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2024/03/15 07:29:26 - patchstitcher - INFO - |
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------------------------------------------------------------ |
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System environment: |
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sys.platform: linux |
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Python: 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0] |
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CUDA available: True |
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numpy_random_seed: 621 |
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GPU 0,1,2,3: NVIDIA A100-SXM4-80GB |
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CUDA_HOME: /sw/rl9g/cuda/11.8/rl9_binary |
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NVCC: Cuda compilation tools, release 11.8, V11.8.89 |
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GCC: gcc (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2) |
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PyTorch: 2.1.2 |
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PyTorch compiling details: PyTorch built with: |
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- GCC 9.3 |
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- C++ Version: 201703 |
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- Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications |
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- Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) |
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- OpenMP 201511 (a.k.a. OpenMP 4.5) |
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- LAPACK is enabled (usually provided by MKL) |
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- NNPACK is enabled |
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- CPU capability usage: AVX2 |
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- CUDA Runtime 11.8 |
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- 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 |
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- CuDNN 8.7 |
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- Magma 2.6.1 |
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- 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, |
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|
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TorchVision: 0.16.2 |
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OpenCV: 4.8.1 |
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MMEngine: 0.10.2 |
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|
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Runtime environment: |
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cudnn_benchmark: True |
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mp_cfg: {'mp_start_method': 'forkserver'} |
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dist_cfg: {'backend': 'nccl'} |
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seed: 621 |
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Distributed launcher: pytorch |
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Distributed training: True |
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GPU number: 4 |
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------------------------------------------------------------ |
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2024/03/15 07:29:26 - patchstitcher - INFO - Config: |
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collect_input_args = [ |
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'image_lr', |
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'crops_image_hr', |
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'depth_gt', |
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'crop_depths', |
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'bboxs', |
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'image_hr', |
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] |
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convert_syncbn = True |
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debug = False |
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env_cfg = dict( |
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cudnn_benchmark=True, |
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dist_cfg=dict(backend='nccl'), |
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mp_cfg=dict(mp_start_method='forkserver')) |
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find_unused_parameters = True |
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general_dataloader = dict( |
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batch_size=1, |
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dataset=dict( |
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dataset_name='', gt_dir=None, rgb_image_dir='', type='ImageDataset'), |
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num_workers=2) |
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launcher = 'pytorch' |
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log_name = 'patchfusion' |
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max_depth = 80 |
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min_depth = 0.001 |
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model = dict( |
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coarse_branch=dict( |
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attractor_alpha=1000, |
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attractor_gamma=2, |
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attractor_kind='mean', |
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attractor_type='inv', |
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aug=True, |
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bin_centers_type='softplus', |
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bin_embedding_dim=128, |
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clip_grad=0.1, |
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dataset='nyu', |
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depth_anything=True, |
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distributed=True, |
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do_resize=False, |
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force_keep_ar=True, |
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freeze_midas_bn=True, |
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gpu='NULL', |
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img_size=[ |
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392, |
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518, |
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], |
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inverse_midas=False, |
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log_images_every=0.1, |
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max_depth=80, |
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max_temp=50.0, |
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max_translation=100, |
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memory_efficient=True, |
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midas_model_type='vits', |
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min_depth=0.001, |
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min_temp=0.0212, |
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model='zoedepth', |
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n_attractors=[ |
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16, |
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8, |
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4, |
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1, |
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], |
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n_bins=64, |
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name='ZoeDepth', |
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notes='', |
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output_distribution='logbinomial', |
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prefetch=False, |
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pretrained_resource='local::./work_dir/DepthAnything_vits.pt', |
