depthanything_vitb_u4k/patchfusion/20240315_193032.log

2024/03/15 19:30:41 - patchstitcher - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0]
    CUDA available: True
    numpy_random_seed: 621
    GPU 0,1,2,3: NVIDIA A100-SXM4-80GB
    CUDA_HOME: /sw/rl9g/cuda/11.8/rl9_binary
    NVCC: Cuda compilation tools, release 11.8, V11.8.89
    GCC: gcc (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2)
    PyTorch: 2.1.2
    PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.8
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.7
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

    TorchVision: 0.16.2
    OpenCV: 4.8.1
    MMEngine: 0.10.2

Runtime environment:
    cudnn_benchmark: True
    mp_cfg: {'mp_start_method': 'forkserver'}
    dist_cfg: {'backend': 'nccl'}
    seed: 621
    Distributed launcher: pytorch
    Distributed training: True
    GPU number: 4
------------------------------------------------------------

2024/03/15 19:30:41 - patchstitcher - INFO - Config:
collect_input_args = [
    'image_lr',
    'crops_image_hr',
    'depth_gt',
    'crop_depths',
    'bboxs',
    'image_hr',
]
convert_syncbn = True
debug = False
env_cfg = dict(
    cudnn_benchmark=True,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='forkserver'))
find_unused_parameters = True
general_dataloader = dict(
    batch_size=1,
    dataset=dict(
        dataset_name='', gt_dir=None, rgb_image_dir='', type='ImageDataset'),
    num_workers=2)
launcher = 'pytorch'
log_name = 'patchfusion'
max_depth = 80
min_depth = 0.001
model = dict(
    coarse_branch=dict(
        attractor_alpha=1000,
        attractor_gamma=2,
        attractor_kind='mean',
        attractor_type='inv',
        aug=True,
        bin_centers_type='softplus',
        bin_embedding_dim=128,
        clip_grad=0.1,
        dataset='nyu',
        depth_anything=True,
        distributed=True,
        do_resize=False,
        force_keep_ar=True,
        freeze_midas_bn=True,
        gpu='NULL',
        img_size=[
            392,
            518,
        ],
        inverse_midas=False,
        log_images_every=0.1,
        max_depth=80,
        max_temp=50.0,
        max_translation=100,
        memory_efficient=True,
        midas_model_type='vitb',
        min_depth=0.001,
        min_temp=0.0212,
        model='zoedepth',
        n_attractors=[
            16,
            8,
            4,
            1,
        ],
        n_bins=64,
        name='ZoeDepth',
        notes='',
        output_distribution='logbinomial',
        prefetch=False,
        pretrained_resource='local::./work_dir/DepthAnything_vitb.pt',
        print_losses=False,
        project='ZoeDepth',
        random_crop=False,
        random_translate=False,
        root='.',
        save_dir='',
        shared_dict='NULL',
        tags='',
        train_midas=True,
        translate_prob=0.2,
        type='DA-ZoeDepth',
        uid='NULL',
        use_amp=False,
        use_pretrained_midas=True,
        use_shared_dict=False,
        validate_every=0.25,
        version_name='v1',
        workers=16),
    fine_branch=dict(
        attractor_alpha=1000,
        attractor_gamma=2,
        attractor_kind='mean',
        attractor_type='inv',
        aug=True,
        bin_centers_type='softplus',
        bin_embedding_dim=128,
        clip_grad=0.1,
        dataset='nyu',
        depth_anything=True,
        distributed=True,
        do_resize=False,
        force_keep_ar=True,
        freeze_midas_bn=True,
        gpu='NULL',
        img_size=[
            392,
            518,
        ],
        inverse_midas=False,
        log_images_every=0.1,
        max_depth=80,
        max_temp=50.0,
        max_translation=100,
        memory_efficient=True,
        midas_model_type='vitb',
        min_depth=0.001,
        min_temp=0.0212,
        model='zoedepth',
        n_attractors=[
            16,
            8,
            4,
            1,
        ],
        n_bins=64,
        name='ZoeDepth',
        notes='',
        output_distribution='logbinomial',
        prefetch=False,
        pretrained_resource='local::./work_dir/DepthAnything_vitb.pt',
        print_losses=False,
        project='ZoeDepth',
        random_crop=False,
        random_translate=False,
        root='.',
        save_dir='',
        shared_dict='NULL',
        tags='',
        train_midas=True,
        translate_prob=0.2,
        type='DA-ZoeDepth',
        uid='NULL',
        use_amp=False,
        use_pretrained_midas=True,
        use_shared_dict=False,
        validate_every=0.25,
        version_name='v1',
        workers=16),
    guided_fusion=dict(
        g2l=True,
        in_channels=[
            32,
            128,
            128,
            128,
            128,
            128,
        ],
        n_channels=5,
        num_patches=[
            203056,
            66304,
            16576,
            4144,
            1036,
            266,
        ],
        patch_process_shape=(
            392,
            518,
        ),
        type='GuidedFusionPatchFusion'),
    max_depth=80,
    min_depth=0.001,
    patch_process_shape=(
        392,
        518,
    ),
    pretrain_model=[
        './work_dir/depthanything_vitb_u4k/coarse_pretrain/checkpoint_24.pth',
        './work_dir/depthanything_vitb_u4k/fine_pretrain/checkpoint_24.pth',
    ],
    sigloss=dict(type='SILogLoss'),
    type='PatchFusion')
optim_wrapper = dict(
    clip_grad=dict(max_norm=0.1, norm_type=2, type='norm'),
    optimizer=dict(lr=0.0001, type='AdamW', weight_decay=0.001),
    paramwise_cfg=dict(bypass_duplicate=True, custom_keys=dict()))
param_scheduler = dict(
    base_momentum=0.85,
    cycle_momentum=True,
    div_factor=10,
    final_div_factor=10000,
    max_momentum=0.95,
    pct_start=0.25,
