diff --git "a/depthanything_vitl_u4k/coarse_pretrain/20240315_102957.log" "b/depthanything_vitl_u4k/coarse_pretrain/20240315_102957.log" new file mode 100644--- /dev/null +++ "b/depthanything_vitl_u4k/coarse_pretrain/20240315_102957.log" @@ -0,0 +1,1191 @@ +2024/03/15 10:30:08 - 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 10:30:08 - 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='vitl', + 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_vitl.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='vitl', + 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_vitl.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', + 'vitl', +] +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_vitl_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='vitl', + 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_vitl.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 10:30:12 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vitl.pt +2024/03/15 10:30:13 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is +2024/03/15 10:30:13 - patchstitcher - INFO - DistributedDataParallel( + (module): BaselinePretrain( + (coarse_branch): ZoeDepth( + (core): DepthAnythingCore( + (core): DPT_DINOv2( + (pretrained): DinoVisionTransformer( + (patch_embed): PatchEmbed( + (proj): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14)) + (norm): Identity() + ) + (blocks): ModuleList( + (0-23): 24 x NestedTensorBlock( + (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) + (attn): MemEffAttention( + (qkv): Linear(in_features=1024, out_features=3072, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1024, out_features=1024, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + ) + (ls1): LayerScale() + (drop_path1): Identity() + (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=1024, out_features=4096, bias=True) + (act): GELU(approximate='none') + (fc2): Linear(in_features=4096, out_features=1024, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ls2): LayerScale() + (drop_path2): Identity() + ) + ) + (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) + (head): Identity() + ) + (depth_head): DPTHead( + (projects): ModuleList( + (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1)) + (1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1)) + (2-3): 2 x Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) + ) + (resize_layers): ModuleList( + (0): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(4, 4)) + (1): ConvTranspose2d(512, 512, kernel_size=(2, 2), stride=(2, 2)) + (2): Identity() + (3): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + ) + (scratch): Module( + (layer1_rn): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (layer2_rn): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (layer3_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (layer4_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (refinenet1): FeatureFusionBlock( + (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (resConfUnit1): ResidualConvUnit( + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (activation): ReLU() + (skip_add): FloatFunctional( + (activation_post_process): Identity() + ) + ) + (resConfUnit2): ResidualConvUnit( + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (conv2): Conv2d(256, 256, 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(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (resConfUnit1): ResidualConvUnit( + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (activation): ReLU() + (skip_add): FloatFunctional( + (activation_post_process): Identity() + ) + ) + (resConfUnit2): ResidualConvUnit( + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (conv2): Conv2d(256, 256, 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(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (resConfUnit1): ResidualConvUnit( + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (activation): ReLU() + (skip_add): FloatFunctional( + (activation_post_process): Identity() + ) + ) + (resConfUnit2): ResidualConvUnit( + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (conv2): Conv2d(256, 256, 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(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (resConfUnit1): ResidualConvUnit( + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (activation): ReLU() + (skip_add): FloatFunctional( + (activation_post_process): Identity() + ) + ) + (resConfUnit2): ResidualConvUnit( + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (conv2): Conv2d(256, 256, 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(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (output_conv2): Sequential( + (0): Conv2d(128, 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(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (seed_bin_regressor): SeedBinRegressorUnnormed( + (_net): Sequential( + (0): Conv2d(256, 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(256, 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(256, 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 10:30:19 - patchstitcher - INFO - successfully init trainer +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.cls_token +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.pos_embed +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.mask_token +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.weight +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.bias +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.weight +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.bias +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.weight +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.bias +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.proj.weight +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.proj.bias +2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.ls1.gamma +2024/03/15 10:30:19 - patchstitcher - INFO - 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param: module.coarse_branch.core.core.depth_head.projects.