depthanything_vitb_u4k/fine_pretrain/20240315_153036.log

2024/03/15 15:30:44 - 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 15:30:44 - 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='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),
    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',
    '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=24,
    save_checkpoint_interval=24,
    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/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='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 15:30:45 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vitb.pt
2024/03/15 15:30:45 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'>
2024/03/15 15:30:45 - patchstitcher - INFO - DistributedDataParallel(
  (module): BaselinePretrain(
    (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()
  )
)
2024/03/15 15:30:51 - patchstitcher - INFO - successfully init trainer
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.cls_token
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.pos_embed
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.mask_token
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.weight
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2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.resize_layers.1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.resize_layers.3.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.resize_layers.3.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.layer1_rn.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.layer2_rn.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.layer3_rn.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.layer4_rn.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.out_conv.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.out_conv.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.out_conv.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.out_conv.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conv2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conv2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.bias
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.weight
2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.bias
2024/03/15 15:33:25 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.288588523864746 - fine_loss: 2.288588523864746
2024/03/15 15:35:13 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.749260425567627 - fine_loss: 1.749260425567627
2024/03/15 15:36:58 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.603142499923706 - fine_loss: 2.603142499923706
2024/03/15 15:38:59 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 3.0235860347747803 - fine_loss: 3.0235860347747803
2024/03/15 15:42:38 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.2628891468048096 - fine_loss: 2.2628891468048096
2024/03/15 15:44:44 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.2125635147094727 - fine_loss: 2.2125635147094727
2024/03/15 15:46:44 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.884977102279663 - fine_loss: 1.884977102279663
2024/03/15 15:48:46 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 3.667808771133423 - fine_loss: 3.667808771133423
2024/03/15 15:50:43 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |   rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
| 0.7653929 | 0.9569647 | 0.9891034 | 0.1631364 | 2.063872 | 0.0675193 | 0.2015772 | 17.5721867 | 0.3284417 | 1.5396647 |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 15:52:52 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.9002115726470947 - fine_loss: 1.9002115726470947
2024/03/15 15:54:51 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.533200979232788 - fine_loss: 1.533200979232788
2024/03/15 15:56:53 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3708069324493408 - fine_loss: 1.3708069324493408
2024/03/15 15:58:56 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3536834716796875 - fine_loss: 1.3536834716796875
2024/03/15 16:02:35 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4067535400390625 - fine_loss: 1.4067535400390625
2024/03/15 16:04:38 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.571197509765625 - fine_loss: 1.571197509765625
2024/03/15 16:06:40 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.9749035835266113 - fine_loss: 2.9749035835266113
2024/03/15 16:08:48 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.893333911895752 - fine_loss: 0.893333911895752
2024/03/15 16:10:40 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
|     a1    |     a2    |    a3    |  abs_rel  |    rmse   |   log_10  | rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
| 0.8439181 | 0.9733375 | 0.992747 | 0.1316369 | 1.8230734 | 0.0558847 | 0.171333 | 15.4284363 | 0.2575101 | 1.3799866 |
+-----------+-----------+----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
2024/03/15 16:12:51 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5204694271087646 - fine_loss: 1.5204694271087646
2024/03/15 16:14:53 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0538222789764404 - fine_loss: 1.0538222789764404
2024/03/15 16:17:00 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.246050477027893 - fine_loss: 1.246050477027893
2024/03/15 16:19:04 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4139764308929443 - fine_loss: 1.4139764308929443
2024/03/15 16:22:40 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5990095138549805 - fine_loss: 1.5990095138549805
2024/03/15 16:24:45 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.4719877243041992 - fine_loss: 1.4719877243041992
2024/03/15 16:26:49 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.998321533203125 - fine_loss: 0.998321533203125
2024/03/15 16:28:52 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2637615203857422 - fine_loss: 1.2637615203857422
