2023-03-03 20:38:32,836 - mmseg - INFO - Multi-processing start method is `None` 2023-03-03 20:38:32,858 - mmseg - INFO - OpenCV num_threads is `128 2023-03-03 20:38:32,858 - mmseg - INFO - OMP num threads is 1 2023-03-03 20:38:32,925 - 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+c844fc6 ------------------------------------------------------------ 2023-03-03 20:38:32,925 - mmseg - INFO - Distributed training: True 2023-03-03 20:38:33,574 - mmseg - INFO - Config: norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( type='EncoderDecoderFreeze', pretrained= 'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth', backbone=dict( type='ResNetV1cCustomInitWeights', depth=101, num_stages=4, out_indices=(0, 1, 2, 3), dilations=(1, 1, 2, 4), strides=(1, 2, 1, 1), norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, style='pytorch', contract_dilation=True), decode_head=dict( type='DepthwiseSeparableASPPHeadUnetFCHeadSingleStep', pretrained= 'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth', dim=256, out_dim=256, unet_channels=528, dim_mults=[1, 1, 1], cat_embedding_dim=16, ignore_index=0, in_channels=2048, in_index=3, channels=512, dilations=(1, 12, 24, 36), c1_in_channels=256, c1_channels=48, dropout_ratio=0.1, num_classes=20, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), auxiliary_head=None, train_cfg=dict(), test_cfg=dict(mode='whole'), freeze_parameters=['backbone', 'decode_head']) dataset_type = 'Cityscapes20Dataset' data_root = 'data/cityscapes/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) crop_size = (512, 1024) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotationsCityscapes20'), dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 1024), 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, 1024), 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, 1024), 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='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict( type='Cityscapes20Dataset', data_root='data/cityscapes/', img_dir='leftImg8bit/train', ann_dir='gtFine/train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotationsCityscapes20'), dict( type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 1024), 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, 1024), pad_val=0, seg_pad_val=0), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']) ]), val=dict( type='Cityscapes20Dataset', data_root='data/cityscapes/', img_dir='leftImg8bit/val', ann_dir='gtFine/val', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 1024), 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='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='Cityscapes20Dataset', data_root='data/cityscapes/', img_dir='leftImg8bit/val', ann_dir='gtFine/val', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 1024), 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='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, max_keep_ckpts=1) evaluation = dict( interval=8000, metric='mIoU', pre_eval=True, save_best='mIoU') checkpoint = 'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth' work_dir = './work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20' gpu_ids = range(0, 8) auto_resume = True 2023-03-03 20:38:37,967 - mmseg - INFO - Set random seed to 835892801, deterministic: False 2023-03-03 20:38:39,336 - mmseg - INFO - Parameters in backbone freezed! 2023-03-03 20:38:39,337 - mmseg - INFO - Trainable parameters in DepthwiseSeparableASPPHeadUnetFCHeadSingleStep: ['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', 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'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 20:38:39,337 - mmseg - INFO - Parameters in decode_head freezed! 2023-03-03 20:38:39,389 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth 2023-03-03 20:38:39,920 - 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.image_pool.1.conv.weight, decode_head.image_pool.1.bn.weight, decode_head.image_pool.1.bn.bias, decode_head.image_pool.1.bn.running_mean, decode_head.image_pool.1.bn.running_var, decode_head.image_pool.1.bn.num_batches_tracked, decode_head.aspp_modules.0.conv.weight, decode_head.aspp_modules.0.bn.weight, decode_head.aspp_modules.0.bn.bias, decode_head.aspp_modules.0.bn.running_mean, decode_head.aspp_modules.0.bn.running_var, decode_head.aspp_modules.0.bn.num_batches_tracked, decode_head.aspp_modules.1.depthwise_conv.conv.weight, decode_head.aspp_modules.1.depthwise_conv.bn.weight, decode_head.aspp_modules.1.depthwise_conv.bn.bias, decode_head.aspp_modules.1.depthwise_conv.bn.running_mean, 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auxiliary_head.convs.0.bn.running_mean, auxiliary_head.convs.0.bn.running_var, auxiliary_head.convs.0.bn.num_batches_tracked 2023-03-03 20:38:39,948 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth 2023-03-03 20:38:40,463 - mmseg - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: backbone.stem.0.weight, backbone.stem.1.weight, backbone.stem.1.bias, backbone.stem.1.running_mean, backbone.stem.1.running_var, backbone.stem.1.num_batches_tracked, backbone.stem.3.weight, backbone.stem.4.weight, backbone.stem.4.bias, backbone.stem.4.running_mean, backbone.stem.4.running_var, backbone.stem.4.num_batches_tracked, backbone.stem.6.weight, backbone.stem.7.weight, backbone.stem.7.bias, backbone.stem.7.running_mean, backbone.stem.7.running_var, backbone.stem.7.num_batches_tracked, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, 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conv_seg_new.bias, embed.weight 2023-03-03 20:38:40,512 - mmseg - INFO - EncoderDecoderFreeze( (backbone): ResNetV1cCustomInitWeights( (stem): Sequential( (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), 