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.projects.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.projects.3.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.projects.3.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.3.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.3.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer1_rn.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer2_rn.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer3_rn.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer4_rn.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.out_conv.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.out_conv.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conv2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conv2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.bias +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.weight +2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.bias +2024/03/15 10:33:18 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3691939115524292 - coarse_loss: 1.3691939115524292 +2024/03/15 10:35:12 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.619562029838562 - coarse_loss: 1.619562029838562 +2024/03/15 10:37:06 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.4217467308044434 - coarse_loss: 1.4217467308044434 +2024/03/15 10:38:59 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.368167519569397 - coarse_loss: 1.368167519569397 +2024/03/15 10:42:42 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7346311211585999 - coarse_loss: 0.7346311211585999 +2024/03/15 10:44:36 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.020386815071106 - coarse_loss: 1.020386815071106 +2024/03/15 10:46:30 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2180640697479248 - coarse_loss: 1.2180640697479248 +2024/03/15 10:48:23 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1110318899154663 - coarse_loss: 1.1110318899154663 +2024/03/15 10:50:14 - patchstitcher - INFO - Evaluation Summary: ++-----------+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+ +| 0.9598735 | 0.9891855 | 0.9951727 | 0.072453 | 1.587027 | 0.0309977 | 0.1077142 | 9.6015839 | 0.2148946 | 1.2024959 | ++-----------+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+ +2024/03/15 10:52:09 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.123214602470398 - coarse_loss: 1.123214602470398 +2024/03/15 10:54:04 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0366013050079346 - coarse_loss: 1.0366013050079346 +2024/03/15 10:55:52 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3733680248260498 - coarse_loss: 1.3733680248260498 +2024/03/15 10:57:44 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9410479068756104 - coarse_loss: 0.9410479068756104 +2024/03/15 11:01:02 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.338404893875122 - coarse_loss: 1.338404893875122 +2024/03/15 11:02:55 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8547713756561279 - coarse_loss: 0.8547713756561279 +2024/03/15 11:04:47 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0474542379379272 - coarse_loss: 1.0474542379379272 +2024/03/15 11:06:40 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7824715971946716 - coarse_loss: 0.7824715971946716 +2024/03/15 11:08:17 - patchstitcher - INFO - Evaluation Summary: ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ +| 0.9550269 | 0.9895827 | 0.9956135 | 0.0805194 | 1.5514586 | 0.0332511 | 0.1088383 | 9.1410583 | 0.2326348 | 1.2689711 | ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ +2024/03/15 11:10:14 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5382569432258606 - coarse_loss: 0.5382569432258606 +2024/03/15 11:12:06 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5633417367935181 - coarse_loss: 0.5633417367935181 +2024/03/15 11:13:57 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5049566030502319 - coarse_loss: 0.5049566030502319 +2024/03/15 11:15:48 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.69290691614151 - coarse_loss: 0.69290691614151 +2024/03/15 11:19:09 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2545151710510254 - coarse_loss: 1.2545151710510254 +2024/03/15 11:20:59 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.805565357208252 - coarse_loss: 0.805565357208252 +2024/03/15 11:22:49 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6542041897773743 - coarse_loss: 0.6542041897773743 +2024/03/15 11:24:41 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6170626282691956 - coarse_loss: 0.6170626282691956 +2024/03/15 11:26:22 - patchstitcher - INFO - Evaluation Summary: ++-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ +| 0.9726048 | 0.990251 | 0.9956528 | 0.0649057 | 1.4030008 | 0.0282428 | 0.0986983 | 8.6367456 | 0.1673493 | 1.0348499 | ++-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ +2024/03/15 11:28:17 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7065086960792542 - coarse_loss: 0.7065086960792542 +2024/03/15 11:30:12 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0559895038604736 - coarse_loss: 1.0559895038604736 +2024/03/15 11:32:01 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0681591033935547 - coarse_loss: 1.0681591033935547 +2024/03/15 11:33:51 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5747287273406982 - coarse_loss: 0.5747287273406982 +2024/03/15 11:37:07 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6418678760528564 - coarse_loss: 0.6418678760528564 +2024/03/15 11:38:58 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8234983086585999 - coarse_loss: 0.8234983086585999 +2024/03/15 11:40:44 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.230547308921814 - coarse_loss: 1.230547308921814 +2024/03/15 11:42:33 