    three_phase=False)
project = 'patchfusion'
tags = [
    'patchfusion',
    'da',
    'vitb',
]
test_in_dataloader = dict(
    batch_size=1,
    dataset=dict(
        data_root='./data/u4k',
        max_depth=80,
        min_depth=0.001,
        mode='infer',
        split='./data/u4k/splits/test.txt',
        transform_cfg=dict(network_process_size=[
            384,
            512,
        ]),
        type='UnrealStereo4kDataset'),
    num_workers=2)
test_out_dataloader = dict(
    batch_size=1,
    dataset=dict(
        data_root='./data/u4k',
        max_depth=80,
        min_depth=0.001,
        mode='infer',
        split='./data/u4k/splits/test_out.txt',
        transform_cfg=dict(network_process_size=[
            384,
            512,
        ]),
        type='UnrealStereo4kDataset'),
    num_workers=2)
train_cfg = dict(
    eval_start=0,
    log_interval=100,
    max_epochs=16,
    save_checkpoint_interval=16,
    train_log_img_interval=500,
    val_interval=2,
    val_log_img_interval=50,
    val_type='epoch_base')
train_dataloader = dict(
    batch_size=4,
    dataset=dict(
        data_root='./data/u4k',
        max_depth=80,
        min_depth=0.001,
        mode='train',
        resize_mode='depth-anything',
        split='./data/u4k/splits/train.txt',
        transform_cfg=dict(
            degree=1.0, network_process_size=[
                392,
                518,
            ], random_crop=True),
        type='UnrealStereo4kDataset'),
    num_workers=4)
val_dataloader = dict(
    batch_size=1,
    dataset=dict(
        data_root='./data/u4k',
        max_depth=80,
        min_depth=0.001,
        mode='infer',
        resize_mode='depth-anything',
        split='./data/u4k/splits/val.txt',
        transform_cfg=dict(degree=1.0, network_process_size=[
            392,
            518,
        ]),
        type='UnrealStereo4kDataset'),
    num_workers=2)
work_dir = './work_dir/depthanything_vitb_u4k/patchfusion'
zoe_depth_config = dict(
    attractor_alpha=1000,
    attractor_gamma=2,
    attractor_kind='mean',
    attractor_type='inv',
    aug=True,
    bin_centers_type='softplus',
    bin_embedding_dim=128,
    clip_grad=0.1,
    dataset='nyu',
    depth_anything=True,
    distributed=True,
    do_resize=False,
    force_keep_ar=True,
    freeze_midas_bn=True,
    gpu='NULL',
    img_size=[
        392,
        518,
    ],
    inverse_midas=False,
    log_images_every=0.1,
    max_depth=80,
    max_temp=50.0,
    max_translation=100,
    memory_efficient=True,
    midas_model_type='vitb',
    min_depth=0.001,
    min_temp=0.0212,
    model='zoedepth',
    n_attractors=[
        16,
        8,
        4,
        1,
    ],
    n_bins=64,
    name='ZoeDepth',
    notes='',
    output_distribution='logbinomial',
    prefetch=False,
    pretrained_resource='local::./work_dir/DepthAnything_vitb.pt',
    print_losses=False,
    project='ZoeDepth',
    random_crop=False,
    random_translate=False,
    root='.',
    save_dir='',
    shared_dict='NULL',
    tags='',
    train_midas=True,
    translate_prob=0.2,
    type='DA-ZoeDepth',
    uid='NULL',
    use_amp=False,
    use_pretrained_midas=True,
    use_shared_dict=False,
    validate_every=0.25,
    version_name='v1',
    workers=16)

2024/03/15 19:30:43 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vitb.pt
2024/03/15 19:30:43 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'>
2024/03/15 19:30:43 - patchstitcher - INFO - Loading coarse_branch from ./work_dir/depthanything_vitb_u4k/coarse_pretrain/checkpoint_24.pth
2024/03/15 19:30:43 - patchstitcher - INFO - <All keys matched successfully>
2024/03/15 19:30:44 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vitb.pt
2024/03/15 19:30:44 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'>
2024/03/15 19:30:44 - patchstitcher - INFO - Loading fine_branch from ./work_dir/depthanything_vitb_u4k/fine_pretrain/checkpoint_24.pth
2024/03/15 19:30:44 - patchstitcher - INFO - <All keys matched successfully>
2024/03/15 19:30:45 - patchstitcher - INFO - DistributedDataParallel(
  (module): PatchFusion(
    (coarse_branch): ZoeDepth(
      (core): DepthAnythingCore(
        (core): DPT_DINOv2(
          (pretrained): DinoVisionTransformer(
            (patch_embed): PatchEmbed(
              (proj): Conv2d(3, 768, kernel_size=(14, 14), stride=(14, 14))
              (norm): Identity()
            )
            (blocks): ModuleList(
              (0-11): 12 x NestedTensorBlock(
                (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
                (attn): MemEffAttention(
                  (qkv): Linear(in_features=768, out_features=2304, bias=True)
                  (attn_drop): Dropout(p=0.0, inplace=False)
                  (proj): Linear(in_features=768, out_features=768, bias=True)
                  (proj_drop): Dropout(p=0.0, inplace=False)
                )
                (ls1): LayerScale()
                (drop_path1): Identity()
                (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
                (mlp): Mlp(
                  (fc1): Linear(in_features=768, out_features=3072, bias=True)
                  (act): GELU(approximate='none')
                  (fc2): Linear(in_features=3072, out_features=768, bias=True)
                  (drop): Dropout(p=0.0, inplace=False)
                )
                (ls2): LayerScale()
                (drop_path2): Identity()
              )
            )
            (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
            (head): Identity()
          )
          (depth_head): DPTHead(
            (projects): ModuleList(
              (0): Conv2d(768, 96, kernel_size=(1, 1), stride=(1, 1))
              (1): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1))
