta: 0:00:06  time: 0.0129  data_time: 0.0023  memory: 6269  
+2026/07/28 00:38:36 - mmengine - INFO - Epoch(val)  [8][100/584]    eta: 0:00:06  time: 0.0129  data_time: 0.0019  memory: 392  
+2026/07/28 00:38:36 - mmengine - INFO - Epoch(val)  [8][150/584]    eta: 0:00:05  time: 0.0133  data_time: 0.0020  memory: 392  
+2026/07/28 00:38:37 - mmengine - INFO - Epoch(val)  [8][200/584]    eta: 0:00:04  time: 0.0126  data_time: 0.0019  memory: 392  
+2026/07/28 00:38:37 - mmengine - INFO - Epoch(val)  [8][250/584]    eta: 0:00:04  time: 0.0126  data_time: 0.0017  memory: 392  
+2026/07/28 00:38:38 - mmengine - INFO - Epoch(val)  [8][300/584]    eta: 0:00:03  time: 0.0121  data_time: 0.0015  memory: 392  
+2026/07/28 00:38:39 - mmengine - INFO - Epoch(val)  [8][350/584]    eta: 0:00:02  time: 0.0124  data_time: 0.0019  memory: 392  
+2026/07/28 00:38:39 - mmengine - INFO - Epoch(val)  [8][400/584]    eta: 0:00:02  time: 0.0121  data_time: 0.0016  memory: 392  
+2026/07/28 00:38:40 - mmengine - INFO - Epoch(val)  [8][450/584]    eta: 0:00:01  time: 0.0129  data_time: 0.0020  memory: 392  
+2026/07/28 00:38:41 - mmengine - INFO - Epoch(val)  [8][500/584]    eta: 0:00:01  time: 0.0131  data_time: 0.0020  memory: 392  
+2026/07/28 00:38:41 - mmengine - INFO - Epoch(val)  [8][550/584]    eta: 0:00:00  time: 0.0125  data_time: 0.0019  memory: 392  
+2026/07/28 00:38:42 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:38:42 - mmengine - INFO - bbox_mAP_copypaste: 0.243 0.717 0.080 0.247 0.097 -1.000
+2026/07/28 00:38:42 - mmengine - INFO - Epoch(val) [8][584/584]    coco/bbox_mAP: 0.2430  coco/bbox_mAP_50: 0.7170  coco/bbox_mAP_75: 0.0800  coco/bbox_mAP_s: 0.2470  coco/bbox_mAP_m: 0.0970  coco/bbox_mAP_l: -1.0000  data_time: 0.0019  time: 0.0127
+2026/07/28 00:38:42 - mmengine - INFO - The previous best checkpoint /marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768/best_coco_bbox_mAP_75_epoch_5.pth is removed
+2026/07/28 00:38:43 - mmengine - INFO - The best checkpoint with 0.0800 coco/bbox_mAP_75 at 8 epoch is saved to best_coco_bbox_mAP_75_epoch_8.pth.
+2026/07/28 00:38:50 - mmengine - INFO - Epoch(train)  [9][ 50/158]  base_lr: 1.0000e-03 lr: 5.0000e-04  eta: 0:00:36  time: 0.1356  data_time: 0.0170  memory: 6269  grad_norm: 5.0729  loss: 1.0906  loss_cls: 0.1067  loss_bbox: 0.3523  loss_centerness: 0.5700  loss_pos: 0.0585  loss_offset: 0.0031
+2026/07/28 00:38:57 - mmengine - INFO - Epoch(train)  [9][100/158]  base_lr: 1.0000e-03 lr: 5.0000e-04  eta: 0:00:29  time: 0.1332  data_time: 0.0150  memory: 6269  grad_norm: 5.1711  loss: 1.1066  loss_cls: 0.1049  loss_bbox: 0.3613  loss_centerness: 0.5701  loss_pos: 0.0671  loss_offset: 0.0032
+2026/07/28 00:39:03 - mmengine - INFO - Epoch(train)  [9][150/158]  base_lr: 1.0000e-03 lr: 5.0000e-04  eta: 0:00:22  time: 0.1325  data_time: 0.0149  memory: 6269  grad_norm: 4.9784  loss: 1.1225  loss_cls: 0.1084  loss_bbox: 0.3705  loss_centerness: 0.5711  loss_pos: 0.0687  loss_offset: 0.0038
+2026/07/28 00:39:04 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:39:04 - mmengine - INFO - Saving checkpoint at 9 epochs
+2026/07/28 00:39:05 - mmengine - INFO - Epoch(val)  [9][ 50/584]    eta: 0:00:07  time: 0.0135  data_time: 0.0023  memory: 6269  
+2026/07/28 00:39:06 - mmengine - INFO - Epoch(val)  [9][100/584]    eta: 0:00:06  time: 0.0127  data_time: 0.0018  memory: 392  
+2026/07/28 00:39:07 - mmengine - INFO - Epoch(val)  [9][150/584]    eta: 0:00:05  time: 0.0139  data_time: 0.0020  memory: 392  
+2026/07/28 00:39:07 - mmengine - INFO - Epoch(val)  [9][200/584]    eta: 0:00:05  time: 0.0142  data_time: 0.0020  memory: 392  
+2026/07/28 00:39:08 - mmengine - INFO - Epoch(val)  [9][250/584]    eta: 0:00:04  time: 0.0131  data_time: 0.0018  memory: 392  
+2026/07/28 00:39:09 - mmengine - INFO - Epoch(val)  [9][300/584]    eta: 0:00:03  time: 0.0123  data_time: 0.0016  memory: 392  
+2026/07/28 00:39:09 - mmengine - INFO - Epoch(val)  [9][350/584]    eta: 0:00:03  time: 0.0124  data_time: 0.0019  memory: 392  
+2026/07/28 00:39:10 - mmengine - INFO - Epoch(val)  [9][400/584]    eta: 0:00:02  time: 0.0123  data_time: 0.0018  memory: 392  
+2026/07/28 00:39:11 - mmengine - INFO - Epoch(val)  [9][450/584]    eta: 0:00:01  time: 0.0139  data_time: 0.0020  memory: 392  