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print_losses=False, |
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project='ZoeDepth', |
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random_crop=False, |
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random_translate=False, |
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root='.', |
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save_dir='', |
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shared_dict='NULL', |
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tags='', |
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train_midas=True, |
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translate_prob=0.2, |
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type='DA-ZoeDepth', |
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uid='NULL', |
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use_amp=False, |
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use_pretrained_midas=True, |
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use_shared_dict=False, |
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validate_every=0.25, |
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version_name='v1', |
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workers=16), |
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fine_branch=dict( |
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attractor_alpha=1000, |
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attractor_gamma=2, |
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attractor_kind='mean', |
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attractor_type='inv', |
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aug=True, |
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bin_centers_type='softplus', |
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bin_embedding_dim=128, |
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clip_grad=0.1, |
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dataset='nyu', |
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depth_anything=True, |
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distributed=True, |
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do_resize=False, |
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force_keep_ar=True, |
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freeze_midas_bn=True, |
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gpu='NULL', |
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img_size=[ |
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392, |
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518, |
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], |
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inverse_midas=False, |
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log_images_every=0.1, |
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max_depth=80, |
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max_temp=50.0, |
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max_translation=100, |
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memory_efficient=True, |
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midas_model_type='vits', |
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min_depth=0.001, |
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min_temp=0.0212, |
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model='zoedepth', |
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n_attractors=[ |
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16, |
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8, |
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4, |
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1, |
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], |
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n_bins=64, |
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name='ZoeDepth', |
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notes='', |
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output_distribution='logbinomial', |
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prefetch=False, |
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pretrained_resource='local::./work_dir/DepthAnything_vits.pt', |
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print_losses=False, |
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project='ZoeDepth', |
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random_crop=False, |
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random_translate=False, |
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root='.', |
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save_dir='', |
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shared_dict='NULL', |
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tags='', |
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train_midas=True, |
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translate_prob=0.2, |
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type='DA-ZoeDepth', |
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uid='NULL', |
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use_amp=False, |
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use_pretrained_midas=True, |
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use_shared_dict=False, |
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validate_every=0.25, |
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version_name='v1', |
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workers=16), |
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guided_fusion=dict( |
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g2l=True, |
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in_channels=[ |
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32, |
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64, |
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64, |
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64, |
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64, |
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64, |
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], |
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n_channels=5, |
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num_patches=[ |
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203056, |
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66304, |
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16576, |
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4144, |
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1036, |
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266, |
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], |
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patch_process_shape=( |
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392, |
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518, |
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), |
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type='GuidedFusionPatchFusion'), |
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max_depth=80, |
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min_depth=0.001, |
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patch_process_shape=( |
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392, |
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518, |
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), |
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pretrain_model=[ |
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'./work_dir/depthanything_vits_u4k/coarse_pretrain/checkpoint_24.pth', |
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'./work_dir/depthanything_vits_u4k/fine_pretrain/checkpoint_24.pth', |
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], |
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sigloss=dict(type='SILogLoss'), |
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type='PatchFusion') |
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optim_wrapper = dict( |
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clip_grad=dict(max_norm=0.1, norm_type=2, type='norm'), |
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optimizer=dict(lr=0.0001, type='AdamW', weight_decay=0.001), |
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paramwise_cfg=dict(bypass_duplicate=True, custom_keys=dict())) |
