depthanything_vits_u4k/fine_pretrain/20240315_035516.log

2024/03/15 03:55:26 - 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 03:55:26 - 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 = 'fine_pretrain'
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='vits',
        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_vits.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='vits',
        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_vits.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),
    max_depth=80,
    min_depth=0.001,
    patch_process_shape=(
        392,
        518,
    ),
    sigloss=dict(type='SILogLoss'),
    target='fine',
    type='BaselinePretrain')
optim_wrapper = dict(
    clip_grad=dict(max_norm=0.1, norm_type=2, type='norm'),
    optimizer=dict(lr=4e-06, type='AdamW', weight_decay=0.01),
    paramwise_cfg=dict(bypass_duplicate=True, custom_keys=dict()))
param_scheduler = dict(
    base_momentum=0.85,
    cycle_momentum=True,
    div_factor=1,
    final_div_factor=10000,
    max_momentum=0.95,
    pct_start=0.5,
    three_phase=False)
project = 'patchfusion'
tags = [
    'fine',
    'da',
    'vits',
]
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=24,
    save_checkpoint_interval=24,
    train_log_img_interval=100,
    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_vits_u4k/fine_pretrain'
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='vits',
    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_vits.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 03:55:27 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vits.pt
2024/03/15 03:55:27 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'>
2024/03/15 03:55:27 - patchstitcher - INFO - DistributedDataParallel(
  (module): BaselinePretrain(
    (fine_branch): ZoeDepth(
      (core): DepthAnythingCore(
        (core): DPT_DINOv2(
          (pretrained): DinoVisionTransformer(
            (patch_embed): PatchEmbed(
              (proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14))
              (norm): Identity()
            )
            (blocks): ModuleList(
              (0-11): 12 x NestedTensorBlock(
                (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
                (attn): MemEffAttention(
                  (qkv): Linear(in_features=384, out_features=1152, bias=True)
                  (attn_drop): Dropout(p=0.0, inplace=False)
                  (proj): Linear(in_features=384, out_features=384, bias=True)
                  (proj_drop): Dropout(p=0.0, inplace=False)
                )
                (ls1): LayerScale()
                (drop_path1): Identity()
                (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
                (mlp): Mlp(
                  (fc1): Linear(in_features=384, out_features=1536, bias=True)
                  (act): GELU(approximate='none')
                  (fc2): Linear(in_features=1536, out_features=384, bias=True)
                  (drop): Dropout(p=0.0, inplace=False)
                )
                (ls2): LayerScale()
                (drop_path2): Identity()
              )
            )
            (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
            (head): Identity()
          )
          (depth_head): DPTHead(
            (projects): ModuleList(
              (0): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))
              (1): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1))
              (2): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1))
              (3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1))
            )
            (resize_layers): ModuleList(
              (0): ConvTranspose2d(48, 48, kernel_size=(4, 4), stride=(4, 4))
              (1): ConvTranspose2d(96, 96, kernel_size=(2, 2), stride=(2, 2))
              (2): Identity()
              (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
            )
            (scratch): Module(
              (layer1_rn): Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer2_rn): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer3_rn): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer4_rn): Conv2d(384, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (refinenet1): FeatureFusionBlock(
                (out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(64, 64, 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(64, 64, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(64, 64, 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(64, 64, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(64, 64, 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(64, 64, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(64, 64, 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(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (output_conv2): Sequential(
                (0): Conv2d(32, 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(64, 64, kernel_size=(1, 1), stride=(1, 1))
      (seed_bin_regressor): SeedBinRegressorUnnormed(
        (_net): Sequential(
          (0): Conv2d(64, 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(64, 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(64, 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()
  )
)
2024/03/15 03:55:33 - patchstitcher - INFO - successfully init trainer
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.cls_token
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.pos_embed
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.mask_token
