2024/03/15 00:20:41 - patchstitcher - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0] CUDA available: True numpy_random_seed: 621 GPU 0,1,2,3: NVIDIA A100-SXM4-80GB CUDA_HOME: /sw/rl9g/cuda/11.8/rl9_binary NVCC: Cuda compilation tools, release 11.8, V11.8.89 GCC: gcc (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2) PyTorch: 2.1.2 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.8 - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.7 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.16.2 OpenCV: 4.8.1 MMEngine: 0.10.2 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'forkserver'} dist_cfg: {'backend': 'nccl'} seed: 621 Distributed launcher: pytorch Distributed training: True GPU number: 4 ------------------------------------------------------------ 2024/03/15 00:20:41 - patchstitcher - INFO - Config: collect_input_args = [ 'image_lr', 'crops_image_hr', 'depth_gt', 'crop_depths', 'bboxs', 'image_hr', ] convert_syncbn = True debug = False env_cfg = dict( cudnn_benchmark=True, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='forkserver')) find_unused_parameters = True general_dataloader = dict( batch_size=1, dataset=dict( dataset_name='', gt_dir=None, rgb_image_dir='', type='ImageDataset'), num_workers=2) launcher = 'pytorch' log_name = 'coarse_pretrain' max_depth = 80 min_depth = 0.001 model = dict( coarse_branch=dict( attractor_alpha=1000, attractor_gamma=2, attractor_kind='mean', attractor_type='inv', aug=True, bin_centers_type='softplus', bin_embedding_dim=128, clip_grad=0.1, dataset='nyu', depth_anything=True, distributed=True, do_resize=False, force_keep_ar=True, freeze_midas_bn=True, gpu='NULL', img_size=[ 392, 518, ], inverse_midas=False, log_images_every=0.1, max_depth=80, max_temp=50.0, max_translation=100, memory_efficient=True, midas_model_type='vits', min_depth=0.001, min_temp=0.0212, model='zoedepth', n_attractors=[ 16, 8, 4, 1, ], n_bins=64, name='ZoeDepth', notes='', output_distribution='logbinomial', prefetch=False, pretrained_resource='local::./work_dir/DepthAnything_vits.pt', print_losses=False, project='ZoeDepth', random_crop=False, random_translate=False, root='.', save_dir='', shared_dict='NULL', tags='', train_midas=True, translate_prob=0.2, type='DA-ZoeDepth', uid='NULL', use_amp=False, use_pretrained_midas=True, use_shared_dict=False, validate_every=0.25, version_name='v1', workers=16), fine_branch=dict( attractor_alpha=1000, attractor_gamma=2, attractor_kind='mean', attractor_type='inv', aug=True, bin_centers_type='softplus', bin_embedding_dim=128, clip_grad=0.1, dataset='nyu', depth_anything=True, distributed=True, do_resize=False, force_keep_ar=True, freeze_midas_bn=True, gpu='NULL', img_size=[ 392, 518, ], inverse_midas=False, log_images_every=0.1, max_depth=80, max_temp=50.0, max_translation=100, memory_efficient=True, midas_model_type='vits', min_depth=0.001, min_temp=0.0212, model='zoedepth', n_attractors=[ 16, 8, 4, 1, ], n_bins=64, name='ZoeDepth', notes='', output_distribution='logbinomial', prefetch=False, pretrained_resource='local::./work_dir/DepthAnything_vits.pt', print_losses=False, project='ZoeDepth', random_crop=False, random_translate=False, root='.', save_dir='', shared_dict='NULL', tags='', train_midas=True, translate_prob=0.2, type='DA-ZoeDepth', uid='NULL', use_amp=False, use_pretrained_midas=True, use_shared_dict=False, validate_every=0.25, version_name='v1', workers=16), max_depth=80, min_depth=0.001, sigloss=dict(type='SILogLoss'), target='coarse', type='BaselinePretrain') optim_wrapper = dict( clip_grad=dict(max_norm=0.1, norm_type=2, type='norm'), optimizer=dict(lr=4e-06, type='AdamW', weight_decay=0.01), paramwise_cfg=dict(bypass_duplicate=True, custom_keys=dict())) param_scheduler = dict( base_momentum=0.85, cycle_momentum=True, div_factor=1, final_div_factor=10000, max_momentum=0.95, pct_start=0.5, three_phase=False) project = 'patchfusion' tags = [ 'coarse', 'da', 'vits', ] test_in_dataloader = dict( batch_size=1, dataset=dict( data_root='./data/u4k', max_depth=80, min_depth=0.001, mode='infer', split='./data/u4k/splits/test.txt', transform_cfg=dict(network_process_size=[ 384, 512, ]), type='UnrealStereo4kDataset'), num_workers=2) test_out_dataloader = dict( batch_size=1, dataset=dict( data_root='./data/u4k', max_depth=80, min_depth=0.001, mode='infer', split='./data/u4k/splits/test_out.txt', transform_cfg=dict(network_process_size=[ 384, 512, ]), type='UnrealStereo4kDataset'), num_workers=2) train_cfg = dict( eval_start=0, log_interval=100, max_epochs=24, save_checkpoint_interval=24, train_log_img_interval=100, val_interval=2, val_log_img_interval=50, val_type='epoch_base') train_dataloader = dict( batch_size=4, dataset=dict( data_root='./data/u4k', max_depth=80, min_depth=0.001, mode='train', resize_mode='depth-anything', split='./data/u4k/splits/train.txt', transform_cfg=dict( degree=1.0, network_process_size=[ 392, 518, ], random_crop=True), type='UnrealStereo4kDataset'), num_workers=4) val_dataloader = dict( batch_size=1, dataset=dict( data_root='./data/u4k', max_depth=80, min_depth=0.001, mode='infer', resize_mode='depth-anything', split='./data/u4k/splits/val.txt', transform_cfg=dict(degree=1.0, network_process_size=[ 392, 518, ]), type='UnrealStereo4kDataset'), num_workers=2) work_dir = './work_dir/depthanything_vits_u4k/coarse_pretrain' zoe_depth_config = dict( attractor_alpha=1000, attractor_gamma=2, attractor_kind='mean', attractor_type='inv', aug=True, bin_centers_type='softplus', bin_embedding_dim=128, clip_grad=0.1, dataset='nyu', depth_anything=True, distributed=True, do_resize=False, force_keep_ar=True, freeze_midas_bn=True, gpu='NULL', img_size=[ 392, 518, ], inverse_midas=False, log_images_every=0.1, max_depth=80, max_temp=50.0, max_translation=100, memory_efficient=True, midas_model_type='vits', min_depth=0.001, min_temp=0.0212, model='zoedepth', n_attractors=[ 16, 8, 4, 1, ], n_bins=64, name='ZoeDepth', notes='', output_distribution='logbinomial', prefetch=False, pretrained_resource='local::./work_dir/DepthAnything_vits.pt', print_losses=False, project='ZoeDepth', random_crop=False, random_translate=False, root='.', save_dir='', shared_dict='NULL', tags='', train_midas=True, translate_prob=0.2, type='DA-ZoeDepth', uid='NULL', use_amp=False, use_pretrained_midas=True, use_shared_dict=False, validate_every=0.25, version_name='v1', workers=16) 2024/03/15 00:20:41 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vits.pt 2024/03/15 00:20:41 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is 2024/03/15 00:20:42 - patchstitcher - INFO - DistributedDataParallel( (module): BaselinePretrain( (coarse_branch): ZoeDepth( (core): DepthAnythingCore( (core): DPT_DINOv2( (pretrained): DinoVisionTransformer( (patch_embed): PatchEmbed( (proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14)) (norm): Identity() ) (blocks): ModuleList( (0-11): 12 x NestedTensorBlock( (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True) (attn): MemEffAttention( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (ls1): LayerScale() (drop_path1): Identity() (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=384, out_features=1536, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=1536, out_features=384, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): Identity() ) ) (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True) (head): Identity() ) (depth_head): DPTHead( (projects): ModuleList( (0): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) (1): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1)) (2): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1)) (3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) ) (resize_layers): ModuleList( (0): ConvTranspose2d(48, 48, kernel_size=(4, 4), stride=(4, 4)) (1): ConvTranspose2d(96, 96, kernel_size=(2, 2), stride=(2, 2)) (2): Identity() (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) (scratch): Module( (layer1_rn): Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer2_rn): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer3_rn): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer4_rn): Conv2d(384, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (refinenet1): FeatureFusionBlock( (out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (refinenet2): FeatureFusionBlock( (out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (refinenet3): FeatureFusionBlock( (out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (refinenet4): FeatureFusionBlock( (out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (output_conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (output_conv2): Sequential( (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1)) (3): ReLU(inplace=True) (4): Identity() ) ) ) ) ) (conv2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) (seed_bin_regressor): SeedBinRegressorUnnormed( (_net): Sequential( (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) (seed_projector): Projector( (_net): Sequential( (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) ) ) (projectors): ModuleList( (0-3): 4 x Projector( (_net): Sequential( (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) ) ) ) (attractors): ModuleList( (0): AttractorLayerUnnormed( (_net): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) (1): AttractorLayerUnnormed( (_net): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) (2): AttractorLayerUnnormed( (_net): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) (3): AttractorLayerUnnormed( (_net): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) ) (conditional_log_binomial): ConditionalLogBinomial( (log_binomial_transform): LogBinomial() (mlp): Sequential( (0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1)) (1): GELU(approximate='none') (2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) ) (sigloss): SILogLoss() ) ) 2024/03/15 00:20:47 - patchstitcher - INFO - successfully init trainer 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.cls_token 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.pos_embed 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.mask_token 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.proj.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.proj.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.ls1.gamma 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.ls2.gamma 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.qkv.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.qkv.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.proj.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.proj.