depthanything_vitb_u4k/coarse_pretrain/20240315_095516.log

2024/03/15 09:55:26 - patchstitcher - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0]
    CUDA available: True
    numpy_random_seed: 621
    GPU 0,1,2,3: NVIDIA A100-SXM4-80GB
    CUDA_HOME: /sw/rl9g/cuda/11.8/rl9_binary
    NVCC: Cuda compilation tools, release 11.8, V11.8.89
    GCC: gcc (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2)
    PyTorch: 2.1.2
    PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.8
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.7
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

    TorchVision: 0.16.2
    OpenCV: 4.8.1
    MMEngine: 0.10.2

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

2024/03/15 09:55:26 - patchstitcher - INFO - Config:
collect_input_args = [
    'image_lr',
    'crops_image_hr',
    'depth_gt',
    'crop_depths',
    'bboxs',
    'image_hr',
]
convert_syncbn = True
debug = False
env_cfg = dict(
    cudnn_benchmark=True,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='forkserver'))
find_unused_parameters = True
general_dataloader = dict(
    batch_size=1,
    dataset=dict(
        dataset_name='', gt_dir=None, rgb_image_dir='', type='ImageDataset'),
    num_workers=2)
launcher = 'pytorch'
log_name = '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='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,
    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',
    '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/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='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 09:55:28 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vitb.pt
2024/03/15 09:55:28 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'>
2024/03/15 09:55:28 - patchstitcher - INFO - DistributedDataParallel(
  (module): BaselinePretrain(
    (coarse_branch): ZoeDepth(
      (core): DepthAnythingCore(
        (core): DPT_DINOv2(
          (pretrained): DinoVisionTransformer(
            (patch_embed): PatchEmbed(
              (proj): Conv2d(3, 768, kernel_size=(14, 14), stride=(14, 14))
              (norm): Identity()
            )
            (blocks): ModuleList(
              (0-11): 12 x NestedTensorBlock(
                (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
                (attn): MemEffAttention(
                  (qkv): Linear(in_features=768, out_features=2304, bias=True)
                  (attn_drop): Dropout(p=0.0, inplace=False)
                  (proj): Linear(in_features=768, out_features=768, bias=True)
                  (proj_drop): Dropout(p=0.0, inplace=False)
                )
                (ls1): LayerScale()
                (drop_path1): Identity()
                (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
                (mlp): Mlp(
                  (fc1): Linear(in_features=768, out_features=3072, bias=True)
                  (act): GELU(approximate='none')
                  (fc2): Linear(in_features=3072, out_features=768, bias=True)
                  (drop): Dropout(p=0.0, inplace=False)
                )
                (ls2): LayerScale()
                (drop_path2): Identity()
              )
            )
            (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
            (head): Identity()
          )
          (depth_head): DPTHead(
            (projects): ModuleList(
              (0): Conv2d(768, 96, kernel_size=(1, 1), stride=(1, 1))
              (1): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1))
              (2): Conv2d(768, 384, kernel_size=(1, 1), stride=(1, 1))
              (3): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1))
            )
            (resize_layers): ModuleList(
              (0): ConvTranspose2d(96, 96, kernel_size=(4, 4), stride=(4, 4))
              (1): ConvTranspose2d(192, 192, kernel_size=(2, 2), stride=(2, 2))
              (2): Identity()
              (3): Conv2d(768, 768, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
            )
            (scratch): Module(
              (layer1_rn): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer2_rn): Conv2d(192, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer3_rn): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (layer4_rn): Conv2d(768, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (refinenet1): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (refinenet2): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (refinenet3): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (refinenet4): FeatureFusionBlock(
                (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
                (resConfUnit1): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (resConfUnit2): ResidualConvUnit(
                  (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                  (activation): ReLU()
                  (skip_add): FloatFunctional(
                    (activation_post_process): Identity()
                  )
                )
                (skip_add): FloatFunctional(
                  (activation_post_process): Identity()
                )
              )
              (output_conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (output_conv2): Sequential(
                (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                (1): ReLU(inplace=True)
                (2): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))
                (3): ReLU(inplace=True)
                (4): Identity()
              )
            )
          )
        )
      )
      (conv2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (seed_bin_regressor): SeedBinRegressorUnnormed(
        (_net): Sequential(
          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
      (seed_projector): Projector(
        (_net): Sequential(
          (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (projectors): ModuleList(