+2026/07/28 00:39:11 - mmengine - INFO - Epoch(val)  [9][500/584]    eta: 0:00:01  time: 0.0148  data_time: 0.0028  memory: 392  
+2026/07/28 00:39:12 - mmengine - INFO - Epoch(val)  [9][550/584]    eta: 0:00:00  time: 0.0153  data_time: 0.0028  memory: 392  
+2026/07/28 00:39:13 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:39:13 - mmengine - INFO - bbox_mAP_copypaste: 0.278 0.768 0.093 0.280 0.187 -1.000
+2026/07/28 00:39:13 - mmengine - INFO - Epoch(val) [9][584/584]    coco/bbox_mAP: 0.2780  coco/bbox_mAP_50: 0.7680  coco/bbox_mAP_75: 0.0930  coco/bbox_mAP_s: 0.2800  coco/bbox_mAP_m: 0.1870  coco/bbox_mAP_l: -1.0000  data_time: 0.0020  time: 0.0134
+2026/07/28 00:39:13 - mmengine - INFO - The previous best checkpoint /marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768/best_coco_bbox_mAP_75_epoch_8.pth is removed
+2026/07/28 00:39:14 - mmengine - INFO - The best checkpoint with 0.0930 coco/bbox_mAP_75 at 9 epoch is saved to best_coco_bbox_mAP_75_epoch_9.pth.
+2026/07/28 00:39:21 - mmengine - INFO - Epoch(train) [10][ 50/158]  base_lr: 1.0000e-04 lr: 5.0000e-05  eta: 0:00:14  time: 0.1346  data_time: 0.0173  memory: 6269  grad_norm: 3.9817  loss: 1.0602  loss_cls: 0.0992  loss_bbox: 0.3334  loss_centerness: 0.5690  loss_pos: 0.0549  loss_offset: 0.0038
+2026/07/28 00:39:28 - mmengine - INFO - Epoch(train) [10][100/158]  base_lr: 1.0000e-04 lr: 5.0000e-05  eta: 0:00:07  time: 0.1369  data_time: 0.0171  memory: 6269  grad_norm: 4.2568  loss: 1.0803  loss_cls: 0.1040  loss_bbox: 0.3393  loss_centerness: 0.5703  loss_pos: 0.0633  loss_offset: 0.0035
+2026/07/28 00:39:34 - mmengine - INFO - Epoch(train) [10][150/158]  base_lr: 1.0000e-04 lr: 5.0000e-05  eta: 0:00:01  time: 0.1334  data_time: 0.0154  memory: 6269  grad_norm: 4.3441  loss: 1.0743  loss_cls: 0.1050  loss_bbox: 0.3386  loss_centerness: 0.5699  loss_pos: 0.0573  loss_offset: 0.0035
+2026/07/28 00:39:35 - mmengine - INFO - Exp name: patched_config_20260728_003429
+2026/07/28 00:39:35 - mmengine - INFO - Saving checkpoint at 10 epochs
+2026/07/28 00:39:37 - mmengine - INFO - Epoch(val) [10][ 50/584]    eta: 0:00:06  time: 0.0131  data_time: 0.0023  memory: 6269  
+2026/07/28 00:39:37 - mmengine - INFO - Epoch(val) [10][100/584]    eta: 0:00:06  time: 0.0132  data_time: 0.0019  memory: 392  
+2026/07/28 00:39:38 - mmengine - INFO - Epoch(val) [10][150/584]    eta: 0:00:05  time: 0.0127  data_time: 0.0020  memory: 392  
+2026/07/28 00:39:38 - mmengine - INFO - Epoch(val) [10][200/584]    eta: 0:00:04  time: 0.0128  data_time: 0.0019  memory: 392  
+2026/07/28 00:39:39 - mmengine - INFO - Epoch(val) [10][250/584]    eta: 0:00:04  time: 0.0124  data_time: 0.0017  memory: 392  
+2026/07/28 00:39:40 - mmengine - INFO - Epoch(val) [10][300/584]    eta: 0:00:03  time: 0.0121  data_time: 0.0015  memory: 392  
+2026/07/28 00:39:40 - mmengine - INFO - Epoch(val) [10][350/584]    eta: 0:00:02  time: 0.0128  data_time: 0.0019  memory: 392  
+2026/07/28 00:39:41 - mmengine - INFO - Epoch(val) [10][400/584]    eta: 0:00:02  time: 0.0123  data_time: 0.0016  memory: 392  
+2026/07/28 00:39:42 - mmengine - INFO - Epoch(val) [10][450/584]    eta: 0:00:01  time: 0.0137  data_time: 0.0019  memory: 392  
+2026/07/28 00:39:42 - mmengine - INFO - Epoch(val) [10][500/584]    eta: 0:00:01  time: 0.0137  data_time: 0.0021  memory: 392  
+2026/07/28 00:39:43 - mmengine - INFO - Epoch(val) [10][550/584]    eta: 0:00:00  time: 0.0136  data_time: 0.0021  memory: 392  
+2026/07/28 00:39:44 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:39:44 - mmengine - INFO - bbox_mAP_copypaste: 0.277 0.750 0.104 0.277 0.236 -1.000
+2026/07/28 00:39:44 - mmengine - INFO - Epoch(val) [10][584/584]    coco/bbox_mAP: 0.2770  coco/bbox_mAP_50: 0.7500  coco/bbox_mAP_75: 0.1040  coco/bbox_mAP_s: 0.2770  coco/bbox_mAP_m: 0.2360  coco/bbox_mAP_l: -1.0000  data_time: 0.0019  time: 0.0129
+2026/07/28 00:39:44 - mmengine - INFO - The previous best checkpoint /marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768/best_coco_bbox_mAP_75_epoch_9.pth is removed
+2026/07/28 00:39:44 - mmengine - INFO - The best checkpoint with 0.1040 coco/bbox_mAP_75 at 10 epoch is saved to best_coco_bbox_mAP_75_epoch_10.pth.