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.ls1.gamma 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.mlp.fc1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.mlp.fc1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.mlp.fc2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.mlp.fc2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.ls2.gamma 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.norm1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.norm1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.attn.qkv.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.attn.qkv.bias 2024/03/15 00:20:47 - 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patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conv2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conv2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.bias 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.weight 2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.bias 2024/03/15 00:23:05 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.218886375427246 - coarse_loss: 2.218886375427246 2024/03/15 00:24:52 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.0132031440734863 - coarse_loss: 2.0132031440734863 2024/03/15 00:26:41 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.1340489387512207 - coarse_loss: 2.1340489387512207 2024/03/15 00:28:31 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.68356192111969 - coarse_loss: 1.68356192111969 2024/03/15 00:31:46 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1240144968032837 - coarse_loss: 1.1240144968032837 2024/03/15 00:33:37 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.2552540302276611 - coarse_loss: 1.2552540302276611 2024/03/15 00:35:27 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3931670188903809 - coarse_loss: 1.3931670188903809 2024/03/15 00:37:17 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4315416812896729 - coarse_loss: 1.4315416812896729 2024/03/15 00:38:56 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ | 0.9222131 | 0.9841732 | 0.9937032 | 0.0942684 | 1.901311 | 0.0392215 | 0.1319014 | 11.5870857 | 0.3169146 | 1.4523976 | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 00:40:53 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3891286849975586 - coarse_loss: 1.3891286849975586 2024/03/15 00:42:45 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.3853542804718018 - coarse_loss: 1.3853542804718018 2024/03/15 00:44:31 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6085820198059082 - coarse_loss: 1.6085820198059082 2024/03/15 00:46:24 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2743269205093384 - coarse_loss: 1.2743269205093384 2024/03/15 00:49:33 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4644969701766968 - coarse_loss: 1.4644969701766968 2024/03/15 00:51:20 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.040415644645691 - coarse_loss: 1.040415644645691 2024/03/15 00:53:07 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2523736953735352 - coarse_loss: 1.2523736953735352 2024/03/15 00:54:57 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7893640995025635 - coarse_loss: 0.7893640995025635 2024/03/15 00:56:31 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ | 0.9466366 | 0.9857079 | 0.9944696 | 0.0784504 | 1.723246 | 0.0331783 | 0.1166779 | 10.4672395 | 0.2658952 | 1.2480133 | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 00:58:25 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8934182524681091 - coarse_loss: 0.8934182524681091 2024/03/15 01:00:10 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0365135669708252 - coarse_loss: 1.0365135669708252 2024/03/15 01:02:00 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0158889293670654 - coarse_loss: 1.0158889293670654 2024/03/15 01:03:50 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7366129159927368 - coarse_loss: 0.7366129159927368 2024/03/15 01:07:04 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4556183815002441 - coarse_loss: 1.4556183815002441 2024/03/15 01:08:51 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.093213677406311 - coarse_loss: 1.093213677406311 2024/03/15 01:10:45 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8329901099205017 - coarse_loss: 0.8329901099205017 2024/03/15 01:12:32 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8255199193954468 - coarse_loss: 0.8255199193954468 2024/03/15 01:14:05 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9492006 | 0.9876058 | 0.9947174 | 0.0765434 | 1.6623389 | 0.0336977 | 0.1157899 | 10.168448 | 0.2274059 | 1.1601292 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 01:16:01 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9320656061172485 - coarse_loss: 0.9320656061172485 2024/03/15 01:17:44 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.3558683395385742 - coarse_loss: 1.3558683395385742 2024/03/15 01:19:36 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1851251125335693 - coarse_loss: 1.1851251125335693 2024/03/15 01:21:28 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7694360613822937 - coarse_loss: 0.7694360613822937 2024/03/15 01:24:39 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9268642067909241 - coarse_loss: 0.9268642067909241 2024/03/15 01:26:28 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0070387125015259 - coarse_loss: 1.0070387125015259 2024/03/15 01:28:17 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3363308906555176 - coarse_loss: 1.3363308906555176 2024/03/15 01:30:03 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.549015998840332 - coarse_loss: 1.549015998840332 2024/03/15 01:31:38 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+ | 0.9580896 | 0.9882235 | 0.9949475 | 0.0697348 | 1.6023046 | 0.0295156 | 0.1067427 | 9.6755001 | 0.224005 | 1.1545794 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+ 2024/03/15 01:33:33 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2725939750671387 - coarse_loss: 1.2725939750671387 2024/03/15 01:35:25 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.8319188356399536 - coarse_loss: 1.8319188356399536 2024/03/15 01:37:16 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9146164655685425 - coarse_loss: 0.9146164655685425 2024/03/15 01:39:08 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9933633208274841 - coarse_loss: 0.9933633208274841 2024/03/15 01:42:21 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.531670331954956 - coarse_loss: 0.531670331954956 2024/03/15 01:44:13 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.98005211353302 - coarse_loss: 0.98005211353302 2024/03/15 01:46:08 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.562972068786621 - coarse_loss: 1.562972068786621 2024/03/15 01:48:00 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0578055381774902 - coarse_loss: 1.0578055381774902 2024/03/15 01:49:39 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+----------+-----------+--------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+----------+-----------+--------+-----------+-----------+ | 0.9573399 | 0.9884624 | 0.9949526 | 0.0727779 | 1.5619678 | 0.030998 | 0.1089102 | 9.5524 | 0.2075647 | 1.1259904 | +-----------+-----------+-----------+-----------+-----------+----------+-----------+--------+-----------+-----------+ 2024/03/15 01:51:35 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9536416530609131 - coarse_loss: 0.9536416530609131 2024/03/15 01:53:33 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.061272382736206 - coarse_loss: 1.061272382736206 2024/03/15 01:55:27 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.403846263885498 - coarse_loss: 1.403846263885498 2024/03/15 01:57:19 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6634379625320435 - coarse_loss: 0.6634379625320435 2024/03/15 02:00:39 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7105982303619385 - coarse_loss: 0.7105982303619385 2024/03/15 02:02:34 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8706010580062866 - coarse_loss: 0.8706010580062866 2024/03/15 02:04:29 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.013525366783142 - coarse_loss: 1.013525366783142 2024/03/15 02:06:17 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9357657432556152 - coarse_loss: 0.9357657432556152 2024/03/15 02:07:50 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9572283 | 0.9886936 | 0.9950871 | 0.0731142 | 1.5668887 | 0.0308406 | 0.1077591 | 9.5263897 | 0.2176418 | 1.1755943 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 02:09:47 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4000838994979858 - coarse_loss: 1.4000838994979858 2024/03/15 02:11:41 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.147301435470581 - coarse_loss: 1.147301435470581 2024/03/15 02:13:39 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9417784214019775 - coarse_loss: 0.9417784214019775 2024/03/15 02:15:36 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.898872971534729 - coarse_loss: 0.898872971534729 2024/03/15 02:18:53 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6218137741088867 - coarse_loss: 0.6218137741088867 2024/03/15 02:20:49 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9591147303581238 - coarse_loss: 0.9591147303581238 2024/03/15 02:22:42 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7330798506736755 - coarse_loss: 0.7330798506736755 2024/03/15 02:24:37 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.671249508857727 - coarse_loss: 0.671249508857727 2024/03/15 02:26:12 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9656246 | 0.9891472 | 0.9951051 | 0.0614303 | 1.5103214 | 0.0264747 | 0.1001097 | 9.4011921 | 0.1946697 | 1.0991172 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 02:28:10 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7798411846160889 - coarse_loss: 0.7798411846160889 2024/03/15 02:30:02 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9757544994354248 - coarse_loss: 0.9757544994354248 2024/03/15 02:31:49 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1485944986343384 - coarse_loss: 1.1485944986343384 2024/03/15 02:33:42 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8730670809745789 - coarse_loss: 0.8730670809745789 2024/03/15 02:36:57 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0859500169754028 - coarse_loss: 1.0859500169754028 2024/03/15 02:38:46 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9123729467391968 - coarse_loss: 0.9123729467391968 2024/03/15 02:40:36 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0700657367706299 - coarse_loss: 1.0700657367706299 2024/03/15 02:42:24 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.8393980264663696 - coarse_loss: 1.8393980264663696 2024/03/15 02:43:58 