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param_scheduler = dict( |
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base_momentum=0.85, |
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cycle_momentum=True, |
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div_factor=10, |
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final_div_factor=10000, |
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max_momentum=0.95, |
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pct_start=0.25, |
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three_phase=False) |
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project = 'patchfusion' |
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tags = [ |
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'patchfusion', |
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'da', |
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'vits', |
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] |
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test_in_dataloader = dict( |
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batch_size=1, |
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dataset=dict( |
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data_root='./data/u4k', |
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max_depth=80, |
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min_depth=0.001, |
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mode='infer', |
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split='./data/u4k/splits/test.txt', |
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transform_cfg=dict(network_process_size=[ |
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384, |
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512, |
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]), |
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type='UnrealStereo4kDataset'), |
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num_workers=2) |
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test_out_dataloader = dict( |
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batch_size=1, |
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dataset=dict( |
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data_root='./data/u4k', |
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max_depth=80, |
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min_depth=0.001, |
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mode='infer', |
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split='./data/u4k/splits/test_out.txt', |
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transform_cfg=dict(network_process_size=[ |
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384, |
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512, |
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]), |
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type='UnrealStereo4kDataset'), |
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num_workers=2) |
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train_cfg = dict( |
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eval_start=0, |
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log_interval=100, |
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max_epochs=16, |
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save_checkpoint_interval=16, |
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train_log_img_interval=500, |
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val_interval=2, |
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val_log_img_interval=50, |
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val_type='epoch_base') |
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train_dataloader = dict( |
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batch_size=4, |
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dataset=dict( |
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data_root='./data/u4k', |
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max_depth=80, |
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min_depth=0.001, |
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mode='train', |
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resize_mode='depth-anything', |
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split='./data/u4k/splits/train.txt', |
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transform_cfg=dict( |
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degree=1.0, network_process_size=[ |
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392, |
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518, |
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], random_crop=True), |
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type='UnrealStereo4kDataset'), |
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num_workers=4) |
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val_dataloader = dict( |
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batch_size=1, |
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dataset=dict( |
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data_root='./data/u4k', |
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max_depth=80, |
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min_depth=0.001, |
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mode='infer', |
|
resize_mode='depth-anything', |
|
split='./data/u4k/splits/val.txt', |
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transform_cfg=dict(degree=1.0, network_process_size=[ |
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392, |
|
518, |
|
]), |
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type='UnrealStereo4kDataset'), |
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num_workers=2) |
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work_dir = './work_dir/depthanything_vits_u4k/patchfusion' |
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zoe_depth_config = dict( |
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attractor_alpha=1000, |
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attractor_gamma=2, |
|
attractor_kind='mean', |
|
attractor_type='inv', |
|
aug=True, |
|
bin_centers_type='softplus', |
|
bin_embedding_dim=128, |
|
clip_grad=0.1, |
|
dataset='nyu', |
|
depth_anything=True, |
|
distributed=True, |
|
do_resize=False, |
|
force_keep_ar=True, |
|
freeze_midas_bn=True, |
|
gpu='NULL', |
|
img_size=[ |
|
392, |
|
518, |
|
], |
|
inverse_midas=False, |
|
log_images_every=0.1, |
|
max_depth=80, |
|
max_temp=50.0, |
|
max_translation=100, |
|
memory_efficient=True, |
|
midas_model_type='vits', |
|
min_depth=0.001, |
|
min_temp=0.0212, |
|
model='zoedepth', |
|
n_attractors=[ |
|
16, |
|
8, |
|
4, |
|
1, |
|
], |
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n_bins=64, |
|
name='ZoeDepth', |
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notes='', |
|
output_distribution='logbinomial', |
|
prefetch=False, |
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pretrained_resource='local::./work_dir/DepthAnything_vits.pt', |
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print_losses=False, |
|
project='ZoeDepth', |
|
random_crop=False, |
|
random_translate=False, |
|
root='.', |
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save_dir='', |
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shared_dict='NULL', |
|
tags='', |
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train_midas=True, |
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translate_prob=0.2, |
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type='DA-ZoeDepth', |
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uid='NULL', |
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use_amp=False, |
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use_pretrained_midas=True, |
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use_shared_dict=False, |
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validate_every=0.25, |
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version_name='v1', |
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workers=16) |
|
|
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2024/03/15 07:29:27 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vits.pt |
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2024/03/15 07:29:27 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'> |