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.weight
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2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.resize_layers.1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.resize_layers.3.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.resize_layers.3.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.layer1_rn.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.layer2_rn.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.layer3_rn.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.layer4_rn.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.out_conv.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.out_conv.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.out_conv.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.out_conv.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conv2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conv2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.bias
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.weight
2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.bias
2024/03/15 03:57:49 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.039879322052002 - fine_loss: 2.039879322052002
2024/03/15 03:59:40 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 3.776620626449585 - fine_loss: 3.776620626449585
2024/03/15 04:01:30 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.1612205505371094 - fine_loss: 2.1612205505371094
2024/03/15 04:03:20 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3563077449798584 - fine_loss: 1.3563077449798584
2024/03/15 04:06:31 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.1678900718688965 - fine_loss: 2.1678900718688965
2024/03/15 04:08:25 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.8825774192810059 - fine_loss: 1.8825774192810059
2024/03/15 04:10:14 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.350590467453003 - fine_loss: 2.350590467453003
2024/03/15 04:12:06 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 2.691840648651123 - fine_loss: 2.691840648651123
2024/03/15 04:13:51 - patchstitcher - INFO - Evaluation Summary: 
+----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|    a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.707044 | 0.9293698 | 0.9801447 | 0.1927294 | 2.3443637 | 0.0782506 | 0.2331481 | 20.0879481 | 0.4492522 | 1.7012854 |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 04:15:48 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2447803020477295 - fine_loss: 1.2447803020477295
2024/03/15 04:17:37 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.6822900772094727 - fine_loss: 1.6822900772094727
2024/03/15 04:19:22 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.7436625957489014 - fine_loss: 2.7436625957489014
2024/03/15 04:21:15 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.9489283561706543 - fine_loss: 1.9489283561706543
2024/03/15 04:24:21 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5366265773773193 - fine_loss: 1.5366265773773193
2024/03/15 04:26:10 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.0812580585479736 - fine_loss: 2.0812580585479736
2024/03/15 04:28:00 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.318430185317993 - fine_loss: 2.318430185317993
2024/03/15 04:29:48 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.638041615486145 - fine_loss: 1.638041615486145
2024/03/15 04:31:27 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+----------+
|     a1    |     a2    |     a3    |  abs_rel  |   rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |   see    |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+----------+
| 0.7926732 | 0.9574633 | 0.9874803 | 0.1658911 | 2.033809 | 0.0653377 | 0.1971613 | 17.6386279 | 0.3764188 | 1.566062 |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+----------+
2024/03/15 04:33:22 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.071550726890564 - fine_loss: 1.071550726890564
2024/03/15 04:35:11 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.159848928451538 - fine_loss: 1.159848928451538
2024/03/15 04:36:58 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2986273765563965 - fine_loss: 1.2986273765563965
2024/03/15 04:38:48 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5721113681793213 - fine_loss: 1.5721113681793213
2024/03/15 04:42:00 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.7645320892333984 - fine_loss: 1.7645320892333984
2024/03/15 04:43:48 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.2818663120269775 - fine_loss: 1.2818663120269775
2024/03/15 04:45:40 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2445242404937744 - fine_loss: 1.2445242404937744
2024/03/15 04:47:30 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5368983745574951 - fine_loss: 1.5368983745574951
2024/03/15 04:49:02 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.8194143 | 0.9697637 | 0.9905658 | 0.1490125 | 1.8480574 | 0.0592408 | 0.1810736 | 15.8342003 | 0.3005681 | 1.3977808 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 04:50:58 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4791369438171387 - fine_loss: 1.4791369438171387
2024/03/15 04:52:44 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.1252331733703613 - fine_loss: 2.1252331733703613
2024/03/15 04:54:32 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.84209406375885 - fine_loss: 1.84209406375885
2024/03/15 04:56:25 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1359673738479614 - fine_loss: 1.1359673738479614