2024/03/15 16:30:46 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.8831826 | 0.9846013 | 0.9953048 | 0.1145366 | 1.6448599 | 0.0488564 | 0.1510406 | 14.0402038 | 0.2199031 | 1.3085128 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 16:32:53 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.65132737159729 - fine_loss: 1.65132737159729
2024/03/15 16:34:56 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.4322144985198975 - fine_loss: 1.4322144985198975
2024/03/15 16:37:04 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.034339427947998 - fine_loss: 1.034339427947998
2024/03/15 16:39:08 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0732086896896362 - fine_loss: 1.0732086896896362
2024/03/15 16:42:43 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3489086627960205 - fine_loss: 1.3489086627960205
2024/03/15 16:44:47 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.4356486797332764 - fine_loss: 1.4356486797332764
2024/03/15 16:46:50 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6865524649620056 - fine_loss: 0.6865524649620056
2024/03/15 16:48:50 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4590085744857788 - fine_loss: 1.4590085744857788
2024/03/15 16:50:41 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  | rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
| 0.8921932 | 0.9874671 | 0.9972081 | 0.1083586 | 1.6257898 | 0.0457595 | 0.142043 | 12.7745355 | 0.2076856 | 1.2743567 |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+
2024/03/15 16:52:44 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.008254885673523 - fine_loss: 1.008254885673523
2024/03/15 16:54:54 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8210620880126953 - fine_loss: 0.8210620880126953
2024/03/15 16:56:55 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.8681334257125854 - fine_loss: 1.8681334257125854
2024/03/15 16:58:59 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9568914771080017 - fine_loss: 0.9568914771080017
2024/03/15 17:02:34 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5452194213867188 - fine_loss: 1.5452194213867188
2024/03/15 17:04:40 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9237810373306274 - fine_loss: 0.9237810373306274
2024/03/15 17:06:43 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.4192367792129517 - fine_loss: 1.4192367792129517
2024/03/15 17:08:47 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1616711616516113 - fine_loss: 1.1616711616516113
2024/03/15 17:10:40 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |    a3    |  abs_rel  |   rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
| 0.9095374 | 0.9878494 | 0.996491 | 0.1000458 | 1.529536 | 0.0445519 | 0.1377915 | 12.2980782 | 0.1741764 | 1.1720957 |
+-----------+-----------+----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 17:12:48 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2545241117477417 - fine_loss: 1.2545241117477417
2024/03/15 17:14:52 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9477699398994446 - fine_loss: 0.9477699398994446
2024/03/15 17:16:59 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3806159496307373 - fine_loss: 1.3806159496307373
2024/03/15 17:19:02 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.12031888961792 - fine_loss: 1.12031888961792
2024/03/15 17:22:38 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9633316993713379 - fine_loss: 0.9633316993713379
2024/03/15 17:24:38 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9473192691802979 - fine_loss: 0.9473192691802979
2024/03/15 17:26:38 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8891739845275879 - fine_loss: 0.8891739845275879
2024/03/15 17:28:46 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9305822849273682 - fine_loss: 0.9305822849273682
2024/03/15 17:30:43 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9285209 | 0.9902661 | 0.9963124 | 0.0922186 | 1.4988106 | 0.0394503 | 0.1265562 | 11.929424 | 0.1792194 | 1.2142439 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 17:32:52 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.26497220993042 - fine_loss: 1.26497220993042
2024/03/15 17:35:00 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.580217957496643 - fine_loss: 1.580217957496643
2024/03/15 17:36:59 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6395942568778992 - fine_loss: 0.6395942568778992
2024/03/15 17:39:02 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.32594698667526245 - fine_loss: 0.32594698667526245
2024/03/15 17:42:34 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.924031674861908 - fine_loss: 0.924031674861908
2024/03/15 17:44:36 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.985018253326416 - fine_loss: 0.985018253326416
2024/03/15 17:46:38 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0442320108413696 - fine_loss: 1.0442320108413696
2024/03/15 17:48:43 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5068702101707458 - fine_loss: 0.5068702101707458
2024/03/15 17:50:33 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.9381619 | 0.9895476 | 0.9972216 | 0.0913334 | 1.5578288 | 0.0391697 | 0.1243245 | 11.1463653 | 0.1706981 | 1.1217431 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 17:52:46 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1108862161636353 - fine_loss: 1.1108862161636353
2024/03/15 17:54:52 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9237959980964661 - fine_loss: 0.9237959980964661
2024/03/15 17:56:56 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.5644421577453613 - fine_loss: 1.5644421577453613
2024/03/15 17:58:54 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7902756929397583 - fine_loss: 0.7902756929397583
2024/03/15 18:02:26 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.966326117515564 - fine_loss: 0.966326117515564
2024/03/15 18:04:32 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9776898622512817 - fine_loss: 0.9776898622512817
