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 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 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.proj.weight 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.proj.bias 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.ls1.gamma 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm2.weight 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm2.bias 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc1.weight 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc1.bias 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc2.weight 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc2.bias 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.ls2.gamma 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm1.weight 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm1.bias 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.qkv.weight 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.qkv.bias 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.proj.weight 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.proj.bias 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.ls1.gamma 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm2.weight 2024/03/15 09:55:34 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm2.bias 2024/03/15 09:55:34 - patchstitcher - INFO - 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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