| 2023-03-03 13:59:33,312 - mmseg - INFO - Multi-processing start method is `None` | |
| 2023-03-03 13:59:33,327 - mmseg - INFO - OpenCV num_threads is `128 | |
| 2023-03-03 13:59:33,327 - mmseg - INFO - OMP num threads is 1 | |
| 2023-03-03 13:59:33,410 - mmseg - INFO - Environment info: | |
| ------------------------------------------------------------ | |
| sys.platform: linux | |
| Python: 3.7.16 (default, Jan 17 2023, 22:20:44) [GCC 11.2.0] | |
| CUDA available: True | |
| GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB | |
| CUDA_HOME: /mnt/petrelfs/laizeqiang/miniconda3/envs/torch | |
| NVCC: Cuda compilation tools, release 11.6, V11.6.124 | |
| GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) | |
| PyTorch: 1.13.1 | |
| PyTorch compiling details: PyTorch built with: | |
| - GCC 9.3 | |
| - C++ Version: 201402 | |
| - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications | |
| - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) | |
| - OpenMP 201511 (a.k.a. OpenMP 4.5) | |
| - LAPACK is enabled (usually provided by MKL) | |
| - NNPACK is enabled | |
| - CPU capability usage: AVX2 | |
| - CUDA Runtime 11.6 | |
| - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-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=compute_37 | |
| - CuDNN 8.3.2 (built against CUDA 11.5) | |
| - Magma 2.6.1 | |
| - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -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_VERSION=1.13.1, 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: 0.14.1 | |
| OpenCV: 4.7.0 | |
| MMCV: 1.7.1 | |
| MMCV Compiler: GCC 9.3 | |
| MMCV CUDA Compiler: 11.6 | |
| MMSegmentation: 0.30.0+ad87029 | |
| ------------------------------------------------------------ | |
| 2023-03-03 13:59:33,411 - mmseg - INFO - Distributed training: True | |
| 2023-03-03 13:59:34,043 - mmseg - INFO - Config: | |
| norm_cfg = dict(type='SyncBN', requires_grad=True) | |
| checkpoint = 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth' | |
| model = dict( | |
| type='EncoderDecoderFreeze', | |
| freeze_parameters=['backbone', 'decode_head'], | |
| pretrained= | |
| 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth', | |
| backbone=dict( | |
| type='MixVisionTransformerCustomInitWeights', | |
| in_channels=3, | |
| embed_dims=64, | |
| num_stages=4, | |
| num_layers=[3, 4, 6, 3], | |
| num_heads=[1, 2, 5, 8], | |
| patch_sizes=[7, 3, 3, 3], | |
| sr_ratios=[8, 4, 2, 1], | |
| out_indices=(0, 1, 2, 3), | |
| mlp_ratio=4, | |
| qkv_bias=True, | |
| drop_rate=0.0, | |
| attn_drop_rate=0.0, | |
| drop_path_rate=0.1), | |
| decode_head=dict( | |
| type='SegformerHeadUnetFCHeadSingleStep', | |
| pretrained= | |
| 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth', | |
| dim=128, | |
| out_dim=256, | |
| unet_channels=272, | |
| dim_mults=[1, 1, 1], | |
| cat_embedding_dim=16, | |
| in_channels=[64, 128, 320, 512], | |
| in_index=[0, 1, 2, 3], | |
| channels=256, | |
| dropout_ratio=0.1, | |
| num_classes=151, | |
| norm_cfg=dict(type='SyncBN', requires_grad=True), | |
| align_corners=False, | |
| ignore_index=0, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | |
| train_cfg=dict(), | |
| test_cfg=dict(mode='whole')) | |
| dataset_type = 'ADE20K151Dataset' | |
| data_root = 'data/ade/ADEChallengeData2016' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| crop_size = (512, 512) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', reduce_zero_label=False), | |
| dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), | |
| dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=0), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_semantic_seg']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(2048, 512), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ] | |
| data = dict( | |
| samples_per_gpu=4, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='ADE20K151Dataset', | |
| data_root='data/ade/ADEChallengeData2016', | |
| img_dir='images/training', | |
| ann_dir='annotations/training', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', reduce_zero_label=False), | |
| dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), | |
| dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=0), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_semantic_seg']) | |
| ]), | |
| val=dict( | |
| type='ADE20K151Dataset', | |
| data_root='data/ade/ADEChallengeData2016', | |
| img_dir='images/validation', | |
| ann_dir='annotations/validation', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(2048, 512), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict( | |
| type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ]), | |
| test=dict( | |
| type='ADE20K151Dataset', | |
| data_root='data/ade/ADEChallengeData2016', | |
| img_dir='images/validation', | |
| ann_dir='annotations/validation', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(2048, 512), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict( | |
| type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ])) | |
| log_config = dict( | |
| interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) | |
| dist_params = dict(backend='nccl') | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| workflow = [('train', 1)] | |
| cudnn_benchmark = True | |
| optimizer = dict( | |
| type='AdamW', lr=0.00015, betas=[0.9, 0.96], weight_decay=0.045) | |
| optimizer_config = dict() | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=1000, | |
| warmup_ratio=1e-06, | |
| step=10000, | |
| gamma=0.5, | |
| min_lr=1e-06, | |
| by_epoch=False) | |
| runner = dict(type='IterBasedRunner', max_iters=80000) | |
| checkpoint_config = dict(by_epoch=False, interval=8000) | |
| evaluation = dict( | |
| interval=8000, metric='mIoU', pre_eval=True, save_best='mIoU') | |
| work_dir = './work_dirs/segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151' | |
| gpu_ids = range(0, 8) | |
| auto_resume = True | |
| 2023-03-03 13:59:38,432 - mmseg - INFO - Set random seed to 97773280, deterministic: False | |
| 2023-03-03 13:59:38,757 - mmseg - INFO - Parameters in backbone freezed! | |
| 2023-03-03 13:59:38,758 - mmseg - INFO - Trainable parameters in SegformerHeadUnetFCHeadSingleStep: ['unet.init_conv.weight', 'unet.init_conv.bias', 'unet.time_mlp.1.weight', 'unet.time_mlp.1.bias', 'unet.time_mlp.3.weight', 'unet.time_mlp.3.bias', 'unet.downs.0.0.mlp.1.weight', 'unet.downs.0.0.mlp.1.bias', 'unet.downs.0.0.block1.proj.weight', 'unet.downs.0.0.block1.proj.bias', 'unet.downs.0.0.block1.norm.weight', 'unet.downs.0.0.block1.norm.bias', 'unet.downs.0.0.block2.proj.weight', 'unet.downs.0.0.block2.proj.bias', 'unet.downs.0.0.block2.norm.weight', 'unet.downs.0.0.block2.norm.bias', 'unet.downs.0.1.mlp.1.weight', 'unet.downs.0.1.mlp.1.bias', 'unet.downs.0.1.block1.proj.weight', 'unet.downs.0.1.block1.proj.bias', 'unet.downs.0.1.block1.norm.weight', 'unet.downs.0.1.block1.norm.bias', 'unet.downs.0.1.block2.proj.weight', 'unet.downs.0.1.block2.proj.bias', 'unet.downs.0.1.block2.norm.weight', 'unet.downs.0.1.block2.norm.bias', 'unet.downs.0.2.fn.fn.to_qkv.weight', 'unet.downs.0.2.fn.fn.to_out.0.weight', 'unet.downs.0.2.fn.fn.to_out.0.bias', 'unet.downs.0.2.fn.fn.to_out.1.g', 'unet.downs.0.2.fn.norm.g', 'unet.downs.0.3.weight', 'unet.downs.0.3.bias', 'unet.downs.1.0.mlp.1.weight', 'unet.downs.1.0.mlp.1.bias', 'unet.downs.1.0.block1.proj.weight', 'unet.downs.1.0.block1.proj.bias', 'unet.downs.1.0.block1.norm.weight', 'unet.downs.1.0.block1.norm.bias', 'unet.downs.1.0.block2.proj.weight', 'unet.downs.1.0.block2.proj.bias', 'unet.downs.1.0.block2.norm.weight', 'unet.downs.1.0.block2.norm.bias', 'unet.downs.1.1.mlp.1.weight', 'unet.downs.1.1.mlp.1.bias', 'unet.downs.1.1.block1.proj.weight', 'unet.downs.1.1.block1.proj.bias', 'unet.downs.1.1.block1.norm.weight', 'unet.downs.1.1.block1.norm.bias', 'unet.downs.1.1.block2.proj.weight', 'unet.downs.1.1.block2.proj.bias', 'unet.downs.1.1.block2.norm.weight', 'unet.downs.1.1.block2.norm.bias', 'unet.downs.1.2.fn.fn.to_qkv.weight', 'unet.downs.1.2.fn.fn.to_out.0.weight', 'unet.downs.1.2.fn.fn.to_out.0.bias', 'unet.downs.1.2.fn.fn.to_out.1.g', 'unet.downs.1.2.fn.norm.g', 'unet.downs.1.3.weight', 'unet.downs.1.3.bias', 'unet.downs.2.0.mlp.1.weight', 'unet.downs.2.0.mlp.1.bias', 'unet.downs.2.0.block1.proj.weight', 'unet.downs.2.0.block1.proj.bias', 'unet.downs.2.0.block1.norm.weight', 'unet.downs.2.0.block1.norm.bias', 'unet.downs.2.0.block2.proj.weight', 'unet.downs.2.0.block2.proj.bias', 'unet.downs.2.0.block2.norm.weight', 'unet.downs.2.0.block2.norm.bias', 'unet.downs.2.1.mlp.1.weight', 'unet.downs.2.1.mlp.1.bias', 'unet.downs.2.1.block1.proj.weight', 'unet.downs.2.1.block1.proj.bias', 'unet.downs.2.1.block1.norm.weight', 'unet.downs.2.1.block1.norm.bias', 'unet.downs.2.1.block2.proj.weight', 'unet.downs.2.1.block2.proj.bias', 'unet.downs.2.1.block2.norm.weight', 'unet.downs.2.1.block2.norm.bias', 'unet.downs.2.2.fn.fn.to_qkv.weight', 'unet.downs.2.2.fn.fn.to_out.0.weight', 'unet.downs.2.2.fn.fn.to_out.0.bias', 'unet.downs.2.2.fn.fn.to_out.1.g', 'unet.downs.2.2.fn.norm.g', 'unet.downs.2.3.weight', 'unet.downs.2.3.bias', 'unet.ups.0.0.mlp.1.weight', 'unet.ups.0.0.mlp.1.bias', 'unet.ups.0.0.block1.proj.weight', 'unet.ups.0.0.block1.proj.bias', 'unet.ups.0.0.block1.norm.weight', 'unet.ups.0.0.block1.norm.bias', 'unet.ups.0.0.block2.proj.weight', 'unet.ups.0.0.block2.proj.bias', 'unet.ups.0.0.block2.norm.weight', 'unet.ups.0.0.block2.norm.bias', 'unet.ups.0.0.res_conv.weight', 'unet.ups.0.0.res_conv.bias', 'unet.ups.0.1.mlp.1.weight', 'unet.ups.0.1.mlp.1.bias', 'unet.ups.0.1.block1.proj.weight', 'unet.ups.0.1.block1.proj.bias', 'unet.ups.0.1.block1.norm.weight', 'unet.ups.0.1.block1.norm.bias', 'unet.ups.0.1.block2.proj.weight', 'unet.ups.0.1.block2.proj.bias', 'unet.ups.0.1.block2.norm.weight', 'unet.ups.0.1.block2.norm.bias', 'unet.ups.0.1.res_conv.weight', 'unet.ups.0.1.res_conv.bias', 'unet.ups.0.2.fn.fn.to_qkv.weight', 'unet.ups.0.2.fn.fn.to_out.0.weight', 'unet.ups.0.2.fn.fn.to_out.0.bias', 'unet.ups.0.2.fn.fn.to_out.1.g', 'unet.ups.0.2.fn.norm.g', 'unet.ups.0.3.1.weight', 'unet.ups.0.3.1.bias', 'unet.ups.1.0.mlp.1.weight', 'unet.ups.1.0.mlp.1.bias', 