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3322598934173584 - coarse_loss: 1.3322598934173584 +2024/03/15 11:44:10 - patchstitcher - INFO - Evaluation Summary: ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ +| 0.9731681 | 0.9907731 | 0.9960122 | 0.0561289 | 1.3575984 | 0.0240152 | 0.0906736 | 8.3633755 | 0.1553763 | 1.001603 | ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ +2024/03/15 11:46:07 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0721046924591064 - coarse_loss: 1.0721046924591064 +2024/03/15 11:47:57 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.740877389907837 - coarse_loss: 1.740877389907837 +2024/03/15 11:49:43 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7495399713516235 - coarse_loss: 0.7495399713516235 +2024/03/15 11:51:33 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9519928097724915 - coarse_loss: 0.9519928097724915 +2024/03/15 11:54:47 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4422484040260315 - coarse_loss: 0.4422484040260315 +2024/03/15 11:56:39 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8411234617233276 - coarse_loss: 0.8411234617233276 +2024/03/15 11:58:30 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.4021077156066895 - coarse_loss: 1.4021077156066895 +2024/03/15 12:00:23 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.925296425819397 - coarse_loss: 0.925296425819397 +2024/03/15 12:01:59 - patchstitcher - INFO - Evaluation Summary: ++-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ +| 0.9740256 | 0.9909777 | 0.9960672 | 0.0548506 | 1.342812 | 0.0235422 | 0.0895881 | 7.8919773 | 0.1552812 | 1.0021132 | ++-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ +2024/03/15 12:03:58 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.770862340927124 - coarse_loss: 0.770862340927124 +2024/03/15 12:05:47 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8211466073989868 - coarse_loss: 0.8211466073989868 +2024/03/15 12:07:39 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.038414716720581 - coarse_loss: 1.038414716720581 +2024/03/15 12:09:30 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4909307062625885 - coarse_loss: 0.4909307062625885 +2024/03/15 12:12:45 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6742696762084961 - coarse_loss: 0.6742696762084961 +2024/03/15 12:14:37 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7697466015815735 - coarse_loss: 0.7697466015815735 +2024/03/15 12:16:25 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8855838179588318 - coarse_loss: 0.8855838179588318 +2024/03/15 12:18:20 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.735936164855957 - coarse_loss: 0.735936164855957 +2024/03/15 12:19:58 - patchstitcher - INFO - Evaluation Summary: ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+ +| 0.9751907 | 0.9907739 | 0.9959293 | 0.0535933 | 1.3472066 | 0.0232206 | 0.0892041 | 7.9883518 | 0.1477947 | 0.97325 | ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+ +2024/03/15 12:21:57 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1638290882110596 - coarse_loss: 1.1638290882110596 +2024/03/15 12:23:45 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0216888189315796 - coarse_loss: 1.0216888189315796 +2024/03/15 12:25:40 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8358562588691711 - coarse_loss: 0.8358562588691711 +2024/03/15 12:27:34 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7175360918045044 - coarse_loss: 0.7175360918045044 +2024/03/15 12:30:52 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5334227085113525 - coarse_loss: 0.5334227085113525 +2024/03/15 12:32:41 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7218840718269348 - coarse_loss: 0.7218840718269348 +2024/03/15 12:34:35 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5779137015342712 - coarse_loss: 0.5779137015342712 +2024/03/15 12:36:26 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4988095760345459 - coarse_loss: 0.4988095760345459 +2024/03/15 12:38:04 - patchstitcher - INFO - Evaluation Summary: ++-----------+-----------+-----------+-----------+-----------+-----------+----------+----------+-----------+-----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+-----------+-----------+-----------+-----------+-----------+----------+----------+-----------+-----------+ +| 0.9751327 | 0.9912391 | 0.9962269 | 0.0510332 | 1.3050884 | 0.0220181 | 0.085528 | 7.621976 | 0.1437526 | 0.9718316 | ++-----------+-----------+-----------+-----------+-----------+-----------+----------+----------+-----------+-----------+ +2024/03/15 12:40:00 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.563976526260376 - coarse_loss: 0.563976526260376 +2024/03/15 12:41:52 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8443318605422974 - coarse_loss: 0.8443318605422974 +2024/03/15 12:43:42 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0198791027069092 - coarse_loss: 1.0198791027069092 +2024/03/15 12:45:33 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6089605093002319 - coarse_loss: 0.6089605093002319 +2024/03/15 12:48:51 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9242361187934875 - coarse_loss: 0.9242361187934875 +2024/03/15 12:50:38 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8595165014266968 - coarse_loss: 0.8595165014266968 +2024/03/15 12:52:29 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9421486854553223 - coarse_loss: 0.9421486854553223 +2024/03/15 12:54:21 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.6723458766937256 - coarse_loss: 1.6723458766937256 +2024/03/15 12:55:59 - patchstitcher - INFO - Evaluation Summary: ++-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+ +| 0.9746925 | 0.9912283 | 0.9961616 | 0.050426 | 1.2974334 | 0.0215315 | 0.0845853 | 7.6158042 | 0.1422237 | 0.9475862 | ++-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+ +2024/03/15 12:57:56 