2024/03/13 15:40:06 - 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 ------------------------------------------------------------ 2024/03/13 15:40:06 - patchstitcher - INFO - Config: collect_input_args = [ 'image_lr', 'crops_image_hr', 'depth_gt', 'crop_depths', 'bboxs', 'image_hr', ] convert_syncbn = True debug = True 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', distributed=True, do_resize=False, force_keep_ar=True, freeze_midas_bn=True, gpu='NULL', img_size=[ 384, 512, ], inverse_midas=False, log_images_every=0.1, max_depth=80, max_temp=50.0, max_translation=100, memory_efficient=True, midas_model_type='DPT_BEiT_L_384', 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/ZoeDepthv1_30-Dec_16-29-4e2bc436e4e1_best.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='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', distributed=True, do_resize=False, force_keep_ar=True, freeze_midas_bn=True, gpu='NULL', img_size=[ 384, 512, ], inverse_midas=False, log_images_every=0.1, max_depth=80, max_temp=50.0, max_translation=100, memory_efficient=True, midas_model_type='DPT_BEiT_L_384', 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/ZoeDepthv1_30-Dec_16-29-4e2bc436e4e1_best.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='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=0.0002, type='AdamW', weight_decay=0.01), paramwise_cfg=dict( bypass_duplicate=True, custom_keys=dict( {'coarse_branch.core': dict(decay_mult=1.0, lr_mult=0.1)}))) 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 = [ 'pcoarse', ] 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', split='./data/u4k/splits/train.txt', transform_cfg=dict( degree=1.0, network_process_size=[ 384, 512, ], 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', split='./data/u4k/splits/val.txt', transform_cfg=dict(network_process_size=[ 384, 512, ]), type='UnrealStereo4kDataset'), num_workers=2) work_dir = './work_dir/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', distributed=True, do_resize=False, force_keep_ar=True, freeze_midas_bn=True, gpu='NULL', img_size=[ 384, 512, ], inverse_midas=False, log_images_every=0.1, max_depth=80, max_temp=50.0, max_translation=100, memory_efficient=True, midas_model_type='DPT_BEiT_L_384', 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/ZoeDepthv1_30-Dec_16-29-4e2bc436e4e1_best.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='ZoeDepth', uid='NULL', use_amp=False, use_pretrained_midas=True, use_shared_dict=False, validate_every=0.25, version_name='v1', workers=16) 2024/03/13 15:40:27 - patchstitcher - INFO - Loading deepnet from local::./work_dir/ZoeDepthv1_30-Dec_16-29-4e2bc436e4e1_best.pt 2024/03/13 15:40:28 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is 2024/03/13 15:40:28 - patchstitcher - INFO - DistributedDataParallel( (module): BaselinePretrain( (coarse_branch): ZoeDepth( (core): MidasCore( (core): DPTDepthModel( (pretrained): Module( (model): Beit( (patch_embed): PatchEmbed( (proj): Conv2d(3, 1024, kernel_size=(16, 16), stride=(16, 16)) (norm): Identity() ) (pos_drop): Dropout(p=0.0, inplace=False) (blocks): ModuleList( (0-23): 24 x Block( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=1024, out_features=3072, bias=False) (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) ) (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') (drop1): Dropout(p=0.0, inplace=False) (norm): Identity() (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (drop_path2): Identity() ) ) (norm): Identity() (fc_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (head_drop): Dropout(p=0.0, inplace=False) (head): Linear(in_features=1024, out_features=1000, bias=True) ) (act_postprocess1): Sequential( (0): ProjectReadout( (project): Sequential( (0): Linear(in_features=2048, out_features=1024, bias=True) (1): GELU(approximate='none') ) ) (1): Transpose() (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24])) (3): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1)) (4): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(4, 4)) ) (act_postprocess2): Sequential( (0): ProjectReadout( (project): Sequential( (0): Linear(in_features=2048, out_features=1024, bias=True) (1): GELU(approximate='none') ) ) (1): Transpose() (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24])) (3): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1)) (4): ConvTranspose2d(512, 512, kernel_size=(2, 2), stride=(2, 2)) ) (act_postprocess3): Sequential( (0): ProjectReadout( (project): Sequential( (0): Linear(in_features=2048, out_features=1024, bias=True) (1): GELU(approximate='none') ) ) (1): Transpose() (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24])) (3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) ) (act_postprocess4): Sequential( (0): ProjectReadout( (project): Sequential( (0): Linear(in_features=2048, out_features=1024, bias=True) (1): GELU(approximate='none') ) ) (1): Transpose() (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24])) (3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) (4): 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_custom( (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit_custom( (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_custom( (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_custom( (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit_custom( (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_custom( (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_custom( (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit_custom( (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_custom( (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_custom( (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit_custom( (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_custom( (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_conv): Sequential( (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Interpolate() (2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ReLU(inplace=True) (4): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1)) (5): ReLU(inplace=True) (6): 