              (2): Conv2d(768, 384, kernel_size=(1, 1), stride=(1, 1))
              (3): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1))
            )
            (resize_layers): ModuleList(
              (0): ConvTranspose2d(96, 96, kernel_size=(4, 4), stride=(4, 4))
              (1): ConvTranspose2d(192, 192, kernel_size=(2, 2), stride=(2, 2))
              (2): Identity()
              (3): Conv2d(768, 768, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
            )
            (scratch): Module(
              (layer1_rn): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer2_rn): Conv2d(192, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer3_rn): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer4_rn): Conv2d(768, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (refinenet1): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (refinenet2): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (refinenet3): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (refinenet4): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (output_conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (output_conv2): Sequential(
                (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                (1): ReLU(inplace=True)
                (2): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))
                (3): ReLU(inplace=True)
                (4): Identity()
              )
            )
          )
        )
      )
      (conv2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (seed_bin_regressor): SeedBinRegressorUnnormed(
        (_net): Sequential(
          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
      (seed_projector): Projector(
        (_net): Sequential(
          (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (projectors): ModuleList(
        (0-3): 4 x Projector(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          )
        )
      )
      (attractors): ModuleList(
        (0): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
        (1): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
        (2): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
        (3): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
      )
      (conditional_log_binomial): ConditionalLogBinomial(
        (log_binomial_transform): LogBinomial()
        (mlp): Sequential(
          (0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1))
          (1): GELU(approximate='none')
          (2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
    )
    (fine_branch): ZoeDepth(
      (core): DepthAnythingCore(
        (core): DPT_DINOv2(
          (pretrained): DinoVisionTransformer(
            (patch_embed): PatchEmbed(
              (proj): Conv2d(3, 768, kernel_size=(14, 14), stride=(14, 14))
              (norm): Identity()
            )
            (blocks): ModuleList(
              (0-11): 12 x NestedTensorBlock(
                (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
                (attn): MemEffAttention(
                  (qkv): Linear(in_features=768, out_features=2304, bias=True)
                  (attn_drop): Dropout(p=0.0, inplace=False)
                  (proj): Linear(in_features=768, out_features=768, bias=True)
                  (proj_drop): Dropout(p=0.0, inplace=False)
                )
                (ls1): LayerScale()
                (drop_path1): Identity()
                (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
                (mlp): Mlp(
                  (fc1): Linear(in_features=768, out_features=3072, bias=True)
                  (act): GELU(approximate='none')
                  (fc2): Linear(in_features=3072, out_features=768, bias=True)
                  (drop): Dropout(p=0.0, inplace=False)
                )
                (ls2): LayerScale()
                (drop_path2): Identity()
              )
            )
            (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
            (head): Identity()
          )
          (depth_head): DPTHead(
            (projects): ModuleList(
              (0): Conv2d(768, 96, kernel_size=(1, 1), stride=(1, 1))
              (1): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1))
              (2): Conv2d(768, 384, kernel_size=(1, 1), stride=(1, 1))
              (3): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1))
            )
            (resize_layers): ModuleList(
              (0): ConvTranspose2d(96, 96, kernel_size=(4, 4), stride=(4, 4))
              (1): ConvTranspose2d(192, 192, kernel_size=(2, 2), stride=(2, 2))
              (2): Identity()
              (3): Conv2d(768, 768, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
            )
            (scratch): Module(
              (layer1_rn): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer2_rn): Conv2d(192, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer3_rn): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer4_rn): Conv2d(768, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (refinenet1): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (refinenet2): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (refinenet3): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (refinenet4): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (output_conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (output_conv2): Sequential(
                (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                (1): ReLU(inplace=True)
                (2): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))
                (3): ReLU(inplace=True)
                (4): Identity()
              )
            )
          )
        )
      )
      (conv2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (seed_bin_regressor): SeedBinRegressorUnnormed(
