{"env_info": "sys.platform: linux\nPython: 3.8.5 (default, Oct 6 2020, 10:04:29) [GCC 6.3.0]\nCUDA available: True\nGPU 0: NVIDIA A100-PCIE-40GB\nCUDA_HOME: /cluster/apps/gcc-6.3.0/cuda-11.0.3-qdlibd2luz2fy7izfefao4c5yitxwjus\nNVCC: Build cuda_11.0_bu.TC445_37.28845127_0\nGCC: gcc (GCC) 6.3.0\nPyTorch: 1.7.1+cu110\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.0\n - 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_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n - CuDNN 8.0.5\n - Magma 2.5.2\n - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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, \n\nTorchVision: 0.8.2+cu110\nOpenCV: 4.4.0\nMMCV: 1.3.7\nMMCV Compiler: GCC 6.3\nMMCV CUDA Compiler: 11.0\nMMSegmentation: 0.16.0+a2393d9", "seed": 0, "exp_name": "231105_0000_gengta1mixHR2csHR_1024x1024_dgdacs_fdthings_srconly_rcs001-20_shade_shb_hrda1-512-01_daformer_sepaspp_sl_mitb5_poly10warm_w1_s0_d5047", "mmseg_version": "0.16.0+a2393d9", "config": "log_config = dict(\n interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\nnorm_cfg = dict(type='BN', requires_grad=True)\nfind_unused_parameters = True\nmodel = dict(\n type='HRDAEncoderDecoder',\n pretrained='pretrained/mit_b5.pth',\n backbone=dict(\n type='mit_b5',\n style='pytorch',\n style_hallucination_cfg=dict(\n concentration_coeff=0.0156, base_style_num=64, style_dim=64)),\n decode_head=dict(\n type='HRDAHead',\n in_channels=[64, 128, 320, 512],\n in_index=[0, 1, 2, 3],\n channels=256,\n dropout_ratio=0.1,\n num_classes=19,\n norm_cfg=dict(type='BN', requires_grad=True),\n align_corners=False,\n decoder_params=dict(\n embed_dims=256,\n embed_cfg=dict(type='mlp', act_cfg=None, norm_cfg=None),\n embed_neck_cfg=dict(type='mlp', act_cfg=None, norm_cfg=None),\n fusion_cfg=dict(\n type='aspp',\n sep=True,\n dilations=(1, 6, 12, 18),\n pool=False,\n act_cfg=dict(type='ReLU'),\n norm_cfg=dict(type='BN', requires_grad=True))),\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n single_scale_head='DAFormerHead',\n attention_classwise=True,\n hr_loss_weight=0.1),\n train_cfg=dict(\n work_dir=\n 'work_dirs/local-exp60/231105_0000_gengta1mixHR2csHR_1024x1024_dgdacs_fdthings_srconly_rcs001-20_shade_shb_hrda1-512-01_daformer_sepaspp_sl_mitb5_poly10warm_w1_s0_d5047'\n ),\n test_cfg=dict(\n mode='slide',\n batched_slide=True,\n stride=[512, 512],\n crop_size=[1024, 1024]),\n scales=[1, 0.5],\n hr_crop_size=[512, 512],\n feature_scale=0.5,\n crop_coord_divisible=8,\n hr_slide_inference=True)\ndataset_type = 'CityscapesDataset'\ndata_root = 'data/cityscapes/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (1024, 1024)\ngen_train_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations'),\n dict(type='Resize', img_scale=(1024, 1024)),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ngta_train_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations'),\n dict(type='Resize', img_scale=(2560, 1440)),\n dict(type='RandomCrop', crop_size=(1024, 1024), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(1024, 1024), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 1024),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=2,\n workers_per_gpu=1,\n train=dict(\n type='DGDataset',\n source2=dict(\n type='GTADataset',\n data_root='data/gen1/',\n img_dir='images',\n ann_dir='labels',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations'),\n dict(type='Resize', img_scale=(1024, 