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ | 0.9678091 | 0.9892931 | 0.9952321 | 0.0607629 | 1.488932 | 0.0257705 | 0.0981663 | 9.0934609 | 0.1966839 | 1.0878515 | +-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 02:45:51 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7053128480911255 - coarse_loss: 0.7053128480911255 2024/03/15 02:47:43 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9886703491210938 - coarse_loss: 0.9886703491210938 2024/03/15 02:49:32 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.180053949356079 - coarse_loss: 1.180053949356079 2024/03/15 02:51:22 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.316230297088623 - coarse_loss: 1.316230297088623 2024/03/15 02:54:39 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7665231227874756 - coarse_loss: 0.7665231227874756 2024/03/15 02:56:30 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6590834856033325 - coarse_loss: 0.6590834856033325 2024/03/15 02:58:17 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9268083572387695 - coarse_loss: 0.9268083572387695 2024/03/15 03:00:07 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.255874752998352 - coarse_loss: 1.255874752998352 2024/03/15 03:01:42 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9691702 | 0.9894969 | 0.9952754 | 0.0559551 | 1.4743834 | 0.0240017 | 0.0943829 | 8.8561864 | 0.1819411 | 1.0395958 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 03:03:35 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6205350756645203 - coarse_loss: 0.6205350756645203 2024/03/15 03:05:29 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6529569625854492 - coarse_loss: 0.6529569625854492 2024/03/15 03:07:18 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8907508850097656 - coarse_loss: 0.8907508850097656 2024/03/15 03:09:13 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5823774337768555 - coarse_loss: 0.5823774337768555 2024/03/15 03:12:22 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3379265069961548 - coarse_loss: 1.3379265069961548 2024/03/15 03:14:13 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.615516185760498 - coarse_loss: 0.615516185760498 2024/03/15 03:16:04 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5864847302436829 - coarse_loss: 0.5864847302436829 2024/03/15 03:17:54 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9669459462165833 - coarse_loss: 0.9669459462165833 2024/03/15 03:19:29 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9697102 | 0.9895742 | 0.9953449 | 0.0539071 | 1.4497501 | 0.0229091 | 0.0925752 | 8.7784555 | 0.1802817 | 1.0580258 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 03:21:25 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9557666778564453 - coarse_loss: 0.9557666778564453 2024/03/15 03:23:15 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6958411931991577 - coarse_loss: 0.6958411931991577 2024/03/15 03:25:01 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5607629418373108 - coarse_loss: 0.5607629418373108 2024/03/15 03:26:54 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.8118071556091309 - coarse_loss: 1.8118071556091309 2024/03/15 03:30:05 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0183720588684082 - coarse_loss: 1.0183720588684082 2024/03/15 03:31:53 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0083253383636475 - coarse_loss: 1.0083253383636475 2024/03/15 03:33:45 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5852430462837219 - coarse_loss: 0.5852430462837219 2024/03/15 03:35:35 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8135958909988403 - coarse_loss: 0.8135958909988403 2024/03/15 03:37:10 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9699838 | 0.9896694 | 0.9953757 | 0.0526183 | 1.4463599 | 0.0224501 | 0.0915034 | 8.7251583 | 0.1771403 | 1.0479052 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 03:39:04 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6198912262916565 - coarse_loss: 0.6198912262916565 2024/03/15 03:40:57 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1995759010314941 - coarse_loss: 1.1995759010314941 2024/03/15 03:42:47 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.7696393728256226 - coarse_loss: 1.7696393728256226 2024/03/15 03:44:34 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4660639762878418 - coarse_loss: 1.4660639762878418 2024/03/15 03:47:48 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9167467355728149 - coarse_loss: 0.9167467355728149 2024/03/15 03:49:40 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6638955473899841 - coarse_loss: 0.6638955473899841 2024/03/15 03:51:31 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4969237446784973 - coarse_loss: 0.4969237446784973 2024/03/15 03:53:18 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5059656500816345 - coarse_loss: 0.5059656500816345 2024/03/15 03:54:52 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ | 0.9701259 | 0.9896677 | 0.9953767 | 0.0521426 | 1.4457442 | 0.0222779 | 0.0914182 | 8.7319618 | 0.1776046 | 1.046505 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ 2024/03/15 03:54:52 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict 2024/03/15 03:54:52 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :> 2024/03/15 03:54:52 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vits_u4k/coarse_pretrain