        (_net): Sequential(
          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
      (seed_projector): Projector(
        (_net): Sequential(
          (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (projectors): ModuleList(
        (0-3): 4 x Projector(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          )
        )
      )
      (attractors): ModuleList(
        (0): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
        (1): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
        (2): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
        (3): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
      )
      (conditional_log_binomial): ConditionalLogBinomial(
        (log_binomial_transform): LogBinomial()
        (mlp): Sequential(
          (0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1))
          (1): GELU(approximate='none')
          (2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
    )
    (sigloss): SILogLoss()
    (fusion_conv_list): ModuleList(
      (0-4): 5 x Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (5): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (guided_fusion): GuidedFusionPatchFusion(
      (inc): DoubleConv(
        (double_conv): Sequential(
          (0): Conv2d(5, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU(inplace=True)
        )
      )
      (down_conv_list): ModuleList(
        (0): Down(
          (maxpool_conv): Sequential(
            (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
            (1): DoubleConv(
              (double_conv): Sequential(
                (0): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (2): ReLU(inplace=True)
                (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (4): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (5): ReLU(inplace=True)
              )
            )
          )
        )
        (1-4): 4 x Down(
          (maxpool_conv): Sequential(
            (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
            (1): DoubleConv(
              (double_conv): Sequential(
                (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (2): ReLU(inplace=True)
                (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                (4): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (5): ReLU(inplace=True)
              )
            )
          )
        )
      )
      (up_conv_list): ModuleList(
        (0-3): 4 x Upv1(
          (conv): DoubleConvWOBN(
            (double_conv): Sequential(
              (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (1): ReLU(inplace=True)
              (2): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (3): ReLU(inplace=True)
            )
          )
        )
        (4): Upv1(
          (conv): DoubleConvWOBN(
            (double_conv): Sequential(
              (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (1): ReLU(inplace=True)
              (2): Conv2d(288, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (3): ReLU(inplace=True)
            )
          )
        )
      )
      (g2l_att): ModuleList()
      (g2l_list): ModuleList(
        (0-1): 2 x G2LFusion(
          (g2l_layer): G2LBasicLayer(
            (blocks): ModuleList(
              (0-3): 4 x SwinTransformerBlock(
                (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
                (attn): WindowAttention(
                  dim=128, window_size=(12, 12), num_heads=32
                  (qkv): Linear(in_features=128, out_features=384, bias=True)
                  (attn_drop): Dropout(p=0.0, inplace=False)
                  (proj): Linear(in_features=128, out_features=128, bias=True)
                  (proj_drop): Dropout(p=0.0, inplace=False)
                  (softmax): Softmax(dim=-1)
                )
                (drop_path): Identity()
                (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
                (mlp): Mlp(
                  (fc1): Linear(in_features=128, out_features=512, bias=True)
                  (act): GELU(approximate='none')
                  (fc2): Linear(in_features=512, out_features=128, bias=True)
                  (drop): Dropout(p=0.0, inplace=False)
                )
              )
            )
          )
          (g2l_layer_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
          (embed_proj): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1))
        )
        (2-3): 2 x G2LFusion(
          (g2l_layer): G2LBasicLayer(
            (blocks): ModuleList(
              (0-2): 3 x SwinTransformerBlock(
                (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
                (attn): WindowAttention(
                  dim=128, window_size=(12, 12), num_heads=16
                  (qkv): Linear(in_features=128, out_features=384, bias=True)
                  (attn_drop): Dropout(p=0.0, inplace=False)
                  (proj): Linear(in_features=128, out_features=128, bias=True)
                  (proj_drop): Dropout(p=0.0, inplace=False)
                  (softmax): Softmax(dim=-1)
                )
                (drop_path): Identity()
                (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
                (mlp): Mlp(
                  (fc1): Linear(in_features=128, out_features=512, bias=True)
                  (act): GELU(approximate='none')
                  (fc2): Linear(in_features=512, out_features=128, bias=True)
                  (drop): Dropout(p=0.0, inplace=False)
                )
              )
            )
          )
          (g2l_layer_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
          (embed_proj): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1))