'unet.ups.1.0.block1.proj.weight', 'unet.ups.1.0.block1.proj.bias', 'unet.ups.1.0.block1.norm.weight', 'unet.ups.1.0.block1.norm.bias', 'unet.ups.1.0.block2.proj.weight', 'unet.ups.1.0.block2.proj.bias', 'unet.ups.1.0.block2.norm.weight', 'unet.ups.1.0.block2.norm.bias', 'unet.ups.1.0.res_conv.weight', 'unet.ups.1.0.res_conv.bias', 'unet.ups.1.1.mlp.1.weight', 'unet.ups.1.1.mlp.1.bias', 'unet.ups.1.1.block1.proj.weight', 'unet.ups.1.1.block1.proj.bias', 'unet.ups.1.1.block1.norm.weight', 'unet.ups.1.1.block1.norm.bias', 'unet.ups.1.1.block2.proj.weight', 'unet.ups.1.1.block2.proj.bias', 'unet.ups.1.1.block2.norm.weight', 'unet.ups.1.1.block2.norm.bias', 'unet.ups.1.1.res_conv.weight', 'unet.ups.1.1.res_conv.bias', 'unet.ups.1.2.fn.fn.to_qkv.weight', 'unet.ups.1.2.fn.fn.to_out.0.weight', 'unet.ups.1.2.fn.fn.to_out.0.bias', 'unet.ups.1.2.fn.fn.to_out.1.g', 'unet.ups.1.2.fn.norm.g', 'unet.ups.1.3.1.weight', 'unet.ups.1.3.1.bias', 'unet.ups.2.0.mlp.1.weight', 'unet.ups.2.0.mlp.1.bias', 'unet.ups.2.0.block1.proj.weight', 'unet.ups.2.0.block1.proj.bias', 'unet.ups.2.0.block1.norm.weight', 'unet.ups.2.0.block1.norm.bias', 'unet.ups.2.0.block2.proj.weight', 'unet.ups.2.0.block2.proj.bias', 'unet.ups.2.0.block2.norm.weight', 'unet.ups.2.0.block2.norm.bias', 'unet.ups.2.0.res_conv.weight', 'unet.ups.2.0.res_conv.bias', 'unet.ups.2.1.mlp.1.weight', 'unet.ups.2.1.mlp.1.bias', 'unet.ups.2.1.block1.proj.weight', 'unet.ups.2.1.block1.proj.bias', 'unet.ups.2.1.block1.norm.weight', 'unet.ups.2.1.block1.norm.bias', 'unet.ups.2.1.block2.proj.weight', 'unet.ups.2.1.block2.proj.bias', 'unet.ups.2.1.block2.norm.weight', 'unet.ups.2.1.block2.norm.bias', 'unet.ups.2.1.res_conv.weight', 'unet.ups.2.1.res_conv.bias', 'unet.ups.2.2.fn.fn.to_qkv.weight', 'unet.ups.2.2.fn.fn.to_out.0.weight', 'unet.ups.2.2.fn.fn.to_out.0.bias', 'unet.ups.2.2.fn.fn.to_out.1.g', 'unet.ups.2.2.fn.norm.g', 'unet.ups.2.3.weight', 'unet.ups.2.3.bias', 'unet.mid_block1.mlp.1.weight', 'unet.mid_block1.mlp.1.bias', 'unet.mid_block1.block1.proj.weight', 'unet.mid_block1.block1.proj.bias', 'unet.mid_block1.block1.norm.weight', 'unet.mid_block1.block1.norm.bias', 'unet.mid_block1.block2.proj.weight', 'unet.mid_block1.block2.proj.bias', 'unet.mid_block1.block2.norm.weight', 'unet.mid_block1.block2.norm.bias', 'unet.mid_attn.fn.fn.to_qkv.weight', 'unet.mid_attn.fn.fn.to_out.weight', 'unet.mid_attn.fn.fn.to_out.bias', 'unet.mid_attn.fn.norm.g', 'unet.mid_block2.mlp.1.weight', 'unet.mid_block2.mlp.1.bias', 'unet.mid_block2.block1.proj.weight', 'unet.mid_block2.block1.proj.bias', 'unet.mid_block2.block1.norm.weight', 'unet.mid_block2.block1.norm.bias', 'unet.mid_block2.block2.proj.weight', 'unet.mid_block2.block2.proj.bias', 'unet.mid_block2.block2.norm.weight', 'unet.mid_block2.block2.norm.bias', 'unet.final_res_block.mlp.1.weight', 'unet.final_res_block.mlp.1.bias', 'unet.final_res_block.block1.proj.weight', 'unet.final_res_block.block1.proj.bias', 'unet.final_res_block.block1.norm.weight', 'unet.final_res_block.block1.norm.bias', 'unet.final_res_block.block2.proj.weight', 'unet.final_res_block.block2.proj.bias', 'unet.final_res_block.block2.norm.weight', 'unet.final_res_block.block2.norm.bias', 'unet.final_res_block.res_conv.weight', 'unet.final_res_block.res_conv.bias', 'unet.final_conv.weight', 'unet.final_conv.bias', 'conv_seg_new.weight', 'conv_seg_new.bias'] | |
| 2023-03-03 13:59:38,758 - mmseg - INFO - Parameters in decode_head freezed! | |
| 2023-03-03 13:59:38,778 - mmseg - INFO - load checkpoint from local path: pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth | |
| 2023-03-03 13:59:39,026 - mmseg - WARNING - The model and loaded state dict do not match exactly | |
| unexpected key in source state_dict: decode_head.conv_seg.weight, decode_head.conv_seg.bias, decode_head.convs.0.conv.weight, decode_head.convs.0.bn.weight, decode_head.convs.0.bn.bias, decode_head.convs.0.bn.running_mean, decode_head.convs.0.bn.running_var, decode_head.convs.0.bn.num_batches_tracked, decode_head.convs.1.conv.weight, decode_head.convs.1.bn.weight, decode_head.convs.1.bn.bias, decode_head.convs.1.bn.running_mean, decode_head.convs.1.bn.running_var, decode_head.convs.1.bn.num_batches_tracked, decode_head.convs.2.conv.weight, decode_head.convs.2.bn.weight, decode_head.convs.2.bn.bias, decode_head.convs.2.bn.running_mean, decode_head.convs.2.bn.running_var, decode_head.convs.2.bn.num_batches_tracked, decode_head.convs.3.conv.weight, decode_head.convs.3.bn.weight, decode_head.convs.3.bn.bias, decode_head.convs.3.bn.running_mean, decode_head.convs.3.bn.running_var, decode_head.convs.3.bn.num_batches_tracked, decode_head.fusion_conv.conv.weight, decode_head.fusion_conv.bn.weight, decode_head.fusion_conv.bn.bias, decode_head.fusion_conv.bn.running_mean, decode_head.fusion_conv.bn.running_var, decode_head.fusion_conv.bn.num_batches_tracked | |
| 2023-03-03 13:59:39,040 - mmseg - INFO - load checkpoint from local path: pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth | |
| 2023-03-03 13:59:39,262 - mmseg - WARNING - The model and loaded state dict do not match exactly | |
