depthanything_vits_u4k/coarse_pretrain/20240315_002030.log

2024/03/15 00:20:41 - patchstitcher - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0]
    CUDA available: True
    numpy_random_seed: 621
    GPU 0,1,2,3: NVIDIA A100-SXM4-80GB
    CUDA_HOME: /sw/rl9g/cuda/11.8/rl9_binary
    NVCC: Cuda compilation tools, release 11.8, V11.8.89
    GCC: gcc (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2)
    PyTorch: 2.1.2
    PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.8
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.7
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

    TorchVision: 0.16.2
    OpenCV: 4.8.1
    MMEngine: 0.10.2

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

2024/03/15 00:20:41 - patchstitcher - INFO - Config:
collect_input_args = [
    'image_lr',
    'crops_image_hr',
    'depth_gt',
    'crop_depths',
    'bboxs',
    'image_hr',
]
convert_syncbn = True
debug = False
env_cfg = dict(
    cudnn_benchmark=True,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='forkserver'))
find_unused_parameters = True
general_dataloader = dict(
    batch_size=1,
    dataset=dict(
        dataset_name='', gt_dir=None, rgb_image_dir='', type='ImageDataset'),
    num_workers=2)
launcher = 'pytorch'
log_name = 'coarse_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,
    sigloss=dict(type='SILogLoss'),
    target='coarse',
    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 = [
    'coarse',
    '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/coarse_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 00:20:41 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vits.pt
2024/03/15 00:20:41 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'>
2024/03/15 00:20:42 - patchstitcher - INFO - DistributedDataParallel(
  (module): BaselinePretrain(
    (coarse_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 00:20:47 - patchstitcher - INFO - successfully init trainer
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.cls_token
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.pos_embed
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.mask_token
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.bias
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.3.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.3.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer1_rn.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer2_rn.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer3_rn.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer4_rn.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.out_conv.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.out_conv.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conv2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conv2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.bias
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.weight
2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.bias
2024/03/15 00:23:05 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.218886375427246 - coarse_loss: 2.218886375427246
2024/03/15 00:24:52 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.0132031440734863 - coarse_loss: 2.0132031440734863
2024/03/15 00:26:41 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.1340489387512207 - coarse_loss: 2.1340489387512207
2024/03/15 00:28:31 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.68356192111969 - coarse_loss: 1.68356192111969
2024/03/15 00:31:46 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1240144968032837 - coarse_loss: 1.1240144968032837
2024/03/15 00:33:37 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.2552540302276611 - coarse_loss: 1.2552540302276611
2024/03/15 00:35:27 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3931670188903809 - coarse_loss: 1.3931670188903809
2024/03/15 00:37:17 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4315416812896729 - coarse_loss: 1.4315416812896729
2024/03/15 00:38:56 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |   rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
| 0.9222131 | 0.9841732 | 0.9937032 | 0.0942684 | 1.901311 | 0.0392215 | 0.1319014 | 11.5870857 | 0.3169146 | 1.4523976 |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 00:40:53 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3891286849975586 - coarse_loss: 1.3891286849975586
2024/03/15 00:42:45 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.3853542804718018 - coarse_loss: 1.3853542804718018
2024/03/15 00:44:31 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6085820198059082 - coarse_loss: 1.6085820198059082
2024/03/15 00:46:24 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2743269205093384 - coarse_loss: 1.2743269205093384
2024/03/15 00:49:33 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4644969701766968 - coarse_loss: 1.4644969701766968
2024/03/15 00:51:20 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.040415644645691 - coarse_loss: 1.040415644645691
2024/03/15 00:53:07 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2523736953735352 - coarse_loss: 1.2523736953735352
2024/03/15 00:54:57 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7893640995025635 - coarse_loss: 0.7893640995025635
2024/03/15 00:56:31 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |   rmse   |   log_10  |  rmse_log |   silog    |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
| 0.9466366 | 0.9857079 | 0.9944696 | 0.0784504 | 1.723246 | 0.0331783 | 0.1166779 | 10.4672395 | 0.2658952 | 1.2480133 |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 00:58:25 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8934182524681091 - coarse_loss: 0.8934182524681091
2024/03/15 01:00:10 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0365135669708252 - coarse_loss: 1.0365135669708252
2024/03/15 01:02:00 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0158889293670654 - coarse_loss: 1.0158889293670654
2024/03/15 01:03:50 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7366129159927368 - coarse_loss: 0.7366129159927368
2024/03/15 01:07:04 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4556183815002441 - coarse_loss: 1.4556183815002441