        )
        (4): G2LFusion(
          (g2l_layer): G2LBasicLayer(
            (blocks): ModuleList(
              (0-1): 2 x SwinTransformerBlock(
                (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
                (attn): WindowAttention(
                  dim=128, window_size=(12, 12), num_heads=8
                  (qkv): Linear(in_features=128, out_features=384, bias=True)
                  (attn_drop): Dropout(p=0.0, inplace=False)
                  (proj): Linear(in_features=128, out_features=128, bias=True)
                  (proj_drop): Dropout(p=0.0, inplace=False)
                  (softmax): Softmax(dim=-1)
                )
                (drop_path): Identity()
                (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
                (mlp): Mlp(
                  (fc1): Linear(in_features=128, out_features=512, bias=True)
                  (act): GELU(approximate='none')
                  (fc2): Linear(in_features=512, out_features=128, bias=True)
                  (drop): Dropout(p=0.0, inplace=False)
                )
              )
            )
          )
          (g2l_layer_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
          (embed_proj): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1))
        )
        (5): G2LFusion(
          (g2l_layer): G2LBasicLayer(
            (blocks): ModuleList(
              (0-1): 2 x SwinTransformerBlock(
                (norm1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
                (attn): WindowAttention(
                  dim=32, window_size=(12, 12), num_heads=8
                  (qkv): Linear(in_features=32, out_features=96, bias=True)
                  (attn_drop): Dropout(p=0.0, inplace=False)
                  (proj): Linear(in_features=32, out_features=32, bias=True)
                  (proj_drop): Dropout(p=0.0, inplace=False)
                  (softmax): Softmax(dim=-1)
                )
                (drop_path): Identity()
                (norm2): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
                (mlp): Mlp(
                  (fc1): Linear(in_features=32, out_features=128, bias=True)
                  (act): GELU(approximate='none')
                  (fc2): Linear(in_features=128, out_features=32, bias=True)
                  (drop): Dropout(p=0.0, inplace=False)
                )
              )
            )
          )
          (g2l_layer_norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
          (embed_proj): Conv2d(1, 32, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (convs): ModuleList(
        (0-4): 5 x DoubleConvWOBN(
          (double_conv): Sequential(
            (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (3): ReLU(inplace=True)
          )
        )
        (5): DoubleConvWOBN(
          (double_conv): Sequential(
            (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (3): ReLU(inplace=True)
          )
        )
      )
    )
    (seed_bin_regressor): SeedBinRegressorUnnormed(
      (_net): Sequential(
        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
        (1): ReLU(inplace=True)
        (2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
        (3): Softplus(beta=1, threshold=20)
      )
    )
    (seed_projector): Projector(
      (_net): Sequential(
        (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
        (1): ReLU(inplace=True)
        (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      )
    )
    (projectors): ModuleList(
      (0-3): 4 x Projector(
        (_net): Sequential(
          (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
        )
      )
    )
    (attractors): ModuleList(
      (0): AttractorLayerUnnormed(
        (_net): Sequential(
          (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
      (1): AttractorLayerUnnormed(
        (_net): Sequential(
          (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
      (2): AttractorLayerUnnormed(
        (_net): Sequential(
          (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
      (3): AttractorLayerUnnormed(
        (_net): Sequential(
          (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
    )
    (conditional_log_binomial): ConditionalLogBinomial(
      (log_binomial_transform): LogBinomial()
      (mlp): Sequential(
        (0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1))
        (1): GELU(approximate='none')
        (2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1))
        (3): Softplus(beta=1, threshold=20)
      )
    )
  )
)
2024/03/15 19:30:51 - patchstitcher - INFO - successfully init trainer
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.1.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.1.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.3.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.3.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.4.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.4.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.5.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.fusion_conv_list.5.bias