haar_force_phase_768/20260728_003429/vis_data/20260728_003429.json

@@ -0,0 +1,40 @@
+{"base_lr": 0.003333333333333333, "lr": 0.0016666666666666666, "data_time": 0.02405730724334717, "grad_norm": 52.913512744903564, "loss": 3.3625831246376037, "loss_cls": 0.6375094386935234, "loss_bbox": 1.4166621673107147, "loss_centerness": 0.6474498033523559, "loss_pos": 0.6498267774656415, "loss_offset": 0.011134946941165253, "time": 0.1760618829727173, "epoch": 1, "iter": 50, "memory": 6269, "step": 50}
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+{"base_lr": 0.003333333333333333, "lr": 0.0016666666666666666, "data_time": 0.0165403413772583, "grad_norm": 14.041195802688598, "loss": 1.8410870099067689, "loss_cls": 0.2647103378176689, "loss_bbox": 0.8165002620220184, "loss_centerness": 0.6039454388618469, "loss_pos": 0.15009017881006004, "loss_offset": 0.00584080092376098, "time": 0.13689916133880614, "epoch": 1, "iter": 150, "memory": 6269, "step": 150}
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+{"coco/bbox_mAP": 0.277, "coco/bbox_mAP_50": 0.75, "coco/bbox_mAP_75": 0.104, "coco/bbox_mAP_s": 0.277, "coco/bbox_mAP_m": 0.236, "coco/bbox_mAP_l": -1.0, "data_time": 0.0018951717604938736, "time": 0.012925849409184904, "step": 10}