| unexpected key in source state_dict: backbone.layers.0.0.projection.weight, backbone.layers.0.0.projection.bias, backbone.layers.0.0.norm.weight, backbone.layers.0.0.norm.bias, backbone.layers.0.1.0.norm1.weight, backbone.layers.0.1.0.norm1.bias, backbone.layers.0.1.0.attn.attn.in_proj_weight, backbone.layers.0.1.0.attn.attn.in_proj_bias, backbone.layers.0.1.0.attn.attn.out_proj.weight, backbone.layers.0.1.0.attn.attn.out_proj.bias, backbone.layers.0.1.0.attn.sr.weight, backbone.layers.0.1.0.attn.sr.bias, backbone.layers.0.1.0.attn.norm.weight, backbone.layers.0.1.0.attn.norm.bias, backbone.layers.0.1.0.norm2.weight, backbone.layers.0.1.0.norm2.bias, backbone.layers.0.1.0.ffn.layers.0.weight, backbone.layers.0.1.0.ffn.layers.0.bias, backbone.layers.0.1.0.ffn.layers.1.weight, backbone.layers.0.1.0.ffn.layers.1.bias, backbone.layers.0.1.0.ffn.layers.4.weight, backbone.layers.0.1.0.ffn.layers.4.bias, backbone.layers.0.1.1.norm1.weight, backbone.layers.0.1.1.norm1.bias, backbone.layers.0.1.1.attn.attn.in_proj_weight, backbone.layers.0.1.1.attn.attn.in_proj_bias, backbone.layers.0.1.1.attn.attn.out_proj.weight, backbone.layers.0.1.1.attn.attn.out_proj.bias, backbone.layers.0.1.1.attn.sr.weight, backbone.layers.0.1.1.attn.sr.bias, backbone.layers.0.1.1.attn.norm.weight, backbone.layers.0.1.1.attn.norm.bias, backbone.layers.0.1.1.norm2.weight, backbone.layers.0.1.1.norm2.bias, backbone.layers.0.1.1.ffn.layers.0.weight, backbone.layers.0.1.1.ffn.layers.0.bias, backbone.layers.0.1.1.ffn.layers.1.weight, backbone.layers.0.1.1.ffn.layers.1.bias, backbone.layers.0.1.1.ffn.layers.4.weight, backbone.layers.0.1.1.ffn.layers.4.bias, backbone.layers.0.1.2.norm1.weight, backbone.layers.0.1.2.norm1.bias, backbone.layers.0.1.2.attn.attn.in_proj_weight, backbone.layers.0.1.2.attn.attn.in_proj_bias, backbone.layers.0.1.2.attn.attn.out_proj.weight, backbone.layers.0.1.2.attn.attn.out_proj.bias, backbone.layers.0.1.2.attn.sr.weight, backbone.layers.0.1.2.attn.sr.bias, backbone.layers.0.1.2.attn.norm.weight, backbone.layers.0.1.2.attn.norm.bias, backbone.layers.0.1.2.norm2.weight, backbone.layers.0.1.2.norm2.bias, backbone.layers.0.1.2.ffn.layers.0.weight, backbone.layers.0.1.2.ffn.layers.0.bias, backbone.layers.0.1.2.ffn.layers.1.weight, backbone.layers.0.1.2.ffn.layers.1.bias, backbone.layers.0.1.2.ffn.layers.4.weight, backbone.layers.0.1.2.ffn.layers.4.bias, backbone.layers.0.2.weight, backbone.layers.0.2.bias, backbone.layers.1.0.projection.weight, backbone.layers.1.0.projection.bias, backbone.layers.1.0.norm.weight, backbone.layers.1.0.norm.bias, backbone.layers.1.1.0.norm1.weight, backbone.layers.1.1.0.norm1.bias, backbone.layers.1.1.0.attn.attn.in_proj_weight, backbone.layers.1.1.0.attn.attn.in_proj_bias, backbone.layers.1.1.0.attn.attn.out_proj.weight, backbone.layers.1.1.0.attn.attn.out_proj.bias, backbone.layers.1.1.0.attn.sr.weight, backbone.layers.1.1.0.attn.sr.bias, backbone.layers.1.1.0.attn.norm.weight, backbone.layers.1.1.0.attn.norm.bias, backbone.layers.1.1.0.norm2.weight, backbone.layers.1.1.0.norm2.bias, backbone.layers.1.1.0.ffn.layers.0.weight, backbone.layers.1.1.0.ffn.layers.0.bias, backbone.layers.1.1.0.ffn.layers.1.weight, backbone.layers.1.1.0.ffn.layers.1.bias, backbone.layers.1.1.0.ffn.layers.4.weight, backbone.layers.1.1.0.ffn.layers.4.bias, backbone.layers.1.1.1.norm1.weight, backbone.layers.1.1.1.norm1.bias, backbone.layers.1.1.1.attn.attn.in_proj_weight, backbone.layers.1.1.1.attn.attn.in_proj_bias, backbone.layers.1.1.1.attn.attn.out_proj.weight, backbone.layers.1.1.1.attn.attn.out_proj.bias, backbone.layers.1.1.1.attn.sr.weight, backbone.layers.1.1.1.attn.sr.bias, backbone.layers.1.1.1.attn.norm.weight, backbone.layers.1.1.1.attn.norm.bias, backbone.layers.1.1.1.norm2.weight, backbone.layers.1.1.1.norm2.bias, backbone.layers.1.1.1.ffn.layers.0.weight, backbone.layers.1.1.1.ffn.layers.0.bias, backbone.layers.1.1.1.ffn.layers.1.weight, backbone.layers.1.1.1.ffn.layers.1.bias, backbone.layers.1.1.1.ffn.layers.4.weight, backbone.layers.1.1.1.ffn.layers.4.bias, backbone.layers.1.1.2.norm1.weight, backbone.layers.1.1.2.norm1.bias, backbone.layers.1.1.2.attn.attn.in_proj_weight, backbone.layers.1.1.2.attn.attn.in_proj_bias, backbone.layers.1.1.2.attn.attn.out_proj.weight, backbone.layers.1.1.2.attn.attn.out_proj.bias, backbone.layers.1.1.2.attn.sr.weight, backbone.layers.1.1.2.attn.sr.bias, backbone.layers.1.1.2.attn.norm.weight, backbone.layers.1.1.2.attn.norm.bias, backbone.layers.1.1.2.norm2.weight, backbone.layers.1.1.2.norm2.bias, backbone.layers.1.1.2.ffn.layers.0.weight, backbone.layers.1.1.2.ffn.layers.0.bias, backbone.layers.1.1.2.ffn.layers.1.weight, backbone.layers.1.1.2.ffn.layers.1.bias, backbone.layers.1.1.2.ffn.layers.4.weight, backbone.layers.1.1.2.ffn.layers.4.bias, backbone.layers.1.1.3.norm1.weight, backbone.layers.1.1.3.norm1.bias, backbone.layers.1.1.3.attn.attn.in_proj_weight, backbone.layers.1.1.3.attn.attn.in_proj_bias, backbone.layers.1.1.3.attn.attn.out_proj.weight, backbone.layers.1.1.3.attn.attn.out_proj.bias, backbone.layers.1.1