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2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.5.g2l_layer.blocks.1.mlp.fc1.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.5.g2l_layer.blocks.1.mlp.fc1.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.5.g2l_layer.blocks.1.mlp.fc2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.5.g2l_layer.blocks.1.mlp.fc2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.5.g2l_layer_norm.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.5.g2l_layer_norm.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.5.embed_proj.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.5.embed_proj.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.0.double_conv.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.0.double_conv.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.0.double_conv.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.0.double_conv.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.1.double_conv.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.1.double_conv.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.1.double_conv.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.1.double_conv.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.2.double_conv.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.2.double_conv.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.2.double_conv.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.2.double_conv.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.3.double_conv.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.3.double_conv.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.3.double_conv.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.3.double_conv.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.4.double_conv.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.4.double_conv.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.4.double_conv.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.4.double_conv.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.5.double_conv.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.5.double_conv.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.5.double_conv.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.guided_fusion.convs.5.double_conv.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.seed_bin_regressor._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.seed_bin_regressor._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.seed_bin_regressor._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.seed_bin_regressor._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.seed_projector._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.seed_projector._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.seed_projector._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.seed_projector._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.0._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.0._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.0._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.0._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.1._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.1._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.1._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.1._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.2._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.2._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.2._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.2._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.3._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.3._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.3._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.projectors.3._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.0._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.0._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.0._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.0._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.1._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.1._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.1._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.1._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.2._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.2._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.2._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.2._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.3._net.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.3._net.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.3._net.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.attractors.3._net.2.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.conditional_log_binomial.mlp.0.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.conditional_log_binomial.mlp.0.bias
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.conditional_log_binomial.mlp.2.weight
2024/03/15 19:30:51 - patchstitcher - INFO - training param: module.conditional_log_binomial.mlp.2.bias
2024/03/15 19:33:45 - patchstitcher - INFO - Epoch: [01/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 8.196772575378418 - sig_loss: 8.196772575378418
2024/03/15 19:35:51 - patchstitcher - INFO - Epoch: [01/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 4.177845478057861 - sig_loss: 4.177845478057861
2024/03/15 19:38:00 - patchstitcher - INFO - Epoch: [01/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 4.080796241760254 - sig_loss: 4.080796241760254
2024/03/15 19:40:09 - patchstitcher - INFO - Epoch: [01/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0100841522216797 - sig_loss: 1.0100841522216797
2024/03/15 19:44:11 - patchstitcher - INFO - Epoch: [02/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1344835758209229 - sig_loss: 1.1344835758209229
2024/03/15 19:46:18 - patchstitcher - INFO - Epoch: [02/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.766528308391571 - sig_loss: 0.766528308391571
2024/03/15 19:48:26 - patchstitcher - INFO - Epoch: [02/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.742828369140625 - sig_loss: 0.742828369140625
2024/03/15 19:50:31 - patchstitcher - INFO - Epoch: [02/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0008141994476318 - sig_loss: 1.0008141994476318