        (0-3): 4 x Projector(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          )
        )
      )
      (attractors): ModuleList(
        (0): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
        (1): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
        (2): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
        (3): AttractorLayerUnnormed(
          (_net): Sequential(
            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (1): ReLU(inplace=True)
            (2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
            (3): Softplus(beta=1, threshold=20)
          )
        )
      )
      (conditional_log_binomial): ConditionalLogBinomial(
        (log_binomial_transform): LogBinomial()
        (mlp): Sequential(
          (0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1))
          (1): GELU(approximate='none')
          (2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1))
          (3): Softplus(beta=1, threshold=20)
        )
      )
    )
    (sigloss): SILogLoss()
  )
)
2024/03/15 09:55:34 - patchstitcher - INFO - successfully init trainer
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.cls_token
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.pos_embed
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.mask_token
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.bias
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2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.3.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.3.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer1_rn.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer2_rn.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer3_rn.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer4_rn.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.out_conv.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.out_conv.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.conv2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.conv2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.bias
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.weight
2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.bias
2024/03/15 09:57:50 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.9729490280151367 - coarse_loss: 1.9729490280151367
2024/03/15 09:59:39 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.6159499883651733 - coarse_loss: 1.6159499883651733
2024/03/15 10:01:20 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6653645038604736 - coarse_loss: 1.6653645038604736
2024/03/15 10:03:08 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3738189935684204 - coarse_loss: 1.3738189935684204
2024/03/15 10:06:24 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0679881572723389 - coarse_loss: 1.0679881572723389
2024/03/15 10:08:12 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0449714660644531 - coarse_loss: 1.0449714660644531
2024/03/15 10:09:57 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3200674057006836 - coarse_loss: 1.3200674057006836
2024/03/15 10:11:44 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2463884353637695 - coarse_loss: 1.2463884353637695
2024/03/15 10:13:21 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |    a3    | abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9277873 | 0.9864464 | 0.994876 | 0.093889 | 1.7125608 | 0.0411139 | 0.1284599 | 10.310956 | 0.2504752 | 1.2484615 |
+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 10:15:11 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1642838716506958 - coarse_loss: 1.1642838716506958
2024/03/15 10:16:56 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1062591075897217 - coarse_loss: 1.1062591075897217
2024/03/15 10:18:40 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.491640329360962 - coarse_loss: 1.491640329360962
2024/03/15 10:20:26 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0693362951278687 - coarse_loss: 1.0693362951278687
2024/03/15 10:23:28 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2830930948257446 - coarse_loss: 1.2830930948257446
2024/03/15 10:25:13 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8494630455970764 - coarse_loss: 0.8494630455970764
2024/03/15 10:26:59 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.100481390953064 - coarse_loss: 1.100481390953064
2024/03/15 10:28:45 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6722239255905151 - coarse_loss: 0.6722239255905151
2024/03/15 10:30:18 - patchstitcher - INFO - Evaluation Summary: 
+----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+
|    a1    |     a2    |     a3    |  abs_rel  |    rmse   |  log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+
| 0.961338 | 0.9893523 | 0.9953463 | 0.0692743 | 1.5390607 | 0.030108 | 0.1050118 | 9.1967623 | 0.1975309 | 1.1110629 |
+----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+
2024/03/15 10:32:10 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5996298789978027 - coarse_loss: 0.5996298789978027
2024/03/15 10:33:58 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5094271302223206 - coarse_loss: 0.5094271302223206
2024/03/15 10:35:48 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7459169626235962 - coarse_loss: 0.7459169626235962
2024/03/15 10:37:33 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7367539405822754 - coarse_loss: 0.7367539405822754
2024/03/15 10:40:39 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3089935779571533 - coarse_loss: 1.3089935779571533
2024/03/15 10:42:24 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9458222985267639 - coarse_loss: 0.9458222985267639
2024/03/15 10:44:12 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7383743524551392 - coarse_loss: 0.7383743524551392
2024/03/15 10:45:59 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6774943470954895 - coarse_loss: 0.6774943470954895