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2024/03/15 07:29:27 - patchstitcher - INFO - Loading coarse_branch from ./work_dir/depthanything_vits_u4k/coarse_pretrain/checkpoint_24.pth |
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2024/03/15 07:29:27 - patchstitcher - INFO - <All keys matched successfully> |
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2024/03/15 07:29:27 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vits.pt |
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2024/03/15 07:29:27 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'> |
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2024/03/15 07:29:27 - patchstitcher - INFO - Loading fine_branch from ./work_dir/depthanything_vits_u4k/fine_pretrain/checkpoint_24.pth |
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2024/03/15 07:29:27 - patchstitcher - INFO - <All keys matched successfully> |
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2024/03/15 07:29:28 - patchstitcher - INFO - DistributedDataParallel( |
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(module): PatchFusion( |
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(coarse_branch): ZoeDepth( |
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(core): DepthAnythingCore( |
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(core): DPT_DINOv2( |
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(pretrained): DinoVisionTransformer( |
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(patch_embed): PatchEmbed( |
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(proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14)) |
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(norm): Identity() |
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) |
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(blocks): ModuleList( |
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(0-11): 12 x NestedTensorBlock( |
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(norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True) |
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(attn): MemEffAttention( |
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(qkv): Linear(in_features=384, out_features=1152, bias=True) |
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(attn_drop): Dropout(p=0.0, inplace=False) |
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(proj): Linear(in_features=384, out_features=384, bias=True) |
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(proj_drop): Dropout(p=0.0, inplace=False) |
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) |
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(ls1): LayerScale() |
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(drop_path1): Identity() |
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(norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True) |
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(mlp): Mlp( |
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(fc1): Linear(in_features=384, out_features=1536, bias=True) |
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(act): GELU(approximate='none') |
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(fc2): Linear(in_features=1536, out_features=384, bias=True) |
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(drop): Dropout(p=0.0, inplace=False) |
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) |
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(ls2): LayerScale() |
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(drop_path2): Identity() |
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) |
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) |
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(norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True) |
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(head): Identity() |
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) |
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(depth_head): DPTHead( |
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(projects): ModuleList( |
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(0): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) |
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(1): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1)) |
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(2): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) |
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) |
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(resize_layers): ModuleList( |
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(0): ConvTranspose2d(48, 48, kernel_size=(4, 4), stride=(4, 4)) |
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(1): ConvTranspose2d(96, 96, kernel_size=(2, 2), stride=(2, 2)) |
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(2): Identity() |
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(3): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) |
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) |
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(scratch): Module( |
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(layer1_rn): Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
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(layer2_rn): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
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(layer3_rn): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
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(layer4_rn): Conv2d(384, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
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(refinenet1): FeatureFusionBlock( |
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(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
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(resConfUnit1): ResidualConvUnit( |
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(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(activation): ReLU() |
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(skip_add): FloatFunctional( |
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(activation_post_process): Identity() |
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) |
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) |
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(resConfUnit2): ResidualConvUnit( |
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(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
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) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(refinenet2): FeatureFusionBlock( |
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(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(resConfUnit2): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(refinenet3): FeatureFusionBlock( |
|
(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(resConfUnit2): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(refinenet4): FeatureFusionBlock( |
|
(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(resConfUnit2): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(output_conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(output_conv2): Sequential( |
|
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): ReLU(inplace=True) |
|
(4): Identity() |
|
) |
|
) |
|
) |
|
) |
|
) |
|
(conv2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(seed_bin_regressor): SeedBinRegressorUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
(seed_projector): Projector( |
|
(_net): Sequential( |
|
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
) |
|
) |
|
(projectors): ModuleList( |
|
(0-3): 4 x Projector( |
|
(_net): Sequential( |
|
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
) |
|
) |
|
) |
|
(attractors): ModuleList( |
|
(0): AttractorLayerUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
(1): AttractorLayerUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
(2): AttractorLayerUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
(3): AttractorLayerUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
) |
|
(conditional_log_binomial): ConditionalLogBinomial( |
|
(log_binomial_transform): LogBinomial() |
|
(mlp): Sequential( |
|
(0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): GELU(approximate='none') |
|
(2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
) |
|
(fine_branch): ZoeDepth( |
|
(core): DepthAnythingCore( |
|
(core): DPT_DINOv2( |
|