haar_force_phase_768/20260728_003429/vis_data/config.py

@@ -0,0 +1,314 @@
+auto_scale_lr = dict(base_batch_size=16, enable=True)
+backend_args = None
+custom_imports = dict(imports=[
+    'projects.pahr',
+])
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=1,
+        max_keep_ckpts=1,
+        rule='greater',
+        save_best='coco/bbox_mAP_75',
+        save_last=True,
+        type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = None
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+model = dict(
+    backbone=dict(
+        depth=50,
+        frozen_stages=1,
+        init_cfg=dict(
+            checkpoint='open-mmlab://detectron/resnet50_caffe',
+            type='Pretrained'),
+        norm_cfg=dict(requires_grad=False, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        style='caffe',
+        type='ResNet'),
+    bbox_head=dict(
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(loss_weight=1.0, type='IoULoss'),
+        loss_centerness=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        loss_cls=dict(
+            alpha=0.25,
+            gamma=2.0,
+            loss_weight=1.0,
+            type='FocalLoss',
+            use_sigmoid=True),
+        num_classes=1,
+        stacked_convs=4,
+        strides=[
+            8,
+            16,
+            32,
+            64,
+            128,
+        ],
+        type='FCOSHead'),
+    data_preprocessor=dict(
+        bgr_to_rgb=False,
+        mean=[
+            102.9801,
+            115.9465,
+            122.7717,
+        ],
+        pad_size_divisor=32,
+        std=[
+            1.0,
+            1.0,
+            1.0,
+        ],
+        type='DetDataPreprocessor'),
+    loss_offset_weight=0.1,
+    loss_pos_weight=0.1,
+    measurement_center_gate_floor=0.0,
+    measurement_center_weight=0.1,
+    measurement_channels=16,
+    measurement_fixed_strength=None,
+    measurement_phase_weight=0.1,
+    measurement_size_blend=0.5,
+    measurement_size_weight=0.1,
+    measurement_tiny_limit=24.0,
+    neck=dict(
+        add_extra_convs='on_output',
+        correction_gain=0.0,
+        correction_gate_floor=0.0,
+        detach_position_gate=False,
+        detail_channels=64,
+        gate_power=1.0,
+        guide_channels=0,
+        in_channels=[
+            256,
+            512,
+            1024,
+            2048,
+        ],
+        locator_channels=64,
+        num_outs=5,
+        out_channels=256,
+        relu_before_extra_convs=True,
+        start_level=1,
+        target_correction_ratio=0.0,
+        type='PAHRFPN',
+        use_output_gate=False),
+    phase_gate_floor=1.0,
+    phase_strength=1.0,
+    position_stride=8,
+    test_cfg=dict(
+        max_per_img=100,
+        min_bbox_size=0,
+        nms=dict(iou_threshold=0.5, type='nms'),
+        nms_pre=1000,
+        score_thr=0.05),
+    tiny_max_sqrt_area=16.0,
+    type='PAHRFCOS',
+    use_phase_shift=True,
+    use_tiny_measurement=False)
+optim_wrapper = dict(
+    clip_grad=dict(max_norm=35, norm_type=2),
+    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0001),
+    paramwise_cfg=dict(bias_decay_mult=0.0, bias_lr_mult=2.0),
+    type='OptimWrapper')
+pahr_variant = dict(
+    correction_gain=0.0,
+    detail_lr_mult=1.0,
+    giou=False,
+    guide_channels=0,
+    image_size=768,
+    measurement_center_gate_floor=0.0,
+    measurement_fixed_strength=None,
+    measurement_size_blend=0.5,
+    name='haar_force_phase_768',
+    phase_gate_floor=1.0,
+    phase_shift=True,
+    phase_strength=1.0,
+    scaled_schedule=True,
+    target_correction_ratio=0.0,
+    use_output_gate=False,
+    use_tiny_measurement=False,
+    v3_gate=False)
+param_scheduler = [
+    dict(
+        begin=0,
+        by_epoch=False,
+        end=500,
+        factor=0.3333333333333333,
+        type='ConstantLR'),
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=10,
+        gamma=0.1,
+        milestones=[
+            7,
+            9,
+        ],
+        type='MultiStepLR'),
+]
+randomness = dict(seed=42)
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        meta_keys=(
+            'img_id',
+            'img_path',
+            'ori_shape',
+            'img_shape',
+            'scale_factor',
+        ),
+        type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=10, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=8,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/train.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='DetLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = '/marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768'

haar_force_phase_768/20260728_003429/vis_data/scalars.json

@@ -0,0 +1,40 @@
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haar_force_phase_768/best_coco_bbox_mAP_75_epoch_10.pth