1024)),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ]),\n source=dict(\n type='GTADataset',\n data_root='data/gta/',\n img_dir='images',\n ann_dir='labels',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations'),\n dict(type='Resize', img_scale=(2560, 1440)),\n dict(\n type='RandomCrop',\n crop_size=(1024, 1024),\n cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(\n type='Pad', size=(1024, 1024), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ]),\n rare_class_sampling=dict(\n min_pixels=3000, class_temp=0.01, min_crop_ratio=2.0)),\n val=dict(\n type='CityscapesDataset',\n data_root='data/cityscapes/',\n img_dir='leftImg8bit/val',\n ann_dir='gtFine/val',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 1024),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CityscapesDataset',\n data_root='data/cityscapes/',\n img_dir='leftImg8bit/val',\n ann_dir='gtFine/val',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 1024),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nuda = dict(\n type='DACS',\n source_only=True,\n alpha=0.99,\n pseudo_threshold=0.968,\n pseudo_weight_ignore_top=0,\n pseudo_weight_ignore_bottom=0,\n share_src_backward=True,\n imnet_feature_dist_lambda=0.005,\n imnet_feature_dist_classes=[6, 7, 11, 12, 13, 14, 15, 16, 17, 18],\n imnet_feature_dist_scale_min_ratio=0.75,\n mix='class',\n blur=True,\n color_jitter_strength=0.2,\n color_jitter_probability=0.2,\n style_consistency_lambda=10.0,\n svmin_lambda=0,\n svmin_scale_ratio=(0.8, 1.2),\n svmin_domain='mixed',\n debug_img_interval=1000,\n print_grad_magnitude=False,\n imnet_feature_dist_scale_method='ratio',\n model=dict(\n type='HRDAEncoderDecoder',\n pretrained='pretrained/mit_b5.pth',\n backbone=dict(\n type='mit_b5',\n style='pytorch',\n style_hallucination_cfg=dict(\n concentration_coeff=0.0156, base_style_num=64, style_dim=64)),\n decode_head=dict(\n type='HRDAHead',\n in_channels=[64, 128, 320, 512],\n in_index=[0, 1, 2, 3],\n channels=256,\n dropout_ratio=0.1,\n num_classes=19,\n norm_cfg=dict(type='BN', requires_grad=True),\n align_corners=False,\n decoder_params=dict(\n embed_dims=256,\n embed_cfg=dict(type='mlp', act_cfg=None, norm_cfg=None),\n embed_neck_cfg=dict(type='mlp', act_cfg=None, norm_cfg=None),\n fusion_cfg=dict(\n type='aspp',\n sep=True,\n dilations=(1, 6, 12, 18),\n pool=False,\n act_cfg=dict(type='ReLU'),\n norm_cfg=dict(type='BN', requires_grad=True))),\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n single_scale_head='DAFormerHead',\n attention_classwise=True,\n hr_loss_weight=0.1),\n train_cfg=dict(\n work_dir=\n 'work_dirs/local-exp60/231105_0000_gengta1mixHR2csHR_1024x1024_dgdacs_fdthings_srconly_rcs001-20_shade_shb_hrda1-512-01_daformer_sepaspp_sl_mitb5_poly10warm_w1_s0_d5047'\n ),\n test_cfg=dict(\n mode='slide',\n batched_slide=True,\n stride=[512, 512],\n crop_size=[1024, 1024]),\n scales=[1, 0.5],\n hr_crop_size=[512, 512],\n feature_scale=0.5,\n crop_coord_divisible=8,\n hr_slide_inference=True),\n max_iters=40000)\nuse_ddp_wrapper = True\noptimizer = dict(\n type='AdamW',\n lr=6e-05,\n betas=(0.9, 0.999),\n weight_decay=0.01,\n paramwise_cfg=dict(\n custom_keys=dict(\n head=dict(lr_mult=10.0),\n pos_block=dict(decay_mult=0.0),\n norm=dict(decay_mult=0.0))))\noptimizer_config = None\nlr_config = dict(\n policy='poly',\n warmup='linear',\n warmup_iters=1500,\n warmup_ratio=1e-06,\n power=1.0,\n