(pretrained): DinoVisionTransformer( |
|
(patch_embed): PatchEmbed( |
|
(proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14)) |
|
(norm): Identity() |
|
) |
|
(blocks): ModuleList( |
|
(0-11): 12 x NestedTensorBlock( |
|
(norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True) |
|
(attn): MemEffAttention( |
|
(qkv): Linear(in_features=384, out_features=1152, bias=True) |
|
(attn_drop): Dropout(p=0.0, inplace=False) |
|
(proj): Linear(in_features=384, out_features=384, bias=True) |
|
(proj_drop): Dropout(p=0.0, inplace=False) |
|
) |
|
(ls1): LayerScale() |
|
(drop_path1): Identity() |
|
(norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True) |
|
(mlp): Mlp( |
|
(fc1): Linear(in_features=384, out_features=1536, bias=True) |
|
(act): GELU(approximate='none') |
|
(fc2): Linear(in_features=1536, out_features=384, bias=True) |
|
(drop): Dropout(p=0.0, inplace=False) |
|
) |
|
(ls2): LayerScale() |
|
(drop_path2): Identity() |
|
) |
|
) |
|
(norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True) |
|
(head): Identity() |
|
) |
|
(depth_head): DPTHead( |
|
(projects): ModuleList( |
|
(0): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1)) |
|
(2): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) |
|
) |
|
(resize_layers): ModuleList( |
|
(0): ConvTranspose2d(48, 48, kernel_size=(4, 4), stride=(4, 4)) |
|
(1): ConvTranspose2d(96, 96, kernel_size=(2, 2), stride=(2, 2)) |
|
(2): Identity() |
|
(3): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) |
|
) |
|
(scratch): Module( |
|
(layer1_rn): Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(layer2_rn): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(layer3_rn): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(layer4_rn): Conv2d(384, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(refinenet1): FeatureFusionBlock( |
|
(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(resConfUnit2): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(refinenet2): FeatureFusionBlock( |
|
(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(resConfUnit2): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(refinenet3): FeatureFusionBlock( |
|
(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(resConfUnit2): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(refinenet4): FeatureFusionBlock( |
|
(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(resConfUnit2): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(output_conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(output_conv2): Sequential( |
|
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): ReLU(inplace=True) |
|
(4): Identity() |
|
) |
|
) |
|
) |
|
) |
|
) |
|
(conv2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(seed_bin_regressor): SeedBinRegressorUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
(seed_projector): Projector( |
|
(_net): Sequential( |
|
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
) |
|
) |
|
(projectors): ModuleList( |
|
(0-3): 4 x Projector( |
|
(_net): Sequential( |
|
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
) |
|
) |
|
) |
|
(attractors): ModuleList( |
|
(0): AttractorLayerUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
(1): AttractorLayerUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
(2): AttractorLayerUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
(3): AttractorLayerUnnormed( |
|
(_net): Sequential( |
|
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
) |
|
(conditional_log_binomial): ConditionalLogBinomial( |
|
(log_binomial_transform): LogBinomial() |
|
(mlp): Sequential( |
|
(0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): GELU(approximate='none') |
|
(2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1)) |
|
(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
) |
|
(sigloss): SILogLoss() |
|
(fusion_conv_list): ModuleList( |
|
(0-4): 5 x Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(5): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
) |
|
(guided_fusion): GuidedFusionPatchFusion( |
|
(inc): DoubleConv( |
|
(double_conv): Sequential( |
|
(0): Conv2d(5, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(1): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
|
(2): ReLU(inplace=True) |
|
(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(4): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
|
(5): ReLU(inplace=True) |
|
) |
|
) |
|
(down_conv_list): ModuleList( |
|
(0): Down( |
|
(maxpool_conv): Sequential( |
|
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
|
(1): DoubleConv( |
|
(double_conv): Sequential( |
|
(0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
|
(2): ReLU(inplace=True) |
|
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(4): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
|
(5): ReLU(inplace=True) |
|
) |
|
) |
|
) |
|
) |
|
(1-4): 4 x Down( |
|
(maxpool_conv): Sequential( |
|
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
|
(1): DoubleConv( |
|
(double_conv): Sequential( |
|
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
|
(2): ReLU(inplace=True) |
|
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
|
(4): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
|
(5): ReLU(inplace=True) |
|
) |
|
) |
|
) |
|
) |
|
) |
|
(up_conv_list): ModuleList( |
|
(0-3): 4 x Upv1( |
|
(conv): DoubleConvWOBN( |
|
(double_conv): Sequential( |
|
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(3): ReLU(inplace=True) |
|
) |
|
) |
|
) |
|
(4): Upv1( |
|
(conv): DoubleConvWOBN( |
|
(double_conv): Sequential( |
|
(0): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(1): ReLU(inplace=True) |
|
(2): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(3): ReLU(inplace=True) |
|
) |
|
) |
|
) |
|
) |
|
(g2l_att): ModuleList() |
|
(g2l_list): ModuleList( |
|
(0-1): 2 x G2LFusion( |
|
(g2l_layer): G2LBasicLayer( |
|
(blocks): ModuleList( |
|
(0-3): 4 x SwinTransformerBlock( |
|
(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
|
(attn): WindowAttention( |
|
dim=64, window_size=(12, 12), num_heads=32 |
|
(qkv): Linear(in_features=64, out_features=192, bias=True) |
|
(attn_drop): Dropout(p=0.0, inplace=False) |
|
(proj): Linear(in_features=64, out_features=64, bias=True) |
|
(proj_drop): Dropout(p=0.0, inplace=False) |
|
(softmax): Softmax(dim=-1) |
|
) |
|
(drop_path): Identity() |
|
(norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
|
(mlp): Mlp( |
|
(fc1): Linear(in_features=64, out_features=256, bias=True) |
|
(act): GELU(approximate='none') |
|
(fc2): Linear(in_features=256, out_features=64, bias=True) |
|
(drop): Dropout(p=0.0, inplace=False) |
|
) |
|
) |
|
) |
|
) |
|
(g2l_layer_norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
|
(embed_proj): Conv2d(1, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
) |
|
(2-3): 2 x G2LFusion( |
|
(g2l_layer): G2LBasicLayer( |
|
(blocks): ModuleList( |
|
(0-2): 3 x SwinTransformerBlock( |
|
(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
|
(attn): WindowAttention( |
|
dim=64, window_size=(12, 12), num_heads=16 |
|
(qkv): Linear(in_features=64, out_features=192, bias=True) |
|
(attn_drop): Dropout(p=0.0, inplace=False) |
|
(proj): Linear(in_features=64, out_features=64, bias=True) |
|
(proj_drop): Dropout(p=0.0, inplace=False) |
|
(softmax): Softmax(dim=-1) |
|
) |
|
(drop_path): Identity() |
|
(norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(mlp): Mlp( |
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(fc1): Linear(in_features=64, out_features=256, bias=True) |
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(act): GELU(approximate='none') |
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(fc2): Linear(in_features=256, out_features=64, bias=True) |
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(drop): Dropout(p=0.0, inplace=False) |
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) |
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) |
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) |
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) |
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(g2l_layer_norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(embed_proj): Conv2d(1, 64, kernel_size=(1, 1), stride=(1, 1)) |
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) |
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(4): G2LFusion( |
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(g2l_layer): G2LBasicLayer( |
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(blocks): ModuleList( |