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() ) ) 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.cls_token:lr=2e-05 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.cls_token:weight_decay=0.01 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.cls_token:lr_mult=0.1 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.cls_token:decay_mult=1.0 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.patch_embed.proj.weight:lr=2e-05 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.patch_embed.proj.weight:weight_decay=0.01 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.patch_embed.proj.weight:lr_mult=0.1 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.patch_embed.proj.weight:decay_mult=1.0 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.patch_embed.proj.bias:lr=2e-05 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- coarse_branch.core.core.pretrained.model.patch_embed.proj.bias:weight_decay=0.01 2024/03/13 15:40:47 - patchstitcher - INFO - paramwise_options -- 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module.coarse_branch.conditional_log_binomial.mlp.0.weight 2024/03/13 15:40:48 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.bias 2024/03/13 15:40:48 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.weight 2024/03/13 15:40:48 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.bias 2024/03/13 15:44:06 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.7131173610687256 - coarse_loss: 1.7131173610687256 2024/03/13 15:46:19 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.0335652828216553 - coarse_loss: 2.0335652828216553 2024/03/13 15:48:38 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.5221185684204102 - coarse_loss: 1.5221185684204102 2024/03/13 15:50:53 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2828749418258667 - coarse_loss: 1.2828749418258667 2024/03/13 15:55:13 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9703367352485657 - coarse_loss: 0.9703367352485657 2024/03/13 15:57:28 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.2090288400650024 - coarse_loss: 1.2090288400650024 2024/03/13 15:59:42 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.168603539466858 - coarse_loss: 1.168603539466858 2024/03/13 16:01:55 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.143261432647705 - coarse_loss: 1.143261432647705 2024/03/13 16:03:57 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9627955 | 0.9889554 | 0.9953486 | 0.074003 | 1.5849035 | 0.0316879 | 0.1094913 | 9.6426039 | 0.1999298 | 1.1507299 | +-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/13 16:06:17 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1554230451583862 - coarse_loss: 1.1554230451583862 2024/03/13 16:08:25 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0465468168258667 - coarse_loss: 1.0465468168258667 2024/03/13 16:10:42 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.5575401782989502 - coarse_loss: 1.5575401782989502 2024/03/13 16:12:59 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9880474209785461 - coarse_loss: 0.9880474209785461 2024/03/13 16:16:59 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2483534812927246 - coarse_loss: 1.2483534812927246 2024/03/13 16:19:07 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.958562970161438 - coarse_loss: 0.958562970161438 2024/03/13 16:21:22 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9945743083953857 - coarse_loss: 0.9945743083953857 2024/03/13 16:23:39 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5984330773353577 - coarse_loss: 0.5984330773353577 2024/03/13 16:25:35 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+ | 0.9611621 | 0.9898382 | 0.9954542 | 0.0702681 | 1.5167718 | 0.030248 | 0.1041869 | 9.1619216 | 0.2028615 | 1.1258394 | +-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+ 2024/03/13 16:27:53 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6511057615280151 - coarse_loss: 0.6511057615280151 2024/03/13 16:30:04 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5451534390449524 - coarse_loss: 0.5451534390449524 2024/03/13 16:32:22 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7816470265388489 - coarse_loss: 0.7816470265388489 2024/03/13 16:34:35 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8089712262153625 - coarse_loss: 0.8089712262153625 2024/03/13 16:38:31 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2738938331604004 - coarse_loss: 1.2738938331604004 2024/03/13 16:40:46 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8033538460731506 - coarse_loss: 0.8033538460731506 2024/03/13 16:43:02 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.680912435054779 - coarse_loss: 0.680912435054779 2024/03/13 16:45:14 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7955048084259033 - coarse_loss: 0.7955048084259033 2024/03/13 16:47:11 - patchstitcher - INFO - Evaluation Summary: +-----------+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9713826 | 0.990082 | 0.9955904 | 0.063581 | 1.4751952 | 0.0280818 | 0.0985967 | 8.4533564 | 0.1650279 | 1.0380057 | +-----------+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/13 16:49:33 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7122501134872437 - coarse_loss: 0.7122501134872437 2024/03/13 16:51:42 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0893974304199219 - coarse_loss: 1.0893974304199219 2024/03/13 16:53:52 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0466139316558838 - coarse_loss: 1.0466139316558838 2024/03/13 16:56:05 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6300845742225647 - coarse_loss: 0.6300845742225647 2024/03/13 17:00:02 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6965449452400208 - coarse_loss: 0.6965449452400208 2024/03/13 17:02:15 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9946849346160889 - coarse_loss: 0.9946849346160889 2024/03/13 17:04:27 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2835063934326172 - coarse_loss: 1.2835063934326172 2024/03/13 17:06:43 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.366194486618042 - coarse_loss: 1.366194486618042 2024/03/13 17:08:39 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9710541 | 0.9906279 | 0.996204 | 0.0594678 | 1.4010291 | 0.0256353 | 0.0933313 | 8.4669466 | 0.1635645 | 1.0337067 | +-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/13 17:10:59 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9983483552932739 - coarse_loss: 0.9983483552932739 2024/03/13 17:13:13 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.738366961479187 - coarse_loss: 1.738366961479187 2024/03/13 17:15:25 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8853738903999329 - coarse_loss: 0.8853738903999329 2024/03/13 17:17:37 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8385846614837646 - coarse_loss: 0.8385846614837646 2024/03/13 17:21:36 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.44202721118927 - coarse_loss: 0.44202721118927 2024/03/13 17:23:51 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8443354368209839 - coarse_loss: 0.8443354368209839 2024/03/13 17:26:06 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.4652352333068848 - coarse_loss: 1.4652352333068848 2024/03/13 17:28:23 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8553734421730042 - coarse_loss: 0.8553734421730042 2024/03/13 17:30:15 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ | 0.9707531 | 0.9904648 | 0.9957918 | 0.0803878 | 1.431243 | 0.0361089 | 0.1142488 | 8.4251662 | 0.1654137 | 1.0413262 | +-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/13 17:32:34 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7248023152351379 - coarse_loss: 0.7248023152351379 2024/03/13 17:34:52 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8598499298095703 - coarse_loss: 0.8598499298095703 2024/03/13 17:37:02 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0179355144500732 - coarse_loss: 1.0179355144500732 2024/03/13 17:39:17 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5394287109375 - coarse_loss: 0.5394287109375 2024/03/13 17:43:14 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7072930932044983 - coarse_loss: 0.7072930932044983 2024/03/13 17:45:29 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8514989018440247 - coarse_loss: 0.8514989018440247 2024/03/13 17:47:41 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8754427433013916 - coarse_loss: 0.8754427433013916 2024/03/13 17:49:57 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7960880994796753 - coarse_loss: 0.7960880994796753 2024/03/13 17:51:48 - patchstitcher - INFO - Evaluation Summary: +----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+ | 0.973872 | 0.9911502 | 0.9961429 | 0.0544657 | 1.3489431 | 0.0237886 | 0.088933 | 7.8991681 | 0.1474454 | 0.9941621 | +----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+ 2024/03/13 17:54:10 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2369189262390137 - coarse_loss: 1.2369189262390137 2024/03/13 17:56:20 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.072669506072998 - coarse_loss: 1.072669506072998 2024/03/13 17:58:35 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8937158584594727 - coarse_loss: 0.8937158584594727 2024/03/13 18:00:47 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7027549147605896 - coarse_loss: 0.7027549147605896 2024/03/13 18:04:41 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5427314043045044 - coarse_loss: 0.5427314043045044 2024/03/13 18:06:59 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.738332211971283 - coarse_loss: 0.738332211971283 2024/03/13 18:09:11 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5939739942550659 - coarse_loss: 0.5939739942550659 2024/03/13 18:11:25 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5172966718673706 - coarse_loss: 0.5172966718673706 2024/03/13 18:13:17 - patchstitcher - INFO - Evaluation Summary: +----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.972616 | 0.9911106 | 0.9959747 | 0.0618082 | 1.3711957 | 0.0270541 | 0.0955989 | 8.2175053 | 0.1579217 | 1.0465419 | +----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/13 18:15:38 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5915038585662842 - coarse_loss: 0.5915038585662842 2024/03/13 18:17:51 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8162057399749756 - coarse_loss: 0.8162057399749756 2024/03/13 18:20:04 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.027492880821228 - coarse_loss: 1.027492880821228 2024/03/13 18:22:17 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7036874294281006 - coarse_loss: 0.7036874294281006 2024/03/13 18:26:12 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9338802099227905 - coarse_loss: 0.9338802099227905 2024/03/13 18:28:27 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6914787888526917 - coarse_loss: 0.6914787888526917 2024/03/13 18:30:42 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9839471578598022 - coarse_loss: 0.9839471578598022 2024/03/13 18:32:53 