2024/03/15 04:59:38 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5866280794143677 - fine_loss: 1.5866280794143677
2024/03/15 05:01:29 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.3199617862701416 - fine_loss: 1.3199617862701416
2024/03/15 05:03:15 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6660882234573364 - fine_loss: 1.6660882234573364
2024/03/15 05:05:05 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0399880409240723 - fine_loss: 1.0399880409240723
2024/03/15 05:06:40 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.8804187 | 0.9831836 | 0.9948749 | 0.1118127 | 1.7537212 | 0.0498078 | 0.1550232 | 14.4210851 | 0.2352216 | 1.2980962 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 05:08:36 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.7554281949996948 - fine_loss: 1.7554281949996948
2024/03/15 05:10:27 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.8572347164154053 - fine_loss: 2.8572347164154053
2024/03/15 05:12:16 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3657317161560059 - fine_loss: 1.3657317161560059
2024/03/15 05:14:08 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3460898399353027 - fine_loss: 1.3460898399353027
2024/03/15 05:17:20 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0736647844314575 - fine_loss: 1.0736647844314575
2024/03/15 05:19:11 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.179059624671936 - fine_loss: 1.179059624671936
2024/03/15 05:21:00 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0112545490264893 - fine_loss: 1.0112545490264893
2024/03/15 05:22:47 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2453086376190186 - fine_loss: 1.2453086376190186
2024/03/15 05:24:25 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.8772274 | 0.9823961 | 0.9948375 | 0.1173125 | 1.7241426 | 0.0501591 | 0.1553792 | 14.1530364 | 0.2422748 | 1.3415729 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 05:26:18 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.306344747543335 - fine_loss: 1.306344747543335
2024/03/15 05:28:16 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.348771572113037 - fine_loss: 1.348771572113037
2024/03/15 05:30:06 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.549656629562378 - fine_loss: 1.549656629562378
2024/03/15 05:31:57 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4452790021896362 - fine_loss: 1.4452790021896362
2024/03/15 05:35:10 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1077752113342285 - fine_loss: 1.1077752113342285
2024/03/15 05:37:01 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8956596255302429 - fine_loss: 0.8956596255302429
2024/03/15 05:38:52 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9720367789268494 - fine_loss: 0.9720367789268494
2024/03/15 05:40:41 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4826208353042603 - fine_loss: 1.4826208353042603
2024/03/15 05:42:16 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |   rmse   |   log_10  |  rmse_log |   silog    |  sq_rel  |    see    |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+----------+-----------+
| 0.8740682 | 0.9844725 | 0.9957269 | 0.1142447 | 1.696142 | 0.0509766 | 0.1547242 | 13.9800131 | 0.237403 | 1.2716073 |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+----------+-----------+
2024/03/15 05:44:13 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.7906665802001953 - fine_loss: 1.7906665802001953
2024/03/15 05:46:06 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.7277212142944336 - fine_loss: 1.7277212142944336
2024/03/15 05:48:00 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1345900297164917 - fine_loss: 1.1345900297164917
2024/03/15 05:49:53 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.680286169052124 - fine_loss: 0.680286169052124
2024/03/15 05:53:05 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0135771036148071 - fine_loss: 1.0135771036148071
2024/03/15 05:54:56 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1816802024841309 - fine_loss: 1.1816802024841309
2024/03/15 05:56:44 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3476241827011108 - fine_loss: 1.3476241827011108
2024/03/15 05:58:33 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6280028223991394 - fine_loss: 0.6280028223991394
2024/03/15 06:00:11 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  | rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
| 0.9147314 | 0.9859354 | 0.9949076 | 0.1007045 | 1.6106567 | 0.0434999 | 0.138901 | 13.0318626 | 0.2056279 | 1.2140529 |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
2024/03/15 06:02:04 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8376606702804565 - fine_loss: 0.8376606702804565
2024/03/15 06:03:57 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.03225576877594 - fine_loss: 1.03225576877594
2024/03/15 06:05:44 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9883253574371338 - fine_loss: 0.9883253574371338
2024/03/15 06:07:36 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.262385368347168 - fine_loss: 1.262385368347168
2024/03/15 06:10:46 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1695902347564697 - fine_loss: 1.1695902347564697
2024/03/15 06:12:36 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.1688151359558105 - fine_loss: 2.1688151359558105
2024/03/15 06:14:24 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3791565895080566 - fine_loss: 1.3791565895080566
2024/03/15 06:16:12 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2718651294708252 - fine_loss: 1.2718651294708252