min_lr=0.0,\n by_epoch=False)\ngpu_model = 'NVIDIATITANRTX'\nn_gpus = 1\nseed = 0\nstyle_hallucination_hook = dict(\n interval=4000, samples_per_gpu=2, workers_per_gpu=6)\nrunner = dict(type='IterBasedRunner', max_iters=40000)\ncheckpoint_config = dict(by_epoch=False, interval=40000, max_keep_ckpts=1)\nevaluation = dict(interval=80000, metric='mIoU')\nname = '231105_0000_gengta1mixHR2csHR_1024x1024_dgdacs_fdthings_srconly_rcs001-20_shade_shb_hrda1-512-01_daformer_sepaspp_sl_mitb5_poly10warm_w1_s0_d5047'\nexp = 60\nname_dataset = 'gengta1mixHR2cityscapesHR_1024x1024'\nname_architecture = 'hrda1-512-0.1_daformer_sepaspp_sl_mitb5'\nname_encoder = 'mitb5'\nname_decoder = 'hrda1-512-0.1_daformer_sepaspp_sl'\nname_uda = 'dgdacs_fdthings_srconly_rcs0.01-2.0_shade_shb'\nname_opt = 'adamw_6e-05_pmTrue_poly10warm_1x2_40k'\nwork_dir = 'work_dirs/local-exp60/231105_0000_gengta1mixHR2csHR_1024x1024_dgdacs_fdthings_srconly_rcs001-20_shade_shb_hrda1-512-01_daformer_sepaspp_sl_mitb5_poly10warm_w1_s0_d5047'\ngit_rev = 'a2393d9f9f12efb943e2eb9922520c3db95eeaa9'\ngpu_ids = range(0, 1)\n", "CLASSES": ["road", "sidewalk", "building", "wall", "fence", "pole", "traffic light", "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car", "truck", "bus", "train", "motorcycle", "bicycle"], "PALETTE": [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156], [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0], [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60], [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], [0, 80, 100], [0, 0, 230], [119, 11, 32]]} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 26687, "data_time": 0.05034, "decode.loss_seg": 2.47277, "decode.acc_seg": 7.02562, "decode.hr.loss_seg": 0.2733, "decode.hr.acc_seg": 5.41425, "decode.logits": -0.03513, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.00907, "time": 76.24701} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 26687, "data_time": 0.01875, "decode.loss_seg": 2.33158, "decode.acc_seg": 40.55007, "decode.hr.loss_seg": 0.26957, "decode.hr.acc_seg": 30.99429, "decode.logits": -0.08103, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.0103, "time": 1.5448} {"mode": "train", "epoch": 1, "iter": 150, "lr": 1e-05, "memory": 26687, "data_time": 0.02105, "decode.loss_seg": 1.98219, "decode.acc_seg": 58.57881, "decode.hr.loss_seg": 0.23468, "decode.hr.acc_seg": 48.45679, "decode.logits": -0.17152, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.01391, "time": 1.55255} {"mode": "train", "epoch": 1, "iter": 200, "lr": 1e-05, "memory": 26687, "data_time": 0.01674, "decode.loss_seg": 1.62649, "decode.acc_seg": 61.34182, "decode.hr.loss_seg": 0.20534, "decode.hr.acc_seg": 50.63025, "decode.logits": -0.30594, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.02209, "time": 1.54172} {"mode": "train", "epoch": 1, "iter": 250, "lr": 1e-05, "memory": 26687, "data_time": 0.02073, "decode.loss_seg": 1.28496, "decode.acc_seg": 67.59062, "decode.hr.loss_seg": 0.1708, "decode.hr.acc_seg": 56.91454, "decode.logits": -0.47463, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.02784, "time": 1.54414} {"mode": "train", "epoch": 1, "iter": 300, "lr": 1e-05, "memory": 26687, "data_time": 0.01567, "decode.loss_seg": 1.12144, "decode.acc_seg": 65.52937, "decode.hr.loss_seg": 0.15973, "decode.hr.acc_seg": 56.29699, "decode.logits": -0.64944, "src.loss_imnet_feat_dist": 