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5518297553062439 - coarse_loss: 0.5518297553062439 +2024/03/15 12:59:46 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7465678453445435 - coarse_loss: 0.7465678453445435 +2024/03/15 13:01:37 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0676344633102417 - coarse_loss: 1.0676344633102417 +2024/03/15 13:03:28 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1690986156463623 - coarse_loss: 1.1690986156463623 +2024/03/15 13:06:46 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6604115962982178 - coarse_loss: 0.6604115962982178 +2024/03/15 13:08:37 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5035145282745361 - coarse_loss: 0.5035145282745361 +2024/03/15 13:10:28 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7860687375068665 - coarse_loss: 0.7860687375068665 +2024/03/15 13:12:20 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9929344654083252 - coarse_loss: 0.9929344654083252 +2024/03/15 13:13:56 - patchstitcher - INFO - Evaluation Summary: ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+ +| 0.9768352 | 0.9912757 | 0.9961631 | 0.0450889 | 1.3024287 | 0.0197906 | 0.0819427 | 7.473267 | 0.1371208 | 0.9184296 | ++-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+ +2024/03/15 13:15:51 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4816737771034241 - coarse_loss: 0.4816737771034241 +2024/03/15 13:17:44 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5091171264648438 - coarse_loss: 0.5091171264648438 +2024/03/15 13:19:37 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7248642444610596 - coarse_loss: 0.7248642444610596 +2024/03/15 13:21:29 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5194234251976013 - coarse_loss: 0.5194234251976013 +2024/03/15 13:24:49 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0822392702102661 - coarse_loss: 1.0822392702102661 +2024/03/15 13:26:42 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5438417196273804 - coarse_loss: 0.5438417196273804 +2024/03/15 13:28:34 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.44422096014022827 - coarse_loss: 0.44422096014022827 +2024/03/15 13:30:24 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8150679469108582 - coarse_loss: 0.8150679469108582 +2024/03/15 13:32:03 - patchstitcher - INFO - Evaluation Summary: ++----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ +| 0.977051 | 0.9914994 | 0.9962904 | 0.0436792 | 1.2543163 | 0.0187145 | 0.0793818 | 7.3642922 | 0.1357556 | 0.9069338 | ++----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ +2024/03/15 13:34:01 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8032045364379883 - coarse_loss: 0.8032045364379883 +2024/03/15 13:35:50 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5563585758209229 - coarse_loss: 0.5563585758209229 +2024/03/15 13:37:44 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4848824143409729 - coarse_loss: 0.4848824143409729 +2024/03/15 13:39:35 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5596399307250977 - coarse_loss: 1.5596399307250977 +2024/03/15 13:42:55 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9384262561798096 - coarse_loss: 0.9384262561798096 +2024/03/15 13:44:46 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8976131081581116 - coarse_loss: 0.8976131081581116 +2024/03/15 13:46:36 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.43993157148361206 - coarse_loss: 0.43993157148361206 +2024/03/15 13:48:30 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6713937520980835 - coarse_loss: 0.6713937520980835 +2024/03/15 13:50:09 - patchstitcher - INFO - Evaluation Summary: ++-----------+-----------+-----------+----------+-----------+-----------+----------+-----------+-----------+----------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++-----------+-----------+-----------+----------+-----------+-----------+----------+-----------+-----------+----------+ +| 0.9772672 | 0.9916211 | 0.9963902 | 0.042066 | 1.2453072 | 0.0180818 | 0.078149 | 7.2418006 | 0.1324319 | 0.911489 | ++-----------+-----------+-----------+----------+-----------+-----------+----------+-----------+-----------+----------+ +2024/03/15 13:52:06 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.503566563129425 - coarse_loss: 0.503566563129425 +2024/03/15 13:53:55 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0374677181243896 - coarse_loss: 1.0374677181243896 +2024/03/15 13:55:45 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.5681146383285522 - coarse_loss: 1.5681146383285522 +2024/03/15 13:57:38 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3371570110321045 - coarse_loss: 1.3371570110321045 +2024/03/15 14:00:59 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8207178115844727 - coarse_loss: 0.8207178115844727 +2024/03/15 14:02:50 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5041927695274353 - coarse_loss: 0.5041927695274353 +2024/03/15 14:04:43 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.3949466049671173 - coarse_loss: 0.3949466049671173 +2024/03/15 14:06:32 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.37768083810806274 - coarse_loss: 0.37768083810806274 +2024/03/15 14:08:10 - patchstitcher - INFO - Evaluation Summary: ++----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+--------+ +| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | ++----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+--------+ +| 0.977363 | 0.9916447 | 0.9963651 | 0.0417068 | 1.2436186 | 0.0179399 | 0.0778791 | 7.2500755 | 0.1322989 | 0.9124 | ++----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+--------+ +2024/03/15 14:08:10 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict +2024/03/15 14:08:10 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :> +2024/03/15 14:08:13 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vitl_u4k/coarse_pretrain