2024/03/15 10:47:29 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    | abs_rel  |   rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+
| 0.9625513 | 0.9896059 | 0.9953454 | 0.076086 | 1.553624 | 0.0339274 | 0.1113379 | 8.9179546 | 0.1912439 | 1.0962123 |
+-----------+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 10:49:20 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7863880395889282 - coarse_loss: 0.7863880395889282
2024/03/15 10:51:04 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1585361957550049 - coarse_loss: 1.1585361957550049
2024/03/15 10:52:54 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1414254903793335 - coarse_loss: 1.1414254903793335
2024/03/15 10:54:41 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6607706546783447 - coarse_loss: 0.6607706546783447
2024/03/15 10:57:47 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8438395857810974 - coarse_loss: 0.8438395857810974
2024/03/15 10:59:37 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.931841254234314 - coarse_loss: 0.931841254234314
2024/03/15 11:01:23 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2649768590927124 - coarse_loss: 1.2649768590927124
2024/03/15 11:03:05 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.356317400932312 - coarse_loss: 1.356317400932312
2024/03/15 11:04:39 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9688624 | 0.9900475 | 0.9955412 | 0.0621825 | 1.4741381 | 0.0269014 | 0.0983563 | 8.5882915 | 0.1738514 | 1.0249666 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 11:06:28 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1434571743011475 - coarse_loss: 1.1434571743011475
2024/03/15 11:08:19 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.7681660652160645 - coarse_loss: 1.7681660652160645
2024/03/15 11:10:04 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8547622561454773 - coarse_loss: 0.8547622561454773
2024/03/15 11:11:49 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.869714617729187 - coarse_loss: 0.869714617729187
2024/03/15 11:14:59 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5332772731781006 - coarse_loss: 0.5332772731781006
2024/03/15 11:16:44 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8691495060920715 - coarse_loss: 0.8691495060920715
2024/03/15 11:18:28 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.4371870756149292 - coarse_loss: 1.4371870756149292
2024/03/15 11:20:14 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9575653076171875 - coarse_loss: 0.9575653076171875
2024/03/15 11:21:45 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9679335 | 0.9903103 | 0.9957452 | 0.0634565 | 1.4144222 | 0.0269387 | 0.0964634 | 8.5336222 | 0.1681394 | 1.0266862 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 11:23:36 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8048105835914612 - coarse_loss: 0.8048105835914612
2024/03/15 11:25:22 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8616613149642944 - coarse_loss: 0.8616613149642944
2024/03/15 11:27:12 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.221915364265442 - coarse_loss: 1.221915364265442
2024/03/15 11:28:59 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5273403525352478 - coarse_loss: 0.5273403525352478
2024/03/15 11:31:59 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6490796208381653 - coarse_loss: 0.6490796208381653
2024/03/15 11:33:46 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9228641986846924 - coarse_loss: 0.9228641986846924
2024/03/15 11:35:30 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8991217017173767 - coarse_loss: 0.8991217017173767
2024/03/15 11:37:21 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.778996467590332 - coarse_loss: 0.778996467590332
2024/03/15 11:38:51 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  | rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+
| 0.9700605 | 0.9907225 | 0.9956863 | 0.0593423 | 1.3817834 | 0.0258237 | 0.095056 | 8.4508466 | 0.1639893 | 1.0006335 |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+
2024/03/15 11:40:42 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2246499061584473 - coarse_loss: 1.2246499061584473
2024/03/15 11:42:33 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.055445671081543 - coarse_loss: 1.055445671081543
2024/03/15 11:44:18 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8403045535087585 - coarse_loss: 0.8403045535087585
2024/03/15 11:46:03 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7852007150650024 - coarse_loss: 0.7852007150650024
2024/03/15 11:49:05 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5313113331794739 - coarse_loss: 0.5313113331794739
2024/03/15 11:50:53 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.803260326385498 - coarse_loss: 0.803260326385498
2024/03/15 11:52:35 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6353864669799805 - coarse_loss: 0.6353864669799805
2024/03/15 11:54:22 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.603277862071991 - coarse_loss: 0.603277862071991
2024/03/15 11:55:54 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |  log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+
| 0.9716077 | 0.9908243 | 0.9958379 | 0.0603097 | 1.3795547 | 0.025826 | 0.0942337 | 8.2481922 | 0.1615328 | 1.0314286 |
+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+
2024/03/15 11:57:46 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.68681800365448 - coarse_loss: 0.68681800365448
2024/03/15 11:59:38 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8562105894088745 - coarse_loss: 0.8562105894088745
2024/03/15 12:01:24 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0672423839569092 - coarse_loss: 1.0672423839569092
2024/03/15 12:03:05 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7026287317276001 - coarse_loss: 0.7026287317276001
2024/03/15 12:06:08 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9886091947555542 - coarse_loss: 0.9886091947555542