0.09687, "style.loss_style_consistency": 0.03323, "time": 1.54589} {"mode": "train", "epoch": 1, "iter": 350, "lr": 1e-05, "memory": 26687, "data_time": 0.0163, "decode.loss_seg": 1.04963, "decode.acc_seg": 66.16114, "decode.hr.loss_seg": 0.14765, "decode.hr.acc_seg": 57.78455, "decode.logits": -0.78878, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.03632, "time": 1.54455} {"mode": "train", "epoch": 1, "iter": 400, "lr": 2e-05, "memory": 26687, "data_time": 0.04123, "decode.loss_seg": 0.84876, "decode.acc_seg": 68.69769, "decode.hr.loss_seg": 0.13353, "decode.hr.acc_seg": 59.88399, "decode.logits": -0.9245, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.03476, "time": 1.57607} {"mode": "train", "epoch": 1, "iter": 450, "lr": 2e-05, "memory": 26687, "data_time": 0.01617, "decode.loss_seg": 0.81389, "decode.acc_seg": 70.71503, "decode.hr.loss_seg": 0.13349, "decode.hr.acc_seg": 61.77156, "decode.logits": -1.04447, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.0354, "time": 1.54698} {"mode": "train", "epoch": 1, "iter": 500, "lr": 2e-05, "memory": 26687, "data_time": 0.01619, "decode.loss_seg": 0.74811, "decode.acc_seg": 69.8952, "decode.hr.loss_seg": 0.11414, "decode.hr.acc_seg": 63.55531, "decode.logits": -1.15644, "src.loss_imnet_feat_dist": 0.10541, "style.loss_style_consistency": 0.03819, "time": 1.54193} {"mode": "train", "epoch": 1, "iter": 550, "lr": 2e-05, "memory": 26687, "data_time": 0.01657, "decode.loss_seg": 0.75939, "decode.acc_seg": 70.39009, "decode.hr.loss_seg": 0.11947, "decode.hr.acc_seg": 60.63874, "decode.logits": -1.25242, "src.loss_imnet_feat_dist": 0.1077, "style.loss_style_consistency": 0.04421, "time": 1.54878} {"mode": "train", "epoch": 1, "iter": 600, "lr": 2e-05, "memory": 26687, "data_time": 0.01653, "decode.loss_seg": 0.71773, "decode.acc_seg": 72.60444, "decode.hr.loss_seg": 0.12279, "decode.hr.acc_seg": 62.54944, "decode.logits": -1.34792, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.04292, "time": 1.55552} {"mode": "train", "epoch": 1, "iter": 650, "lr": 3e-05, "memory": 26687, "data_time": 0.01732, "decode.loss_seg": 0.64756, "decode.acc_seg": 74.66969, "decode.hr.loss_seg": 0.10442, "decode.hr.acc_seg": 67.64222, "decode.logits": -1.44272, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.04435, "time": 1.55069} {"mode": "train", "epoch": 1, "iter": 700, "lr": 3e-05, "memory": 26687, "data_time": 0.01913, "decode.loss_seg": 0.56443, "decode.acc_seg": 77.0217, "decode.hr.loss_seg": 0.09561, "decode.hr.acc_seg": 69.64967, "decode.logits": -1.56279, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.04143, "time": 1.55447} {"mode": "train", "epoch": 1, "iter": 750, "lr": 3e-05, "memory": 26687, "data_time": 0.04207, "decode.loss_seg": 0.62553, "decode.acc_seg": 73.63009, "decode.hr.loss_seg": 0.10549, "decode.hr.acc_seg": 66.2849, "decode.logits": -1.63037, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.05449, "time": 1.56834} {"mode": "train", "epoch": 1, "iter": 800, "lr": 3e-05, "memory": 26687, "data_time": 0.01697, "decode.loss_seg": 0.52448, "decode.acc_seg": 77.02166, "decode.hr.loss_seg": 0.09283, "decode.hr.acc_seg": 69.40988, "decode.logits": -1.72931, "src.loss_imnet_feat_dist": NaN, "style.loss_style_consistency": 0.04472, "time": 1.5488} {"mode": "train", "epoch": 1, "iter": 850, "lr": 3e-05, "memory": 26687, "data_time": 0.01725, 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