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.651856780052185 - coarse_loss: 1.651856780052185 2024/03/13 18:34:45 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+ | 0.9757517 | 0.9916734 | 0.9963179 | 0.0496801 | 1.2989587 | 0.021218 | 0.0832603 | 7.7315539 | 0.1475995 | 0.9686595 | +-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+ 2024/03/13 18:37:03 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5893704295158386 - coarse_loss: 0.5893704295158386 2024/03/13 18:39:20 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7406035661697388 - coarse_loss: 0.7406035661697388 2024/03/13 18:41:32 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0807702541351318 - coarse_loss: 1.0807702541351318 2024/03/13 18:43:49 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1311298608779907 - coarse_loss: 1.1311298608779907 2024/03/13 18:47:42 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6089723706245422 - coarse_loss: 0.6089723706245422 2024/03/13 18:49:56 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.49315693974494934 - coarse_loss: 0.49315693974494934 2024/03/13 18:52:07 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.765090823173523 - coarse_loss: 0.765090823173523 2024/03/13 18:54:20 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0061668157577515 - coarse_loss: 1.0061668157577515 2024/03/13 18:56:15 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+----------+ | 0.9775608 | 0.9917449 | 0.9962492 | 0.047275 | 1.2713183 | 0.0207713 | 0.0821434 | 7.4744742 | 0.1302942 | 0.914453 | +-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+----------+ 2024/03/13 18:58:37 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.47735485434532166 - coarse_loss: 0.47735485434532166 2024/03/13 19:00:47 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.477491170167923 - coarse_loss: 0.477491170167923 2024/03/13 19:03:01 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7433751821517944 - coarse_loss: 0.7433751821517944 2024/03/13 19:05:14 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4475404620170593 - coarse_loss: 0.4475404620170593 2024/03/13 19:09:10 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0900338888168335 - coarse_loss: 1.0900338888168335 2024/03/13 19:11:24 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5305348634719849 - coarse_loss: 0.5305348634719849 2024/03/13 19:13:40 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.44258806109428406 - coarse_loss: 0.44258806109428406 2024/03/13 19:15:51 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8207988739013672 - coarse_loss: 0.8207988739013672 2024/03/13 19:17:46 - patchstitcher - INFO - Evaluation Summary: +----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.977787 | 0.9918466 | 0.9964245 | 0.0419112 | 1.2307948 | 0.0181534 | 0.0777722 | 7.2845711 | 0.1274987 | 0.9021566 | +----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/13 19:20:07 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8148857355117798 - coarse_loss: 0.8148857355117798 2024/03/13 19:22:16 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5273571610450745 - coarse_loss: 0.5273571610450745 2024/03/13 19:24:29 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4554903507232666 - coarse_loss: 0.4554903507232666 2024/03/13 19:26:44 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5026299953460693 - coarse_loss: 1.5026299953460693 2024/03/13 19:30:42 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.928625226020813 - coarse_loss: 0.928625226020813 2024/03/13 19:32:55 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8586921691894531 - coarse_loss: 0.8586921691894531 2024/03/13 19:35:08 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.435793399810791 - coarse_loss: 0.435793399810791 2024/03/13 19:37:20 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6521605253219604 - coarse_loss: 0.6521605253219604 2024/03/13 19:39:13 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+ | 0.9780887 | 0.9918597 | 0.9964257 | 0.0401822 | 1.2286178 | 0.0173981 | 0.07643 | 7.2405782 | 0.1242689 | 0.8875719 | +-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+ 2024/03/13 19:41:35 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.47807303071022034 - coarse_loss: 0.47807303071022034 2024/03/13 19:43:50 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9843131303787231 - coarse_loss: 0.9843131303787231 2024/03/13 19:45:58 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.528859257698059 - coarse_loss: 1.528859257698059 2024/03/13 19:48:15 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.305454969406128 - coarse_loss: 1.305454969406128 2024/03/13 19:52:15 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7509616613388062 - coarse_loss: 0.7509616613388062 2024/03/13 19:54:26 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.48038068413734436 - coarse_loss: 0.48038068413734436 2024/03/13 19:56:45 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.3761628270149231 - coarse_loss: 0.3761628270149231 2024/03/13 19:58:57 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.3492358326911926 - coarse_loss: 0.3492358326911926 2024/03/13 20:00:48 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+---------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+---------+ | 0.9781844 | 0.9919228 | 0.9964451 | 0.0402917 | 1.221662 | 0.0174114 | 0.0764684 | 7.2314185 | 0.1251314 | 0.89066 | +-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+---------+ 2024/03/13 20:00:48 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict 2024/03/13 20:00:48 - patchstitcher - INFO - For saving space. Would you like to save coarse model several times? :> 2024/03/13 20:00:52 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/coarse_pretrain