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(0-1): 2 x SwinTransformerBlock( |
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(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(attn): WindowAttention( |
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dim=64, window_size=(12, 12), num_heads=8 |
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(qkv): Linear(in_features=64, out_features=192, bias=True) |
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(attn_drop): Dropout(p=0.0, inplace=False) |
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(proj): Linear(in_features=64, out_features=64, bias=True) |
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(proj_drop): Dropout(p=0.0, inplace=False) |
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(softmax): Softmax(dim=-1) |
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) |
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(drop_path): Identity() |
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(norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(mlp): Mlp( |
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(fc1): Linear(in_features=64, out_features=256, bias=True) |
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(act): GELU(approximate='none') |
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(fc2): Linear(in_features=256, out_features=64, bias=True) |
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(drop): Dropout(p=0.0, inplace=False) |
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) |
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) |
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) |
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) |
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(g2l_layer_norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(embed_proj): Conv2d(1, 64, kernel_size=(1, 1), stride=(1, 1)) |
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) |
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(5): G2LFusion( |
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(g2l_layer): G2LBasicLayer( |
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(blocks): ModuleList( |
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(0-1): 2 x SwinTransformerBlock( |
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(norm1): LayerNorm((32,), eps=1e-05, elementwise_affine=True) |
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(attn): WindowAttention( |
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dim=32, window_size=(12, 12), num_heads=8 |
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(qkv): Linear(in_features=32, out_features=96, bias=True) |
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(attn_drop): Dropout(p=0.0, inplace=False) |
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(proj): Linear(in_features=32, out_features=32, bias=True) |
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(proj_drop): Dropout(p=0.0, inplace=False) |
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(softmax): Softmax(dim=-1) |
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) |
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(drop_path): Identity() |
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(norm2): LayerNorm((32,), eps=1e-05, elementwise_affine=True) |
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(mlp): Mlp( |
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(fc1): Linear(in_features=32, out_features=128, bias=True) |
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(act): GELU(approximate='none') |
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(fc2): Linear(in_features=128, out_features=32, bias=True) |
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(drop): Dropout(p=0.0, inplace=False) |
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) |
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) |
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) |
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) |
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(g2l_layer_norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True) |
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(embed_proj): Conv2d(1, 32, kernel_size=(1, 1), stride=(1, 1)) |
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) |
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) |
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(convs): ModuleList( |
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(0-4): 5 x DoubleConvWOBN( |
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(double_conv): Sequential( |
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(0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(3): ReLU(inplace=True) |
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) |
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) |
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(5): DoubleConvWOBN( |
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(double_conv): Sequential( |
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(0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(3): ReLU(inplace=True) |
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) |
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) |
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) |
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) |
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(seed_bin_regressor): SeedBinRegressorUnnormed( |
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(_net): Sequential( |
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(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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(seed_projector): Projector( |
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(_net): Sequential( |
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(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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) |
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) |
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(projectors): ModuleList( |
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(0-3): 4 x Projector( |
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(_net): Sequential( |
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(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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) |
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) |
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) |
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(attractors): ModuleList( |
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(0): AttractorLayerUnnormed( |
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(_net): Sequential( |
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(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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(1): AttractorLayerUnnormed( |
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(_net): Sequential( |
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(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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(2): AttractorLayerUnnormed( |
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(_net): Sequential( |
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(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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(3): AttractorLayerUnnormed( |
|
(_net): Sequential( |
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(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
|
(1): ReLU(inplace=True) |
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(2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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) |
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(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') |
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(2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
|
) |
|
) |
|
) |
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) |