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+size 130077061

haar_force_phase_768/epoch_10.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:07aff23b45b9f3892e4165ec27d6f44d4cb70655f121a7080f411091c1318e7c
+size 258212465

haar_force_phase_768/last_checkpoint

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+/marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768/epoch_10.pth

haar_force_phase_768/patched_config.py

@@ -0,0 +1,314 @@
+auto_scale_lr = dict(base_batch_size=16, enable=True)
+backend_args = None
+custom_imports = dict(imports=[
+    'projects.pahr',
+])
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=1,
+        max_keep_ckpts=1,
+        rule='greater',
+        save_best='coco/bbox_mAP_75',
+        save_last=True,
+        type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = None
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+model = dict(
+    backbone=dict(
+        depth=50,
+        frozen_stages=1,
+        init_cfg=dict(
+            checkpoint='open-mmlab://detectron/resnet50_caffe',
+            type='Pretrained'),
+        norm_cfg=dict(requires_grad=False, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        style='caffe',
+        type='ResNet'),
+    bbox_head=dict(
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(loss_weight=1.0, type='IoULoss'),
+        loss_centerness=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        loss_cls=dict(
+            alpha=0.25,
+            gamma=2.0,
+            loss_weight=1.0,
+            type='FocalLoss',
+            use_sigmoid=True),
+        num_classes=1,
+        stacked_convs=4,
+        strides=[
+            8,
+            16,
+            32,
+            64,
+            128,
+        ],
+        type='FCOSHead'),
+    data_preprocessor=dict(
+        bgr_to_rgb=False,
+        mean=[
+            102.9801,
+            115.9465,
+            122.7717,
+        ],
+        pad_size_divisor=32,
+        std=[
+            1.0,
+            1.0,
+            1.0,
+        ],
+        type='DetDataPreprocessor'),
+    loss_offset_weight=0.1,
+    loss_pos_weight=0.1,
+    measurement_center_gate_floor=0.0,
+    measurement_center_weight=0.1,
+    measurement_channels=16,
+    measurement_fixed_strength=None,
+    measurement_phase_weight=0.1,
+    measurement_size_blend=0.5,
+    measurement_size_weight=0.1,
+    measurement_tiny_limit=24.0,
+    neck=dict(
+        add_extra_convs='on_output',
+        correction_gain=0.0,
+        correction_gate_floor=0.0,
+        detach_position_gate=False,
+        detail_channels=64,
+        gate_power=1.0,
+        guide_channels=0,
+        in_channels=[
+            256,
+            512,
+            1024,
+            2048,
+        ],
+        locator_channels=64,
+        num_outs=5,
+        out_channels=256,
+        relu_before_extra_convs=True,
+        start_level=1,
+        target_correction_ratio=0.0,
+        type='PAHRFPN',
+        use_output_gate=False),
+    phase_gate_floor=1.0,
+    phase_strength=1.0,
+    position_stride=8,
+    test_cfg=dict(
+        max_per_img=100,
+        min_bbox_size=0,
+        nms=dict(iou_threshold=0.5, type='nms'),
+        nms_pre=1000,
+        score_thr=0.05),
+    tiny_max_sqrt_area=16.0,
+    type='PAHRFCOS',
+    use_phase_shift=True,
+    use_tiny_measurement=False)
+optim_wrapper = dict(
+    clip_grad=dict(max_norm=35, norm_type=2),
+    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0001),
+    paramwise_cfg=dict(bias_decay_mult=0.0, bias_lr_mult=2.0),
+    type='OptimWrapper')
+pahr_variant = dict(
+    correction_gain=0.0,
+    detail_lr_mult=1.0,
+    giou=False,
+    guide_channels=0,
+    image_size=768,
+    measurement_center_gate_floor=0.0,
+    measurement_fixed_strength=None,
+    measurement_size_blend=0.5,
+    name='haar_force_phase_768',
+    phase_gate_floor=1.0,
+    phase_shift=True,
+    phase_strength=1.0,
+    scaled_schedule=True,
+    target_correction_ratio=0.0,
+    use_output_gate=False,
+    use_tiny_measurement=False,
+    v3_gate=False)
+param_scheduler = [
+    dict(
+        begin=0,
+        by_epoch=False,
+        end=500,
+        factor=0.3333333333333333,
+        type='ConstantLR'),
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=10,
+        gamma=0.1,
+        milestones=[
+            7,
+            9,
+        ],
+        type='MultiStepLR'),
+]
+randomness = dict(seed=42)
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        meta_keys=(
+            'img_id',
+            'img_path',
+            'ori_shape',
+            'img_shape',
+            'scale_factor',
+        ),
+        type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=10, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=8,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/train.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='DetLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = '/marimo/mmdet_code/mmdetection/work_dirs/levir_pahr_forced_screen/haar_force_phase_768'