2024/03/15 19:52:41 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |    a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9690564 | 0.9931927 | 0.997483 | 0.0683934 | 1.4234179 | 0.0297297 | 0.0982972 | 8.7126409 | 0.1563224 | 1.2264686 |
+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 19:54:56 - patchstitcher - INFO - Epoch: [03/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.9230748414993286 - sig_loss: 1.9230748414993286
2024/03/15 19:57:02 - patchstitcher - INFO - Epoch: [03/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6135638952255249 - sig_loss: 0.6135638952255249
2024/03/15 19:59:09 - patchstitcher - INFO - Epoch: [03/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.49874502420425415 - sig_loss: 0.49874502420425415
2024/03/15 20:01:17 - patchstitcher - INFO - Epoch: [03/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9610685110092163 - sig_loss: 0.9610685110092163
2024/03/15 20:05:08 - patchstitcher - INFO - Epoch: [04/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9942363500595093 - sig_loss: 0.9942363500595093
2024/03/15 20:07:14 - patchstitcher - INFO - Epoch: [04/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8103439807891846 - sig_loss: 0.8103439807891846
2024/03/15 20:09:22 - patchstitcher - INFO - Epoch: [04/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4848376214504242 - sig_loss: 0.4848376214504242
2024/03/15 20:11:31 - patchstitcher - INFO - Epoch: [04/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.39297956228256226 - sig_loss: 0.39297956228256226
2024/03/15 20:13:33 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+----------+-----------+----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |   rmse   |   log_10  | rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+----------+-----------+----------+-----------+-----------+-----------+
| 0.9792909 | 0.9939313 | 0.9976017 | 0.0472044 | 1.221259 | 0.0211389 | 0.079348 | 7.1940729 | 0.1013146 | 0.9066877 |
+-----------+-----------+-----------+-----------+----------+-----------+----------+-----------+-----------+-----------+
2024/03/15 20:15:49 - patchstitcher - INFO - Epoch: [05/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.346112996339798 - sig_loss: 0.346112996339798
2024/03/15 20:17:56 - patchstitcher - INFO - Epoch: [05/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.37177300453186035 - sig_loss: 0.37177300453186035
2024/03/15 20:20:03 - patchstitcher - INFO - Epoch: [05/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.19638416171073914 - sig_loss: 0.19638416171073914
2024/03/15 20:22:15 - patchstitcher - INFO - Epoch: [05/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.31483086943626404 - sig_loss: 0.31483086943626404
2024/03/15 20:26:06 - patchstitcher - INFO - Epoch: [06/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4689038395881653 - sig_loss: 0.4689038395881653
2024/03/15 20:28:15 - patchstitcher - INFO - Epoch: [06/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7205864191055298 - sig_loss: 0.7205864191055298
2024/03/15 20:30:20 - patchstitcher - INFO - Epoch: [06/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.3387361168861389 - sig_loss: 0.3387361168861389
2024/03/15 20:32:31 - patchstitcher - INFO - Epoch: [06/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.17904606461524963 - sig_loss: 0.17904606461524963
2024/03/15 20:34:33 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9811088 | 0.9944261 | 0.9976145 | 0.0587846 | 1.1488307 | 0.0250373 | 0.0835342 | 6.7835254 | 0.0985873 | 0.9090285 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 20:36:45 - patchstitcher - INFO - Epoch: [07/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.598641037940979 - sig_loss: 0.598641037940979
2024/03/15 20:38:52 - patchstitcher - INFO - Epoch: [07/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6939734220504761 - sig_loss: 0.6939734220504761
2024/03/15 20:40:58 - patchstitcher - INFO - Epoch: [07/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2778879404067993 - sig_loss: 1.2778879404067993
2024/03/15 20:43:04 - patchstitcher - INFO - Epoch: [07/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.25490495562553406 - sig_loss: 0.25490495562553406
2024/03/15 20:46:52 - patchstitcher - INFO - Epoch: [08/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6926709413528442 - sig_loss: 0.6926709413528442
2024/03/15 20:49:01 - patchstitcher - INFO - Epoch: [08/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.4315345287322998 - sig_loss: 0.4315345287322998
2024/03/15 20:51:10 - patchstitcher - INFO - Epoch: [08/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.5032333135604858 - sig_loss: 1.5032333135604858
2024/03/15 20:53:16 - patchstitcher - INFO - Epoch: [08/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5602918863296509 - sig_loss: 0.5602918863296509
2024/03/15 20:55:19 - patchstitcher - INFO - Evaluation Summary: 
+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|    a1    |     a2    |    a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.981955 | 0.9944548 | 0.997687 | 0.0567417 | 1.1642307 | 0.0240603 | 0.0811943 | 6.7784523 | 0.1035022 | 0.9762238 |
+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 20:57:34 - patchstitcher - INFO - Epoch: [09/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4373475909233093 - sig_loss: 0.4373475909233093
2024/03/15 20:59:40 - patchstitcher - INFO - Epoch: [09/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.5699481964111328 - sig_loss: 1.5699481964111328
2024/03/15 21:01:47 - patchstitcher - INFO - Epoch: [09/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.3727685213088989 - sig_loss: 0.3727685213088989
2024/03/15 21:03:55 - patchstitcher - INFO - Epoch: [09/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8527534008026123 - sig_loss: 0.8527534008026123