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2024/03/15 07:29:34 - patchstitcher - INFO - successfully init trainer |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.0.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.0.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.1.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.1.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.2.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.2.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.3.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.3.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.4.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.5.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.fusion_conv_list.5.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.inc.double_conv.0.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.inc.double_conv.1.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.inc.double_conv.1.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.inc.double_conv.3.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.inc.double_conv.4.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.inc.double_conv.4.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.0.maxpool_conv.1.double_conv.0.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.0.maxpool_conv.1.double_conv.1.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.0.maxpool_conv.1.double_conv.1.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.0.maxpool_conv.1.double_conv.3.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.0.maxpool_conv.1.double_conv.4.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.0.maxpool_conv.1.double_conv.4.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.1.maxpool_conv.1.double_conv.0.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.1.maxpool_conv.1.double_conv.1.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.1.maxpool_conv.1.double_conv.1.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.1.maxpool_conv.1.double_conv.3.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.1.maxpool_conv.1.double_conv.4.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.1.maxpool_conv.1.double_conv.4.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.2.maxpool_conv.1.double_conv.0.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.2.maxpool_conv.1.double_conv.1.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.2.maxpool_conv.1.double_conv.1.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.2.maxpool_conv.1.double_conv.3.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.2.maxpool_conv.1.double_conv.4.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.2.maxpool_conv.1.double_conv.4.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.3.maxpool_conv.1.double_conv.0.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.3.maxpool_conv.1.double_conv.1.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.3.maxpool_conv.1.double_conv.1.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.3.maxpool_conv.1.double_conv.3.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.3.maxpool_conv.1.double_conv.4.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.3.maxpool_conv.1.double_conv.4.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.4.maxpool_conv.1.double_conv.0.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.4.maxpool_conv.1.double_conv.1.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.down_conv_list.4.maxpool_conv.1.double_conv.1.bias |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.up_conv_list.0.conv.double_conv.0.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.up_conv_list.1.conv.double_conv.0.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.0.g2l_layer.blocks.0.norm1.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.0.g2l_layer.blocks.0.attn.relative_position_bias_table |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.0.g2l_layer.blocks.0.attn.qkv.weight |
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2024/03/15 07:29:34 - patchstitcher - INFO - training param: module.guided_fusion.g2l_list.0.g2l_layer.blocks.1.attn.qkv.weight |
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2024/03/15 07:32:01 - patchstitcher - INFO - Epoch: [01/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 9.088951110839844 - sig_loss: 9.088951110839844 |
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2024/03/15 07:33:51 - patchstitcher - INFO - Epoch: [01/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 10.186747550964355 - sig_loss: 10.186747550964355 |
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2024/03/15 07:35:43 - patchstitcher - INFO - Epoch: [01/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 5.8057050704956055 - sig_loss: 5.8057050704956055 |
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2024/03/15 07:37:34 - patchstitcher - INFO - Epoch: [01/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 3.507359266281128 - sig_loss: 3.507359266281128 |
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2024/03/15 07:40:57 - patchstitcher - INFO - Epoch: [02/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 3.601012706756592 - sig_loss: 3.601012706756592 |
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2024/03/15 07:42:48 - patchstitcher - INFO - Epoch: [02/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.0724120140075684 - sig_loss: 2.0724120140075684 |
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2024/03/15 07:44:37 - patchstitcher - INFO - Epoch: [02/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.867066502571106 - sig_loss: 1.867066502571106 |
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2024/03/15 07:46:29 - patchstitcher - INFO - Epoch: [02/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2382856607437134 - sig_loss: 1.2382856607437134 |
|
2024/03/15 07:48:24 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+-----------+----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+-----------+----------+ |
|
| 0.8245014 | 0.9536317 | 0.978513 | 0.1458762 | 3.0784135 | 0.0653375 | 0.2124487 | 18.7477429 | 0.5830149 | 2.158933 | |
|
+-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+-----------+----------+ |
|
2024/03/15 07:50:21 - patchstitcher - INFO - Epoch: [03/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.246709108352661 - sig_loss: 2.246709108352661 |
|
2024/03/15 07:52:12 - patchstitcher - INFO - Epoch: [03/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.2398436069488525 - sig_loss: 1.2398436069488525 |
|
2024/03/15 07:54:07 - patchstitcher - INFO - Epoch: [03/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1895811557769775 - sig_loss: 1.1895811557769775 |
|
2024/03/15 07:56:01 - patchstitcher - INFO - Epoch: [03/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1725659370422363 - sig_loss: 1.1725659370422363 |
|
2024/03/15 07:59:17 - patchstitcher - INFO - Epoch: [04/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5296531915664673 - sig_loss: 1.5296531915664673 |
|
2024/03/15 08:01:06 - patchstitcher - INFO - Epoch: [04/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6554194688796997 - sig_loss: 0.6554194688796997 |
|
2024/03/15 08:02:58 - patchstitcher - INFO - Epoch: [04/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7219275832176208 - sig_loss: 0.7219275832176208 |
|
2024/03/15 08:04:46 - patchstitcher - INFO - Epoch: [04/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5855780839920044 - sig_loss: 0.5855780839920044 |
|
2024/03/15 08:06:32 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ |
|
| 0.9364886 | 0.9916468 | 0.9968784 | 0.1269969 | 1.5382671 | 0.0514243 | 0.1438391 | 9.4389619 | 0.1894025 | 1.258988 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ |
|
2024/03/15 08:08:29 - patchstitcher - INFO - Epoch: [05/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7347273230552673 - sig_loss: 0.7347273230552673 |
|
2024/03/15 08:10:22 - patchstitcher - INFO - Epoch: [05/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5039417147636414 - sig_loss: 0.5039417147636414 |
|
2024/03/15 08:12:11 - patchstitcher - INFO - Epoch: [05/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7847908735275269 - sig_loss: 0.7847908735275269 |
|
2024/03/15 08:14:03 - patchstitcher - INFO - Epoch: [05/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.3898926079273224 - sig_loss: 0.3898926079273224 |
|
2024/03/15 08:17:23 - patchstitcher - INFO - Epoch: [06/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0257811546325684 - sig_loss: 1.0257811546325684 |
|
2024/03/15 08:19:11 - patchstitcher - INFO - Epoch: [06/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8458202481269836 - sig_loss: 0.8458202481269836 |
|
2024/03/15 08:21:03 - patchstitcher - INFO - Epoch: [06/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.567626416683197 - sig_loss: 0.567626416683197 |