2024/03/15 01:08:51 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.093213677406311 - coarse_loss: 1.093213677406311
2024/03/15 01:10:45 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8329901099205017 - coarse_loss: 0.8329901099205017
2024/03/15 01:12:32 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8255199193954468 - coarse_loss: 0.8255199193954468
2024/03/15 01:14:05 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9492006 | 0.9876058 | 0.9947174 | 0.0765434 | 1.6623389 | 0.0336977 | 0.1157899 | 10.168448 | 0.2274059 | 1.1601292 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 01:16:01 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9320656061172485 - coarse_loss: 0.9320656061172485
2024/03/15 01:17:44 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.3558683395385742 - coarse_loss: 1.3558683395385742
2024/03/15 01:19:36 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1851251125335693 - coarse_loss: 1.1851251125335693
2024/03/15 01:21:28 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7694360613822937 - coarse_loss: 0.7694360613822937
2024/03/15 01:24:39 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9268642067909241 - coarse_loss: 0.9268642067909241
2024/03/15 01:26:28 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0070387125015259 - coarse_loss: 1.0070387125015259
2024/03/15 01:28:17 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3363308906555176 - coarse_loss: 1.3363308906555176
2024/03/15 01:30:03 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.549015998840332 - coarse_loss: 1.549015998840332
2024/03/15 01:31:38 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |  sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+
| 0.9580896 | 0.9882235 | 0.9949475 | 0.0697348 | 1.6023046 | 0.0295156 | 0.1067427 | 9.6755001 | 0.224005 | 1.1545794 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+
2024/03/15 01:33:33 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2725939750671387 - coarse_loss: 1.2725939750671387
2024/03/15 01:35:25 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.8319188356399536 - coarse_loss: 1.8319188356399536
2024/03/15 01:37:16 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9146164655685425 - coarse_loss: 0.9146164655685425
2024/03/15 01:39:08 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9933633208274841 - coarse_loss: 0.9933633208274841
2024/03/15 01:42:21 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.531670331954956 - coarse_loss: 0.531670331954956
2024/03/15 01:44:13 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.98005211353302 - coarse_loss: 0.98005211353302
2024/03/15 01:46:08 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.562972068786621 - coarse_loss: 1.562972068786621
2024/03/15 01:48:00 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0578055381774902 - coarse_loss: 1.0578055381774902
2024/03/15 01:49:39 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+----------+-----------+--------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |  log_10  |  rmse_log | silog  |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+----------+-----------+--------+-----------+-----------+
| 0.9573399 | 0.9884624 | 0.9949526 | 0.0727779 | 1.5619678 | 0.030998 | 0.1089102 | 9.5524 | 0.2075647 | 1.1259904 |
+-----------+-----------+-----------+-----------+-----------+----------+-----------+--------+-----------+-----------+
2024/03/15 01:51:35 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9536416530609131 - coarse_loss: 0.9536416530609131
2024/03/15 01:53:33 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.061272382736206 - coarse_loss: 1.061272382736206
2024/03/15 01:55:27 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.403846263885498 - coarse_loss: 1.403846263885498
2024/03/15 01:57:19 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6634379625320435 - coarse_loss: 0.6634379625320435
2024/03/15 02:00:39 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7105982303619385 - coarse_loss: 0.7105982303619385
2024/03/15 02:02:34 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8706010580062866 - coarse_loss: 0.8706010580062866
2024/03/15 02:04:29 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.013525366783142 - coarse_loss: 1.013525366783142
2024/03/15 02:06:17 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9357657432556152 - coarse_loss: 0.9357657432556152
2024/03/15 02:07:50 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9572283 | 0.9886936 | 0.9950871 | 0.0731142 | 1.5668887 | 0.0308406 | 0.1077591 | 9.5263897 | 0.2176418 | 1.1755943 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 02:09:47 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4000838994979858 - coarse_loss: 1.4000838994979858
2024/03/15 02:11:41 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.147301435470581 - coarse_loss: 1.147301435470581
2024/03/15 02:13:39 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9417784214019775 - coarse_loss: 0.9417784214019775
2024/03/15 02:15:36 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.898872971534729 - coarse_loss: 0.898872971534729
2024/03/15 02:18:53 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6218137741088867 - coarse_loss: 0.6218137741088867
2024/03/15 02:20:49 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9591147303581238 - coarse_loss: 0.9591147303581238
2024/03/15 02:22:42 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7330798506736755 - coarse_loss: 0.7330798506736755
2024/03/15 02:24:37 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.671249508857727 - coarse_loss: 0.671249508857727
2024/03/15 02:26:12 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9656246 | 0.9891472 | 0.9951051 | 0.0614303 | 1.5103214 | 0.0264747 | 0.1001097 | 9.4011921 | 0.1946697 | 1.0991172 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 02:28:10 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7798411846160889 - coarse_loss: 0.7798411846160889
2024/03/15 02:30:02 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9757544994354248 - coarse_loss: 0.9757544994354248