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) (6): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (7): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU(inplace=True) ) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): ResLayer( (0): Bottleneck( (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer2): ResLayer( (0): Bottleneck( (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer3): ResLayer( (0): Bottleneck( (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (4): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (5): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (6): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (7): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (8): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (9): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (10): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (11): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (12): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (13): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (14): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (15): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (16): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (17): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (18): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (19): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (20): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (21): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (22): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer4): ResLayer( (0): Bottleneck( (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False) (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False) (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) ) init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth'} (decode_head): DepthwiseSeparableASPPHeadUnetFCHeadSingleStep( input_transform=None, ignore_index=0, align_corners=False (loss_decode): CrossEntropyLoss(avg_non_ignore=False) (conv_seg): None (dropout): Dropout2d(p=0.1, inplace=False) (image_pool): Sequential( (0): AdaptiveAvgPool2d(output_size=1) (1): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (aspp_modules): DepthwiseSeparableASPPModule( (0): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (1): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), groups=2048, bias=False) (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (2): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(24, 24), dilation=(24, 24), groups=2048, bias=False) (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (3): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(36, 36), dilation=(36, 36), groups=2048, bias=False) (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) ) (bottleneck): ConvModule( (conv): Conv2d(2560, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (c1_bottleneck): ConvModule( (conv): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (sep_bottleneck): Sequential( (0): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(560, 560, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=560, bias=False) (bn): SyncBatchNorm(560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(560, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (1): DepthwiseSeparableConvModule( (depthwise_conv): ConvModule( (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (pointwise_conv): ConvModule( (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) ) (unet): Unet( (init_conv): Conv2d(528, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) (time_mlp): Sequential( (0): SinusoidalPosEmb() (1): Linear(in_features=256, out_features=1024, bias=True) (2): GELU(approximate='none') (3): Linear(in_features=1024, out_features=1024, bias=True) ) (downs): ModuleList( (0): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) (1): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) (2): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (ups): ModuleList( (0): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Sequential( (0): Upsample(scale_factor=2.0, mode=nearest) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (1): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Sequential( (0): Upsample(scale_factor=2.0, mode=nearest) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (2): ModuleList( (0): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) ) (2): Residual( (fn): PreNorm( (fn): LinearAttention( (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LayerNorm() ) ) (norm): LayerNorm() ) ) (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (mid_block1): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (mid_attn): Residual( (fn): PreNorm( (fn): Attention( (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (to_out): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) ) (norm): LayerNorm() ) ) (mid_block2): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Identity() ) (final_res_block): ResnetBlock( (mlp): Sequential( (0): SiLU() (1): Linear(in_features=1024, out_features=512, bias=True) ) (block1): Block( (proj): WeightStandardizedConv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (block2): Block( (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (norm): GroupNorm(8, 256, eps=1e-05, affine=True) (act): SiLU() ) (res_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) ) (final_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) ) (conv_seg_new): Conv2d(256, 20, kernel_size=(1, 1), stride=(1, 1)) (embed): Embedding(20, 16) ) init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth'} ) 2023-03-03 20:38:40,578 - mmseg - INFO - Loaded 2975 images 2023-03-03 20:38:41,636 - mmseg - INFO - Loaded 500 images 2023-03-03 20:38:41,642 - mmseg - INFO - Start running, host: laizeqiang@SH-IDC1-10-140-37-151, work_dir: /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20 2023-03-03 20:38:41,642 - mmseg - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) DistEvalHookMultiSteps (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHookMultiSteps (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHookMultiSteps -------------------- after_train_iter: (ABOVE_NORMAL) OptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) DistEvalHookMultiSteps (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) DistEvalHookMultiSteps (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 20:38:41,642 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters 2023-03-03 20:38:41,642 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20 by HardDiskBackend. 2023-03-03 20:39:26,258 - mmseg - INFO - Iter [50/80000] lr: 7.350e-06, eta: 14:40:40, time: 0.661, data_time: 0.014, memory: 67605, decode.loss_ce: 1.8576, decode.acc_seg: 63.8241, loss: 1.8576