.3.attn.sr.weight, backbone.layers.1.1.3.attn.sr.bias, backbone.layers.1.1.3.attn.norm.weight, backbone.layers.1.1.3.attn.norm.bias, backbone.layers.1.1.3.norm2.weight, backbone.layers.1.1.3.norm2.bias, backbone.layers.1.1.3.ffn.layers.0.weight, backbone.layers.1.1.3.ffn.layers.0.bias, backbone.layers.1.1.3.ffn.layers.1.weight, backbone.layers.1.1.3.ffn.layers.1.bias, backbone.layers.1.1.3.ffn.layers.4.weight, backbone.layers.1.1.3.ffn.layers.4.bias, backbone.layers.1.2.weight, backbone.layers.1.2.bias, backbone.layers.2.0.projection.weight, backbone.layers.2.0.projection.bias, backbone.layers.2.0.norm.weight, backbone.layers.2.0.norm.bias, backbone.layers.2.1.0.norm1.weight, backbone.layers.2.1.0.norm1.bias, backbone.layers.2.1.0.attn.attn.in_proj_weight, backbone.layers.2.1.0.attn.attn.in_proj_bias, backbone.layers.2.1.0.attn.attn.out_proj.weight, backbone.layers.2.1.0.attn.attn.out_proj.bias, backbone.layers.2.1.0.attn.sr.weight, backbone.layers.2.1.0.attn.sr.bias, backbone.layers.2.1.0.attn.norm.weight, backbone.layers.2.1.0.attn.norm.bias, backbone.layers.2.1.0.norm2.weight, backbone.layers.2.1.0.norm2.bias, backbone.layers.2.1.0.ffn.layers.0.weight, backbone.layers.2.1.0.ffn.layers.0.bias, backbone.layers.2.1.0.ffn.layers.1.weight, backbone.layers.2.1.0.ffn.layers.1.bias, backbone.layers.2.1.0.ffn.layers.4.weight, backbone.layers.2.1.0.ffn.layers.4.bias, backbone.layers.2.1.1.norm1.weight, backbone.layers.2.1.1.norm1.bias, backbone.layers.2.1.1.attn.attn.in_proj_weight, backbone.layers.2.1.1.attn.attn.in_proj_bias, backbone.layers.2.1.1.attn.attn.out_proj.weight, backbone.layers.2.1.1.attn.attn.out_proj.bias, backbone.layers.2.1.1.attn.sr.weight, backbone.layers.2.1.1.attn.sr.bias, backbone.layers.2.1.1.attn.norm.weight, backbone.layers.2.1.1.attn.norm.bias, backbone.layers.2.1.1.norm2.weight, backbone.layers.2.1.1.norm2.bias, backbone.layers.2.1.1.ffn.layers.0.weight, backbone.layers.2.1.1.ffn.layers.0.bias, backbone.layers.2.1.1.ffn.layers.1.weight, backbone.layers.2.1.1.ffn.layers.1.bias, backbone.layers.2.1.1.ffn.layers.4.weight, backbone.layers.2.1.1.ffn.layers.4.bias, backbone.layers.2.1.2.norm1.weight, backbone.layers.2.1.2.norm1.bias, backbone.layers.2.1.2.attn.attn.in_proj_weight, backbone.layers.2.1.2.attn.attn.in_proj_bias, backbone.layers.2.1.2.attn.attn.out_proj.weight, backbone.layers.2.1.2.attn.attn.out_proj.bias, backbone.layers.2.1.2.attn.sr.weight, backbone.layers.2.1.2.attn.sr.bias, backbone.layers.2.1.2.attn.norm.weight, backbone.layers.2.1.2.attn.norm.bias, backbone.layers.2.1.2.norm2.weight, backbone.layers.2.1.2.norm2.bias, backbone.layers.2.1.2.ffn.layers.0.weight, backbone.layers.2.1.2.ffn.layers.0.bias, backbone.layers.2.1.2.ffn.layers.1.weight, backbone.layers.2.1.2.ffn.layers.1.bias, backbone.layers.2.1.2.ffn.layers.4.weight, backbone.layers.2.1.2.ffn.layers.4.bias, backbone.layers.2.1.3.norm1.weight, backbone.layers.2.1.3.norm1.bias, backbone.layers.2.1.3.attn.attn.in_proj_weight, backbone.layers.2.1.3.attn.attn.in_proj_bias, backbone.layers.2.1.3.attn.attn.out_proj.weight, backbone.layers.2.1.3.attn.attn.out_proj.bias, backbone.layers.2.1.3.attn.sr.weight, backbone.layers.2.1.3.attn.sr.bias, backbone.layers.2.1.3.attn.norm.weight, backbone.layers.2.1.3.attn.norm.bias, backbone.layers.2.1.3.norm2.weight, backbone.layers.2.1.3.norm2.bias, backbone.layers.2.1.3.ffn.layers.0.weight, backbone.layers.2.1.3.ffn.layers.0.bias, backbone.layers.2.1.3.ffn.layers.1.weight, backbone.layers.2.1.3.ffn.layers.1.bias, backbone.layers.2.1.3.ffn.layers.4.weight, backbone.layers.2.1.3.ffn.layers.4.bias, backbone.layers.2.1.4.norm1.weight, backbone.layers.2.1.4.norm1.bias, backbone.layers.2.1.4.attn.attn.in_proj_weight, backbone.layers.2.1.4.attn.attn.in_proj_bias, backbone.layers.2.1.4.attn.attn.out_proj.weight, backbone.layers.2.1.4.attn.attn.out_proj.bias, backbone.layers.2.1.4.attn.sr.weight, backbone.layers.2.1.4.attn.sr.bias, backbone.layers.2.1.4.attn.norm.weight, backbone.layers.2.1.4.attn.norm.bias, backbone.layers.2.1.4.norm2.weight, backbone.layers.2.1.4.norm2.bias, backbone.layers.2.1.4.ffn.layers.0.weight, backbone.layers.2.1.4.ffn.layers.0.bias, backbone.layers.2.1.4.ffn.layers.1.weight, backbone.layers.2.1.4.ffn.layers.1.bias, backbone.layers.2.1.4.ffn.layers.4.weight, backbone.layers.2.1.4.ffn.layers.4.bias, backbone.layers.2.1.5.norm1.weight, backbone.layers.2.1.5.norm1.bias, backbone.layers.2.1.5.attn.attn.in_proj_weight, backbone.layers.2.1.5.attn.attn.in_proj_bias, backbone.layers.2.1.5.attn.attn.out_proj.weight, backbone.layers.2.1.5.attn.attn.out_proj.bias, backbone.layers.2.1.5.attn.sr.weight, backbone.layers.2.1.5.attn.sr.bias, backbone.layers.2.1.5.attn.norm.weight, backbone.layers.2.1.5.attn.norm.bias, backbone.layers.2.1.5.norm2.weight, backbone.layers.2.1.5.norm2.bias, backbone.layers.2.1.5.ffn.layers.0.weight, backbone.layers.2.1.5.ffn.layers.0.bias, backbone.layers.2.1.5.ffn.layers.1.weight, backbone.layers.2.1.5.ffn.layers.1.bias, backbone.layers.2.1.5.ffn.layers.4.weight, backbone.layers.2.1.5.ffn.layers.4.bias, backbone.layers.2.2.weight, backbone.layers.2.2.bias, backbone.layers.3.0.projection.weight, backbone.layers.3.0.projection.bias, backbone.layers.3.0.norm.weight, backbone.layers.3.0.norm.bias, backbone.layers.3.1.0.norm1.weight, backbone.layers.3.1.0.norm1.bias, backbone.layers.3.1.0.attn.attn.in_proj_weight, backbone.layers.3.1.0.attn.attn.in_proj_bias, backbone.layers.3.1.0.attn.attn.out_proj.weight, backbone.layers.3.1.0.attn.attn.out_proj.bias, backbone.layers.3.1.0.norm2.weight, backbone.layers.3.1.0.norm2.bias, backbone.layers.3.1.0.ffn.layers.0.weight, backbone.layers.3.1.0.ffn.layers.0.bias, backbone.layers.3.1.0.ffn.layers.1.weight, backbone.layers.3.1.0.ffn.layers.1.bias, backbone.layers.3.1.0.ffn.layers.4.weight, backbone.layers.3.1.0.ffn.layers.4.bias, backbone.layers.3.1.1.norm1.weight, backbone.layers.3.1.1.norm1.bias, backbone.layers.3.1.1.attn.attn.in_proj_weight, backbone.layers.3.1.1.attn.attn.in_proj_bias, backbone.layers.3.1.1.attn.attn.out_proj.weight, backbone.layers.3.1.1.attn.attn.out_proj.bias, backbone.layers.3.1.1.norm2.weight, backbone.layers.3.1.1.norm2.bias, backbone.layers.3.1.1.ffn.layers.0.weight, backbone.layers.3.1.1.ffn.layers.0.bias, backbone.layers.3.1.1.ffn.layers.1.weight, backbone.layers.3.1.1.ffn.layers.1.bias, backbone.layers.3.1.1.ffn.layers.4.weight, backbone.layers.3.1.1.ffn.layers.4.bias, backbone.layers.3.1.2.norm1.weight, backbone.layers.3.1.2.norm1.bias, backbone.layers.3.1.2.attn.attn.in_proj_weight, backbone.layers.3.1.2.attn.attn.in_proj_bias, backbone.layers.3.1.2.attn.attn.out_proj.weight, backbone.layers.3.1.2.attn.attn.out_proj.bias, backbone.layers.3.1.2.norm2.weight, backbone.layers.3.1.2.norm2.bias, backbone.layers.3.1.2.ffn.layers.0.weight, backbone.layers.3.1.2.ffn.layers.0.bias, backbone.layers.3.1.2.ffn.layers.1.weight, backbone.layers.3.1.2.ffn.layers.1.bias, backbone.layers.3.1.2.ffn.layers.4.weight, backbone.layers.3.1.2.ffn.layers.4.bias, backbone.layers.3.2.weight, backbone.layers.3.2.bias | |
| missing keys in source state_dict: unet.init_conv.weight, unet.init_conv.bias, unet.time_mlp.1.weight, unet.time_mlp.1.bias, unet.time_mlp.3.weight, unet.time_mlp.3.bias, unet.downs.0.0.mlp.1.weight, unet.downs.0.0.mlp.1.bias, unet.downs.0.0.block1.proj.weight, unet.downs.0.0.block1.proj.bias, unet.downs.0.0.block1.norm.weight, unet.downs.0.0.block1.norm.bias, unet.downs.0.0.block2.proj.weight, unet.downs.0.0.block2.proj.bias, unet.downs.0.0.block2.norm.weight, unet.downs.0.0.block2.norm.bias, unet.downs.0.1.mlp.1.weight, unet.downs.0.1.mlp.1.bias, unet.downs.0.1.block1.proj.weight, unet.downs.0.1.block1.proj.bias, unet.downs.0.1.block1.norm.weight, unet.downs.0.1.block1.norm.bias, unet.downs.0.1.block2.proj.weight, unet.downs.0.1.block2.proj.bias, unet.downs.0.1.block2.norm.weight, unet.downs.0.1.block2.norm.bias, unet.downs.0.2.fn.fn.to_qkv.weight, unet.downs.0.2.fn.fn.to_out.0.weight, unet.downs.0.2.fn.fn.to_out.0.bias, unet.downs.0.2.fn.fn.to_out.1.g, unet.downs.0.2.fn.norm.g, 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| 2023-03-03 13:59:39,286 - mmseg - INFO - EncoderDecoderFreeze( | |
| (backbone): MixVisionTransformerCustomInitWeights( | |
| (layers): ModuleList( | |
| (0): ModuleList( | |
| (0): PatchEmbed( | |
| (projection): Conv2d(3, 64, kernel_size=(7, 7), stride=(4, 4), padding=(3, 3)) | |
| (norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (1): ModuleList( | |
| (0): TransformerEncoderLayer( | |
| (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8)) | |
| (norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (1): TransformerEncoderLayer( | |
| (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8)) | |
| (norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (2): TransformerEncoderLayer( | |
| (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8)) | |
| (norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| ) | |
| (2): LayerNorm((64,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (1): ModuleList( | |
| (0): PatchEmbed( | |
| (projection): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | |
| (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (1): ModuleList( | |
| (0): TransformerEncoderLayer( | |
| (norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) | |
| (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (1): TransformerEncoderLayer( | |
| (norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) | |
| (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (2): TransformerEncoderLayer( | |
| (norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) | |
| (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (3): TransformerEncoderLayer( | |
| (norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4)) | |
| (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| ) | |
| (2): LayerNorm((128,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (2): ModuleList( | |
| (0): PatchEmbed( | |
| (projection): Conv2d(128, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | |
| (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (1): ModuleList( | |
| (0): TransformerEncoderLayer( | |