|
2024/03/15 08:22:55 - patchstitcher - INFO - Epoch: [06/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5519287586212158 - sig_loss: 0.5519287586212158 |
|
2024/03/15 08:24:41 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9724073 | 0.9928337 | 0.9971162 | 0.0579427 | 1.3093408 | 0.0253313 | 0.0904551 | 8.3986621 | 0.1335955 | 1.0827504 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 08:26:36 - patchstitcher - INFO - Epoch: [07/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.34281089901924133 - sig_loss: 0.34281089901924133 |
|
2024/03/15 08:28:27 - patchstitcher - INFO - Epoch: [07/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1112661361694336 - sig_loss: 1.1112661361694336 |
|
2024/03/15 08:30:21 - patchstitcher - INFO - Epoch: [07/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.302087426185608 - sig_loss: 1.302087426185608 |
|
2024/03/15 08:32:09 - patchstitcher - INFO - Epoch: [07/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8661139607429504 - sig_loss: 0.8661139607429504 |
|
2024/03/15 08:35:25 - patchstitcher - INFO - Epoch: [08/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.27660903334617615 - sig_loss: 0.27660903334617615 |
|
2024/03/15 08:37:16 - patchstitcher - INFO - Epoch: [08/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.283405601978302 - sig_loss: 0.283405601978302 |
|
2024/03/15 08:39:05 - patchstitcher - INFO - Epoch: [08/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.42920011281967163 - sig_loss: 0.42920011281967163 |
|
2024/03/15 08:40:53 - patchstitcher - INFO - Epoch: [08/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.6047943830490112 - sig_loss: 1.6047943830490112 |
|
2024/03/15 08:42:38 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9769595 | 0.9933058 | 0.9971508 | 0.0515932 | 1.2690557 | 0.0225415 | 0.0835451 | 7.6818873 | 0.1229265 | 0.9774805 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 08:44:34 - patchstitcher - INFO - Epoch: [09/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4381069540977478 - sig_loss: 0.4381069540977478 |
|
2024/03/15 08:46:25 - patchstitcher - INFO - Epoch: [09/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5580072999000549 - sig_loss: 0.5580072999000549 |
|
2024/03/15 08:48:17 - patchstitcher - INFO - Epoch: [09/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5283715724945068 - sig_loss: 0.5283715724945068 |
|
2024/03/15 08:50:07 - patchstitcher - INFO - Epoch: [09/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4349801242351532 - sig_loss: 0.4349801242351532 |
|
2024/03/15 08:53:21 - patchstitcher - INFO - Epoch: [10/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.31138694286346436 - sig_loss: 0.31138694286346436 |
|
2024/03/15 08:55:13 - patchstitcher - INFO - Epoch: [10/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.4804566204547882 - sig_loss: 0.4804566204547882 |
|
2024/03/15 08:57:04 - patchstitcher - INFO - Epoch: [10/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.249344825744629 - sig_loss: 1.249344825744629 |
|
2024/03/15 08:58:54 - patchstitcher - INFO - Epoch: [10/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.709969699382782 - sig_loss: 0.709969699382782 |
|
2024/03/15 09:00:40 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9774357 | 0.9933226 | 0.9971929 | 0.0527099 | 1.2487739 | 0.0231302 | 0.0840302 | 7.6349905 | 0.1197366 | 0.9869539 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 09:02:42 - patchstitcher - INFO - Epoch: [11/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0747475624084473 - sig_loss: 1.0747475624084473 |
|
2024/03/15 09:04:31 - patchstitcher - INFO - Epoch: [11/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8598241806030273 - sig_loss: 0.8598241806030273 |
|
2024/03/15 09:06:21 - patchstitcher - INFO - Epoch: [11/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2119524478912354 - sig_loss: 1.2119524478912354 |
|
2024/03/15 09:08:09 - patchstitcher - INFO - Epoch: [11/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6661930084228516 - sig_loss: 0.6661930084228516 |
|
2024/03/15 09:11:28 - patchstitcher - INFO - Epoch: [12/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9896361231803894 - sig_loss: 0.9896361231803894 |
|
2024/03/15 09:13:23 - patchstitcher - INFO - Epoch: [12/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5676484107971191 - sig_loss: 0.5676484107971191 |
|
2024/03/15 09:15:13 - patchstitcher - INFO - Epoch: [12/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.756153404712677 - sig_loss: 0.756153404712677 |
|
2024/03/15 09:17:04 - patchstitcher - INFO - Epoch: [12/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7506179213523865 - sig_loss: 0.7506179213523865 |
|
2024/03/15 09:18:50 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+ |
|
| 0.9779995 | 0.9934567 | 0.997205 | 0.0477499 | 1.2371548 | 0.020665 | 0.0797043 | 7.4628689 | 0.1182562 | 0.9688643 | |
|
+-----------+-----------+----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 09:20:47 - patchstitcher - INFO - Epoch: [13/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.8093539476394653 - sig_loss: 1.8093539476394653 |
|
2024/03/15 09:22:38 - patchstitcher - INFO - Epoch: [13/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5200282335281372 - sig_loss: 0.5200282335281372 |
|
2024/03/15 09:24:29 - patchstitcher - INFO - Epoch: [13/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3394041061401367 - sig_loss: 1.3394041061401367 |
|
2024/03/15 09:26:19 - patchstitcher - INFO - Epoch: [13/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.21983280777931213 - sig_loss: 0.21983280777931213 |
|
2024/03/15 09:29:33 - patchstitcher - INFO - Epoch: [14/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.2869156002998352 - sig_loss: 0.2869156002998352 |
|
2024/03/15 09:31:27 - patchstitcher - INFO - Epoch: [14/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.4588187336921692 - sig_loss: 0.4588187336921692 |
|
2024/03/15 09:33:15 - patchstitcher - INFO - Epoch: [14/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.29478126764297485 - sig_loss: 0.29478126764297485 |
|
2024/03/15 09:35:06 - patchstitcher - INFO - Epoch: [14/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.40359240770339966 - sig_loss: 0.40359240770339966 |
|
2024/03/15 09:36:52 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9782872 | 0.9936157 | 0.9972659 | 0.0477206 | 1.2295696 | 0.0205395 | 0.0790254 | 7.4115142 | 0.1175734 | 0.9644176 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 09:38:46 - patchstitcher - INFO - Epoch: [15/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.33154720067977905 - sig_loss: 0.33154720067977905 |
|
2024/03/15 09:40:36 - patchstitcher - INFO - Epoch: [15/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8081605434417725 - sig_loss: 0.8081605434417725 |
|
2024/03/15 09:42:25 - patchstitcher - INFO - Epoch: [15/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1970497369766235 - sig_loss: 1.1970497369766235 |
|
2024/03/15 09:44:19 - patchstitcher - INFO - Epoch: [15/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.2877902388572693 - sig_loss: 0.2877902388572693 |
|
2024/03/15 09:47:39 - patchstitcher - INFO - Epoch: [16/16] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7490944266319275 - sig_loss: 0.7490944266319275 |
|
2024/03/15 09:49:27 - patchstitcher - INFO - Epoch: [16/16] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.496704638004303 - sig_loss: 0.496704638004303 |
|
2024/03/15 09:51:18 - patchstitcher - INFO - Epoch: [16/16] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5354593992233276 - sig_loss: 0.5354593992233276 |
|
2024/03/15 09:53:04 - patchstitcher - INFO - Epoch: [16/16] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9501898884773254 - sig_loss: 0.9501898884773254 |
|
2024/03/15 09:54:50 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+ |
|
| 0.9783667 | 0.9935431 | 0.997246 | 0.0478827 | 1.2350241 | 0.0206808 | 0.0793662 | 7.402013 | 0.1158019 | 0.9521085 | |
|
+-----------+-----------+----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+ |
|
2024/03/15 09:54:50 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict |
|
2024/03/15 09:54:50 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :> |
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2024/03/15 09:54:50 - patchstitcher - INFO - save checkpoint_16.pth at ./work_dir/depthanything_vits_u4k/patchfusion |
|
|