2024/03/15 21:07:41 - patchstitcher - INFO - Epoch: [10/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.3214010000228882 - sig_loss: 0.3214010000228882
2024/03/15 21:09:47 - patchstitcher - INFO - Epoch: [10/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.24748793244361877 - sig_loss: 0.24748793244361877
2024/03/15 21:11:53 - patchstitcher - INFO - Epoch: [10/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9347164630889893 - sig_loss: 0.9347164630889893
2024/03/15 21:14:01 - patchstitcher - INFO - Epoch: [10/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5690074563026428 - sig_loss: 0.5690074563026428
2024/03/15 21:16:03 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |   rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
| 0.9828057 | 0.9944213 | 0.9976482 | 0.0431387 | 1.153271 | 0.0186824 | 0.0717582 | 6.5566115 | 0.0970193 | 0.9094797 |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 21:18:19 - patchstitcher - INFO - Epoch: [11/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4396677017211914 - sig_loss: 0.4396677017211914
2024/03/15 21:20:26 - patchstitcher - INFO - Epoch: [11/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0656664371490479 - sig_loss: 1.0656664371490479
2024/03/15 21:22:32 - patchstitcher - INFO - Epoch: [11/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9697193503379822 - sig_loss: 0.9697193503379822
2024/03/15 21:24:40 - patchstitcher - INFO - Epoch: [11/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.3520055115222931 - sig_loss: 0.3520055115222931
2024/03/15 21:28:27 - patchstitcher - INFO - Epoch: [12/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5407581329345703 - sig_loss: 0.5407581329345703
2024/03/15 21:30:35 - patchstitcher - INFO - Epoch: [12/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.402178019285202 - sig_loss: 0.402178019285202
2024/03/15 21:32:44 - patchstitcher - INFO - Epoch: [12/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.3128345310688019 - sig_loss: 0.3128345310688019
2024/03/15 21:34:48 - patchstitcher - INFO - Epoch: [12/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.2746131122112274 - sig_loss: 0.2746131122112274
2024/03/15 21:36:50 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9829779 | 0.9943216 | 0.9976086 | 0.0449465 | 1.1585257 | 0.0199354 | 0.0738602 | 6.4633956 | 0.0979459 | 0.8825008 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 21:39:05 - patchstitcher - INFO - Epoch: [13/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9073178768157959 - sig_loss: 0.9073178768157959
2024/03/15 21:41:14 - patchstitcher - INFO - Epoch: [13/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.522519052028656 - sig_loss: 0.522519052028656
2024/03/15 21:43:21 - patchstitcher - INFO - Epoch: [13/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.48742449283599854 - sig_loss: 0.48742449283599854
2024/03/15 21:45:26 - patchstitcher - INFO - Epoch: [13/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6288169622421265 - sig_loss: 0.6288169622421265
2024/03/15 21:49:14 - patchstitcher - INFO - Epoch: [14/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.3309251368045807 - sig_loss: 0.3309251368045807
2024/03/15 21:51:19 - patchstitcher - INFO - Epoch: [14/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.2369067668914795 - sig_loss: 0.2369067668914795
2024/03/15 21:53:27 - patchstitcher - INFO - Epoch: [14/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.31572964787483215 - sig_loss: 0.31572964787483215
2024/03/15 21:55:33 - patchstitcher - INFO - Epoch: [14/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4297329783439636 - sig_loss: 0.4297329783439636
2024/03/15 21:57:35 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    | abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9839865 | 0.9945975 | 0.9976648 | 0.040681 | 1.0868772 | 0.0176451 | 0.0695904 | 6.3975404 | 0.0912883 | 0.8778101 |
+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 21:59:49 - patchstitcher - INFO - Epoch: [15/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.27064019441604614 - sig_loss: 0.27064019441604614
2024/03/15 22:01:56 - patchstitcher - INFO - Epoch: [15/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.2585503160953522 - sig_loss: 0.2585503160953522
2024/03/15 22:04:00 - patchstitcher - INFO - Epoch: [15/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.3952506482601166 - sig_loss: 0.3952506482601166
2024/03/15 22:06:07 - patchstitcher - INFO - Epoch: [15/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.49016281962394714 - sig_loss: 0.49016281962394714
2024/03/15 22:09:58 - patchstitcher - INFO - Epoch: [16/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1630560159683228 - sig_loss: 1.1630560159683228
2024/03/15 22:12:03 - patchstitcher - INFO - Epoch: [16/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7704189419746399 - sig_loss: 0.7704189419746399
2024/03/15 22:14:10 - patchstitcher - INFO - Epoch: [16/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4150314927101135 - sig_loss: 0.4150314927101135
2024/03/15 22:16:14 - patchstitcher - INFO - Epoch: [16/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.7451047897338867 - sig_loss: 1.7451047897338867
2024/03/15 22:18:19 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |  silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
| 0.9840401 | 0.9945714 | 0.9976669 | 0.0400293 | 1.0903379 | 0.0174627 | 0.0692402 | 6.368767 | 0.0898903 | 0.8650321 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
2024/03/15 22:18:19 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict
2024/03/15 22:18:19 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :>
2024/03/15 22:18:20 - patchstitcher - INFO - save checkpoint_16.pth at ./work_dir/depthanything_vitb_u4k/patchfusion