| (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2)) | |
| (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (1): TransformerEncoderLayer( | |
| (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2)) | |
| (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (2): TransformerEncoderLayer( | |
| (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2)) | |
| (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (3): TransformerEncoderLayer( | |
| (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2)) | |
| (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (4): TransformerEncoderLayer( | |
| (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2)) | |
| (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (5): TransformerEncoderLayer( | |
| (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2)) | |
| (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| ) | |
| (2): LayerNorm((320,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (3): ModuleList( | |
| (0): PatchEmbed( | |
| (projection): Conv2d(320, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | |
| (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (1): ModuleList( | |
| (0): TransformerEncoderLayer( | |
| (norm1): LayerNorm((512,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| ) | |
| (norm2): LayerNorm((512,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2048) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (1): TransformerEncoderLayer( | |
| (norm1): LayerNorm((512,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| ) | |
| (norm2): LayerNorm((512,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2048) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| (2): TransformerEncoderLayer( | |
| (norm1): LayerNorm((512,), eps=1e-06, elementwise_affine=True) | |
| (attn): EfficientMultiheadAttention( | |
| (attn): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) | |
| ) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| (dropout_layer): DropPath() | |
| ) | |
| (norm2): LayerNorm((512,), eps=1e-06, elementwise_affine=True) | |
| (ffn): MixFFN( | |
| (activate): GELU(approximate='none') | |
| (layers): Sequential( | |
| (0): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2048) | |
| (2): GELU(approximate='none') | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1)) | |
| (5): Dropout(p=0.0, inplace=False) | |
| ) | |
| (dropout_layer): DropPath() | |
| ) | |
| ) | |
| ) | |
| (2): LayerNorm((512,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| ) | |
| ) | |
| init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth'} | |
| (decode_head): SegformerHeadUnetFCHeadSingleStep( | |
| input_transform=multiple_select, ignore_index=0, align_corners=False | |
| (loss_decode): CrossEntropyLoss(avg_non_ignore=False) | |
| (conv_seg): None | |
| (dropout): Dropout2d(p=0.1, inplace=False) | |
| (convs): ModuleList( | |
| (0): ConvModule( | |
| (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activate): ReLU(inplace=True) | |
| ) | |
| (1): ConvModule( | |
| (conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activate): ReLU(inplace=True) | |
| ) | |
| (2): ConvModule( | |
| (conv): Conv2d(320, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activate): ReLU(inplace=True) | |
| ) | |
| (3): ConvModule( | |
| (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activate): ReLU(inplace=True) | |
| ) | |
| ) | |
| (fusion_conv): ConvModule( | |
| (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activate): ReLU(inplace=True) | |
| ) | |
| (unet): Unet( | |
| (init_conv): Conv2d(272, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) | |
| (time_mlp): Sequential( | |
| (0): SinusoidalPosEmb() | |
| (1): Linear(in_features=128, out_features=512, bias=True) | |
| (2): GELU(approximate='none') | |
| (3): Linear(in_features=512, out_features=512, bias=True) | |
| ) | |
| (downs): ModuleList( | |
| (0): ModuleList( | |
| (0): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Identity() | |
| ) | |
| (1): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Identity() | |
| ) | |
| (2): Residual( | |
| (fn): PreNorm( | |
| (fn): LinearAttention( | |
| (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_out): Sequential( | |
| (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): LayerNorm() | |
| ) | |
| ) | |
| (norm): LayerNorm() | |
| ) | |
| ) | |
| (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) | |
| ) | |
| (1): ModuleList( | |
| (0): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Identity() | |
| ) | |
| (1): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Identity() | |
| ) | |
| (2): Residual( | |
| (fn): PreNorm( | |
| (fn): LinearAttention( | |
| (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_out): Sequential( | |
| (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): LayerNorm() | |
| ) | |
| ) | |
| (norm): LayerNorm() | |
| ) | |
| ) | |
| (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) | |
| ) | |
| (2): ModuleList( | |
| (0): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Identity() | |
| ) | |