2024/03/15 18:06:33 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6681317090988159 - fine_loss: 0.6681317090988159
2024/03/15 18:08:34 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.80037522315979 - fine_loss: 0.80037522315979
2024/03/15 18:10:20 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.9538666 | 0.9917138 | 0.9972104 | 0.0811061 | 1.3823568 | 0.0351258 | 0.1140013 | 10.5376763 | 0.1382621 | 1.0577048 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 18:12:28 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.147787094116211 - fine_loss: 1.147787094116211
2024/03/15 18:14:30 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7300316691398621 - fine_loss: 0.7300316691398621
2024/03/15 18:16:37 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7750428318977356 - fine_loss: 0.7750428318977356
2024/03/15 18:18:37 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.50600266456604 - fine_loss: 1.50600266456604
2024/03/15 18:22:20 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8911293745040894 - fine_loss: 0.8911293745040894
2024/03/15 18:24:18 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5605521202087402 - fine_loss: 0.5605521202087402
2024/03/15 18:26:21 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6763710975646973 - fine_loss: 1.6763710975646973
2024/03/15 18:28:20 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6500707864761353 - fine_loss: 0.6500707864761353
2024/03/15 18:30:14 - patchstitcher - INFO - Evaluation Summary: 
+-----------+----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
|     a1    |    a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.9562948 | 0.990871 | 0.9974688 | 0.0761721 | 1.3729287 | 0.0331131 | 0.1092103 | 10.1530306 | 0.1366973 | 1.0216396 |
+-----------+----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 18:32:23 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5755664110183716 - fine_loss: 0.5755664110183716
2024/03/15 18:34:28 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.2044012546539307 - fine_loss: 1.2044012546539307
2024/03/15 18:36:33 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.266536831855774 - fine_loss: 1.266536831855774
2024/03/15 18:38:35 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7211558818817139 - fine_loss: 0.7211558818817139
2024/03/15 18:42:13 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6136915683746338 - fine_loss: 0.6136915683746338
2024/03/15 18:44:12 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.4747104048728943 - fine_loss: 0.4747104048728943
2024/03/15 18:46:16 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5850560069084167 - fine_loss: 0.5850560069084167
2024/03/15 18:48:21 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.37204447388648987 - fine_loss: 0.37204447388648987
2024/03/15 18:50:16 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
|     a1    |     a2    |    a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |   see    |
+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
| 0.9645657 | 0.9920502 | 0.997654 | 0.0686085 | 1.2732928 | 0.0299144 | 0.1009926 | 9.6382305 | 0.1200509 | 0.993343 |
+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
2024/03/15 18:52:27 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6047840714454651 - fine_loss: 0.6047840714454651
2024/03/15 18:54:31 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5551916360855103 - fine_loss: 0.5551916360855103
2024/03/15 18:56:37 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.32560303807258606 - fine_loss: 0.32560303807258606
2024/03/15 18:58:40 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.7431879043579102 - fine_loss: 1.7431879043579102
2024/03/15 19:02:20 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7936936020851135 - fine_loss: 0.7936936020851135
2024/03/15 19:04:21 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6791415214538574 - fine_loss: 0.6791415214538574
2024/03/15 19:06:23 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6265323758125305 - fine_loss: 0.6265323758125305
2024/03/15 19:08:25 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6945874691009521 - fine_loss: 0.6945874691009521
2024/03/15 19:10:17 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9671118 | 0.9931541 | 0.9976758 | 0.0652155 | 1.2549019 | 0.0282474 | 0.0973396 | 9.2669667 | 0.1172386 | 0.9884787 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 19:12:25 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2996392250061035 - fine_loss: 1.2996392250061035
2024/03/15 19:14:26 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.674423098564148 - fine_loss: 0.674423098564148
2024/03/15 19:16:29 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.0330402851104736 - fine_loss: 2.0330402851104736
2024/03/15 19:18:34 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1583242416381836 - fine_loss: 1.1583242416381836
2024/03/15 19:22:12 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8227792978286743 - fine_loss: 0.8227792978286743
2024/03/15 19:24:12 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6849284172058105 - fine_loss: 0.6849284172058105
2024/03/15 19:26:14 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5954287648200989 - fine_loss: 0.5954287648200989
2024/03/15 19:28:20 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.38687634468078613 - fine_loss: 0.38687634468078613
2024/03/15 19:30:07 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |  silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
| 0.9687062 | 0.9931654 | 0.9976169 | 0.0635503 | 1.2467909 | 0.0277027 | 0.0958232 | 9.191893 | 0.1155029 | 0.9803023 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
2024/03/15 19:30:07 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict
2024/03/15 19:30:07 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :>
2024/03/15 19:30:08 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vitb_u4k/fine_pretrain