2024/03/15 06:17:50 - patchstitcher - INFO - Evaluation Summary: 
+----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|    a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.917846 | 0.9849823 | 0.9948954 | 0.0979613 | 1.5791011 | 0.0433261 | 0.1380226 | 12.8257169 | 0.1883265 | 1.1684257 |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 06:19:42 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0557522773742676 - fine_loss: 1.0557522773742676
2024/03/15 06:21:33 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6954542398452759 - fine_loss: 0.6954542398452759
2024/03/15 06:23:20 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.203284740447998 - fine_loss: 1.203284740447998
2024/03/15 06:25:09 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2890739440917969 - fine_loss: 1.2890739440917969
2024/03/15 06:28:25 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8028295040130615 - fine_loss: 0.8028295040130615
2024/03/15 06:30:13 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.499220609664917 - fine_loss: 0.499220609664917
2024/03/15 06:32:01 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8515260219573975 - fine_loss: 0.8515260219573975
2024/03/15 06:33:51 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.953697919845581 - fine_loss: 0.953697919845581
2024/03/15 06:35:27 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.9318894 | 0.9858092 | 0.9957183 | 0.0923063 | 1.5112557 | 0.0394818 | 0.1269675 | 11.6301649 | 0.1761516 | 1.1147971 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 06:37:24 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8879871368408203 - fine_loss: 0.8879871368408203
2024/03/15 06:39:14 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.4138840436935425 - fine_loss: 1.4138840436935425
2024/03/15 06:41:05 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3911192417144775 - fine_loss: 1.3911192417144775
2024/03/15 06:42:59 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9037826061248779 - fine_loss: 0.9037826061248779
2024/03/15 06:46:06 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7059022784233093 - fine_loss: 0.7059022784233093
2024/03/15 06:47:58 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8616353273391724 - fine_loss: 0.8616353273391724
2024/03/15 06:49:51 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8395438194274902 - fine_loss: 0.8395438194274902
2024/03/15 06:51:43 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6362200379371643 - fine_loss: 0.6362200379371643
2024/03/15 06:53:21 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  | rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
| 0.9486918 | 0.9883879 | 0.9965515 | 0.0802352 | 1.4414517 | 0.0349744 | 0.116316 | 10.9957016 | 0.1575956 | 1.0969994 |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
2024/03/15 06:55:18 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6189630627632141 - fine_loss: 0.6189630627632141
2024/03/15 06:57:11 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1719452142715454 - fine_loss: 1.1719452142715454
2024/03/15 06:58:55 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.142961025238037 - fine_loss: 1.142961025238037
2024/03/15 07:00:45 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.719948649406433 - fine_loss: 1.719948649406433
2024/03/15 07:03:58 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6470488905906677 - fine_loss: 0.6470488905906677
2024/03/15 07:05:49 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5520279407501221 - fine_loss: 0.5520279407501221
2024/03/15 07:07:38 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8810967803001404 - fine_loss: 0.8810967803001404
2024/03/15 07:09:32 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6827142238616943 - fine_loss: 0.6827142238616943
2024/03/15 07:11:07 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |   see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
| 0.9523656 | 0.9892937 | 0.9967417 | 0.0767006 | 1.4133022 | 0.0333895 | 0.1125023 | 10.666611 | 0.1523504 | 1.061902 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
2024/03/15 07:13:02 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.8002086877822876 - fine_loss: 1.8002086877822876
2024/03/15 07:14:51 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5043245553970337 - fine_loss: 0.5043245553970337
2024/03/15 07:16:39 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6025413274765015 - fine_loss: 1.6025413274765015
2024/03/15 07:18:29 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3183393478393555 - fine_loss: 1.3183393478393555
2024/03/15 07:21:41 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.6571695804595947 - fine_loss: 1.6571695804595947
2024/03/15 07:23:30 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0306520462036133 - fine_loss: 1.0306520462036133
2024/03/15 07:25:19 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8030037879943848 - fine_loss: 0.8030037879943848
2024/03/15 07:27:09 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6139640808105469 - fine_loss: 0.6139640808105469
2024/03/15 07:28:49 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.9531358 | 0.9897053 | 0.9967571 | 0.0759499 | 1.4041272 | 0.0327699 | 0.1107659 | 10.5243982 | 0.1508702 | 1.0635976 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 07:28:49 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict
2024/03/15 07:28:49 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :>
2024/03/15 07:28:49 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vits_u4k/fine_pretrain