2024/03/15 12:07:54 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6522326469421387 - coarse_loss: 0.6522326469421387
2024/03/15 12:09:39 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9577221870422363 - coarse_loss: 0.9577221870422363
2024/03/15 12:11:22 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.7307658195495605 - coarse_loss: 1.7307658195495605
2024/03/15 12:12:51 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9747152 | 0.9908944 | 0.9959154 | 0.0511857 | 1.3574797 | 0.0221211 | 0.0867927 | 7.9538576 | 0.1511261 | 1.0003225 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 12:14:43 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5646010637283325 - coarse_loss: 0.5646010637283325
2024/03/15 12:16:28 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8057535290718079 - coarse_loss: 0.8057535290718079
2024/03/15 12:18:17 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.11107337474823 - coarse_loss: 1.11107337474823
2024/03/15 12:20:01 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.187990427017212 - coarse_loss: 1.187990427017212
2024/03/15 12:23:09 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6382083892822266 - coarse_loss: 0.6382083892822266
2024/03/15 12:24:49 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5392951965332031 - coarse_loss: 0.5392951965332031
2024/03/15 12:26:37 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8188748359680176 - coarse_loss: 0.8188748359680176
2024/03/15 12:28:18 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0811210870742798 - coarse_loss: 1.0811210870742798
2024/03/15 12:29:49 - patchstitcher - INFO - Evaluation Summary: 
+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|    a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.975323 | 0.9911468 | 0.9959684 | 0.0483459 | 1.3259571 | 0.0207656 | 0.0842995 | 7.8959624 | 0.1478599 | 0.9762505 |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 12:31:43 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5005310773849487 - coarse_loss: 0.5005310773849487
2024/03/15 12:33:28 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5474035143852234 - coarse_loss: 0.5474035143852234
2024/03/15 12:35:16 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7799822092056274 - coarse_loss: 0.7799822092056274
2024/03/15 12:37:02 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5381927490234375 - coarse_loss: 0.5381927490234375
2024/03/15 12:40:07 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1203773021697998 - coarse_loss: 1.1203773021697998
2024/03/15 12:41:51 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5552318096160889 - coarse_loss: 0.5552318096160889
2024/03/15 12:43:35 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4946790933609009 - coarse_loss: 0.4946790933609009
2024/03/15 12:45:21 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.829839825630188 - coarse_loss: 0.829839825630188
2024/03/15 12:46:50 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |  log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+
| 0.9759003 | 0.9912674 | 0.9959566 | 0.0472804 | 1.3156906 | 0.020464 | 0.0841626 | 7.7711489 | 0.1448604 | 0.9643456 |
+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+
2024/03/15 12:48:43 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8187640905380249 - coarse_loss: 0.8187640905380249
2024/03/15 12:50:30 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5510168671607971 - coarse_loss: 0.5510168671607971
2024/03/15 12:52:22 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5071703791618347 - coarse_loss: 0.5071703791618347
2024/03/15 12:54:08 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.6241310834884644 - coarse_loss: 1.6241310834884644
2024/03/15 12:57:18 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9662288427352905 - coarse_loss: 0.9662288427352905
2024/03/15 12:59:03 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.915822446346283 - coarse_loss: 0.915822446346283
2024/03/15 13:00:45 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.48746258020401 - coarse_loss: 0.48746258020401
2024/03/15 13:02:29 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7346612811088562 - coarse_loss: 0.7346612811088562
2024/03/15 13:04:01 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |  silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
| 0.9762025 | 0.9913185 | 0.9959977 | 0.0456843 | 1.3065255 | 0.0197035 | 0.0823783 | 7.684332 | 0.1431234 | 0.9606835 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
2024/03/15 13:05:51 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5825149416923523 - coarse_loss: 0.5825149416923523
2024/03/15 13:07:38 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0635181665420532 - coarse_loss: 1.0635181665420532
2024/03/15 13:09:24 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.632516622543335 - coarse_loss: 1.632516622543335
2024/03/15 13:11:08 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.353378415107727 - coarse_loss: 1.353378415107727
2024/03/15 13:14:18 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8277870416641235 - coarse_loss: 0.8277870416641235
2024/03/15 13:16:02 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5105581283569336 - coarse_loss: 0.5105581283569336
2024/03/15 13:17:45 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.43523621559143066 - coarse_loss: 0.43523621559143066
2024/03/15 13:19:31 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.40485745668411255 - coarse_loss: 0.40485745668411255
2024/03/15 13:21:02 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9762546 | 0.9913396 | 0.9959976 | 0.0452784 | 1.2974494 | 0.0194901 | 0.0821238 | 7.7005432 | 0.1431584 | 0.9635146 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 13:21:02 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict
2024/03/15 13:21:02 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :>
2024/03/15 13:21:03 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vitb_u4k/coarse_pretrain