2024/03/15 02:31:49 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1485944986343384 - coarse_loss: 1.1485944986343384
2024/03/15 02:33:42 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8730670809745789 - coarse_loss: 0.8730670809745789
2024/03/15 02:36:57 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0859500169754028 - coarse_loss: 1.0859500169754028
2024/03/15 02:38:46 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9123729467391968 - coarse_loss: 0.9123729467391968
2024/03/15 02:40:36 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0700657367706299 - coarse_loss: 1.0700657367706299
2024/03/15 02:42:24 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.8393980264663696 - coarse_loss: 1.8393980264663696
2024/03/15 02:43:58 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |   rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
| 0.9678091 | 0.9892931 | 0.9952321 | 0.0607629 | 1.488932 | 0.0257705 | 0.0981663 | 9.0934609 | 0.1966839 | 1.0878515 |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 02:45:51 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7053128480911255 - coarse_loss: 0.7053128480911255
2024/03/15 02:47:43 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9886703491210938 - coarse_loss: 0.9886703491210938
2024/03/15 02:49:32 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.180053949356079 - coarse_loss: 1.180053949356079
2024/03/15 02:51:22 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.316230297088623 - coarse_loss: 1.316230297088623
2024/03/15 02:54:39 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7665231227874756 - coarse_loss: 0.7665231227874756
2024/03/15 02:56:30 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6590834856033325 - coarse_loss: 0.6590834856033325
2024/03/15 02:58:17 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9268083572387695 - coarse_loss: 0.9268083572387695
2024/03/15 03:00:07 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.255874752998352 - coarse_loss: 1.255874752998352
2024/03/15 03:01:42 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9691702 | 0.9894969 | 0.9952754 | 0.0559551 | 1.4743834 | 0.0240017 | 0.0943829 | 8.8561864 | 0.1819411 | 1.0395958 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 03:03:35 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6205350756645203 - coarse_loss: 0.6205350756645203
2024/03/15 03:05:29 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6529569625854492 - coarse_loss: 0.6529569625854492
2024/03/15 03:07:18 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8907508850097656 - coarse_loss: 0.8907508850097656
2024/03/15 03:09:13 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5823774337768555 - coarse_loss: 0.5823774337768555
2024/03/15 03:12:22 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3379265069961548 - coarse_loss: 1.3379265069961548
2024/03/15 03:14:13 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.615516185760498 - coarse_loss: 0.615516185760498
2024/03/15 03:16:04 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5864847302436829 - coarse_loss: 0.5864847302436829
2024/03/15 03:17:54 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9669459462165833 - coarse_loss: 0.9669459462165833
2024/03/15 03:19:29 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9697102 | 0.9895742 | 0.9953449 | 0.0539071 | 1.4497501 | 0.0229091 | 0.0925752 | 8.7784555 | 0.1802817 | 1.0580258 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 03:21:25 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9557666778564453 - coarse_loss: 0.9557666778564453
2024/03/15 03:23:15 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6958411931991577 - coarse_loss: 0.6958411931991577
2024/03/15 03:25:01 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5607629418373108 - coarse_loss: 0.5607629418373108
2024/03/15 03:26:54 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.8118071556091309 - coarse_loss: 1.8118071556091309
2024/03/15 03:30:05 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0183720588684082 - coarse_loss: 1.0183720588684082
2024/03/15 03:31:53 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0083253383636475 - coarse_loss: 1.0083253383636475
2024/03/15 03:33:45 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5852430462837219 - coarse_loss: 0.5852430462837219
2024/03/15 03:35:35 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8135958909988403 - coarse_loss: 0.8135958909988403
2024/03/15 03:37:10 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9699838 | 0.9896694 | 0.9953757 | 0.0526183 | 1.4463599 | 0.0224501 | 0.0915034 | 8.7251583 | 0.1771403 | 1.0479052 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 03:39:04 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6198912262916565 - coarse_loss: 0.6198912262916565
2024/03/15 03:40:57 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1995759010314941 - coarse_loss: 1.1995759010314941
2024/03/15 03:42:47 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.7696393728256226 - coarse_loss: 1.7696393728256226
2024/03/15 03:44:34 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4660639762878418 - coarse_loss: 1.4660639762878418
2024/03/15 03:47:48 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9167467355728149 - coarse_loss: 0.9167467355728149
2024/03/15 03:49:40 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6638955473899841 - coarse_loss: 0.6638955473899841
2024/03/15 03:51:31 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4969237446784973 - coarse_loss: 0.4969237446784973
2024/03/15 03:53:18 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5059656500816345 - coarse_loss: 0.5059656500816345
2024/03/15 03:54:52 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |   see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
| 0.9701259 | 0.9896677 | 0.9953767 | 0.0521426 | 1.4457442 | 0.0222779 | 0.0914182 | 8.7319618 | 0.1776046 | 1.046505 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
2024/03/15 03:54:52 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict
2024/03/15 03:54:52 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :>
2024/03/15 03:54:52 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vits_u4k/coarse_pretrain