| (1): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Identity() | |
| ) | |
| (2): Residual( | |
| (fn): PreNorm( | |
| (fn): LinearAttention( | |
| (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_out): Sequential( | |
| (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): LayerNorm() | |
| ) | |
| ) | |
| (norm): LayerNorm() | |
| ) | |
| ) | |
| (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (ups): ModuleList( | |
| (0): ModuleList( | |
| (0): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (1): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (2): Residual( | |
| (fn): PreNorm( | |
| (fn): LinearAttention( | |
| (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_out): Sequential( | |
| (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): LayerNorm() | |
| ) | |
| ) | |
| (norm): LayerNorm() | |
| ) | |
| ) | |
| (3): Sequential( | |
| (0): Upsample(scale_factor=2.0, mode=nearest) | |
| (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (1): ModuleList( | |
| (0): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (1): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (2): Residual( | |
| (fn): PreNorm( | |
| (fn): LinearAttention( | |
| (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_out): Sequential( | |
| (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): LayerNorm() | |
| ) | |
| ) | |
| (norm): LayerNorm() | |
| ) | |
| ) | |
| (3): Sequential( | |
| (0): Upsample(scale_factor=2.0, mode=nearest) | |
| (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (2): ModuleList( | |
| (0): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (1): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (2): Residual( | |
| (fn): PreNorm( | |
| (fn): LinearAttention( | |
| (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_out): Sequential( | |
| (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| (1): LayerNorm() | |
| ) | |
| ) | |
| (norm): LayerNorm() | |
| ) | |
| ) | |
| (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (mid_block1): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Identity() | |
| ) | |
| (mid_attn): Residual( | |
| (fn): PreNorm( | |
| (fn): Attention( | |
| (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_out): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (norm): LayerNorm() | |
| ) | |
| ) | |
| (mid_block2): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Identity() | |
| ) | |
| (final_res_block): ResnetBlock( | |
| (mlp): Sequential( | |
| (0): SiLU() | |
| (1): Linear(in_features=512, out_features=256, bias=True) | |
| ) | |
| (block1): Block( | |
| (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (block2): Block( | |
| (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (norm): GroupNorm(8, 128, eps=1e-05, affine=True) | |
| (act): SiLU() | |
| ) | |
| (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (final_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (conv_seg_new): Conv2d(256, 151, kernel_size=(1, 1), stride=(1, 1)) | |
| (embed): Embedding(151, 16) | |
| ) | |
| init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth'} | |
| ) | |
| 2023-03-03 13:59:40,184 - mmseg - INFO - Loaded 20210 images | |
| 2023-03-03 13:59:41,189 - mmseg - INFO - Loaded 2000 images | |
| 2023-03-03 13:59:41,192 - mmseg - INFO - Start running, host: laizeqiang@SH-IDC1-10-140-37-124, work_dir: /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs/segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151 | |
| 2023-03-03 13:59:41,192 - mmseg - INFO - Hooks will be executed in the following order: | |
| before_run: | |
| (VERY_HIGH ) StepLrUpdaterHook | |
| (NORMAL ) CheckpointHook | |
| (LOW ) DistEvalHook | |
| (VERY_LOW ) TextLoggerHook | |
| -------------------- | |
| before_train_epoch: | |
| (VERY_HIGH ) StepLrUpdaterHook | |
| (LOW ) IterTimerHook | |
| (LOW ) DistEvalHook | |
| (VERY_LOW ) TextLoggerHook | |
| -------------------- | |
| before_train_iter: | |
| (VERY_HIGH ) StepLrUpdaterHook | |
| (LOW ) IterTimerHook | |
| (LOW ) DistEvalHook | |
| -------------------- | |
| after_train_iter: | |
| (ABOVE_NORMAL) OptimizerHook | |
| (NORMAL ) CheckpointHook | |
| (LOW ) IterTimerHook | |
| (LOW ) DistEvalHook | |
| (VERY_LOW ) TextLoggerHook | |
| -------------------- | |
| after_train_epoch: | |
| (NORMAL ) CheckpointHook | |
| (LOW ) DistEvalHook | |
| (VERY_LOW ) TextLoggerHook | |
| -------------------- | |
| before_val_epoch: | |
| (LOW ) IterTimerHook | |
| (VERY_LOW ) TextLoggerHook | |
| -------------------- | |
| before_val_iter: | |
| (LOW ) IterTimerHook | |
| -------------------- | |
| after_val_iter: | |
| (LOW ) IterTimerHook | |
| -------------------- | |
| after_val_epoch: | |
| (VERY_LOW ) TextLoggerHook | |
| -------------------- | |
| after_run: | |
| (VERY_LOW ) TextLoggerHook | |
| -------------------- | |
| 2023-03-03 13:59:41,192 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters | |
| 2023-03-03 13:59:41,192 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs/segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151 by HardDiskBackend. | |