diff --git a/.gitattributes b/.gitattributes index 1ef325f1b111266a6b26e0196871bd78baa8c2f3..9d42e663cf16ff28ab448f9ab41567c4047b1ce8 100644 --- a/.gitattributes +++ b/.gitattributes @@ -57,3 +57,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +OVRSIS/eval-DLRSD/inference/sem_seg_predictions.json filter=lfs diff=lfs merge=lfs -text +OVRSIS/eval-flair/inference/sem_seg_predictions.json filter=lfs diff=lfs merge=lfs -text +OVRSIS/eval-iSAID/inference/sem_seg_predictions.json filter=lfs diff=lfs merge=lfs -text +OVRSIS/eval-LoveDA/inference/sem_seg_predictions.json filter=lfs diff=lfs merge=lfs -text +OVRSIS/eval-openearthmap/inference/sem_seg_predictions.json filter=lfs diff=lfs merge=lfs -text +OVRSIS/eval-Potsdam/inference/sem_seg_predictions.json filter=lfs diff=lfs merge=lfs -text +OVRSIS/eval-uavid/inference/sem_seg_predictions.json filter=lfs diff=lfs merge=lfs -text +OVRSIS/eval-UDD5/inference/sem_seg_predictions.json filter=lfs diff=lfs merge=lfs -text +OVRSIS/eval-VDD/inference/sem_seg_predictions.json filter=lfs diff=lfs merge=lfs -text diff --git a/OVRSIS/eval-DLRSD/config.yaml b/OVRSIS/eval-DLRSD/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4c3089ff97cb8b1d6a7b2e19f824b472baa868ca --- /dev/null +++ b/OVRSIS/eval-DLRSD/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - DLRSD_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/DLRSD.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-DLRSD +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-DLRSD/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-DLRSD/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-DLRSD/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-DLRSD/inference/sem_seg_evaluation.pth b/OVRSIS/eval-DLRSD/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..d41f02c3cd32025b83dc12feff04f25dacf7a767 --- /dev/null +++ b/OVRSIS/eval-DLRSD/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b78bdab93a613300577c71e5b1a01d0243274f1e98aa52cd6a4bc9caffedf872 +size 5792 diff --git a/OVRSIS/eval-DLRSD/inference/sem_seg_predictions.json b/OVRSIS/eval-DLRSD/inference/sem_seg_predictions.json new file mode 100644 index 0000000000000000000000000000000000000000..ad4c0bb6976b8e2962438ffd10407ecc83fce562 --- /dev/null +++ b/OVRSIS/eval-DLRSD/inference/sem_seg_predictions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2f9ce018c73a4c5d222b0d28b4120997307285e08d9ccae60a5daca62b5a1f3b +size 47473684 diff --git a/OVRSIS/eval-DLRSD/log.txt b/OVRSIS/eval-DLRSD/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..4e440f42f6e800dc22021e99d3630a5aaddc8b17 --- /dev/null +++ b/OVRSIS/eval-DLRSD/log.txt @@ -0,0 +1,1018 @@ +[03/31 22:00:21] detectron2 INFO: Rank of current process: 0. World size: 4 +[03/31 22:00:23] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:00:23] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-DLRSD', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/DLRSD.json', 'DATASETS.TEST', '("DLRSD_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:00:23] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:00:23] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - DLRSD_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/DLRSD.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-DLRSD +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 22:00:23] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-DLRSD/config.yaml +[03/31 22:00:23] d2.utils.env INFO: Using a generated random seed 27234973 +[03/31 22:00:25] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:00:25] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:00:25] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:00:26] d2.data.datasets.coco INFO: Loaded 7002 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/DLRSD/imgs +[03/31 22:00:26] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:00:26] d2.data.common INFO: Serializing the dataset using: +[03/31 22:00:26] d2.data.common INFO: Serializing 7002 elements to byte tensors and concatenating them all ... +[03/31 22:00:26] d2.data.common INFO: Serialized dataset takes 1.68 MiB +[03/31 22:00:26] d2.data.datasets.coco INFO: Loaded 7002 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/DLRSD/imgs +[03/31 22:00:26] d2.evaluation.evaluator INFO: Start inference on 1751 batches +[03/31 22:00:33] d2.evaluation.evaluator INFO: Inference done 11/1751. Dataloading: 0.0007 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0685 s/iter. ETA=0:01:59 +[03/31 22:00:38] d2.evaluation.evaluator INFO: Inference done 84/1751. Dataloading: 0.0012 s/iter. Inference: 0.0648 s/iter. Eval: 0.0026 s/iter. Total: 0.0687 s/iter. ETA=0:01:54 +[03/31 22:00:43] d2.evaluation.evaluator INFO: Inference done 156/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0028 s/iter. Total: 0.0691 s/iter. ETA=0:01:50 +[03/31 22:00:48] d2.evaluation.evaluator INFO: Inference done 229/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0028 s/iter. Total: 0.0691 s/iter. ETA=0:01:45 +[03/31 22:00:53] d2.evaluation.evaluator INFO: Inference done 301/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0029 s/iter. Total: 0.0692 s/iter. ETA=0:01:40 +[03/31 22:00:58] d2.evaluation.evaluator INFO: Inference done 374/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0029 s/iter. Total: 0.0692 s/iter. ETA=0:01:35 +[03/31 22:01:03] d2.evaluation.evaluator INFO: Inference done 447/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:01:30 +[03/31 22:01:08] d2.evaluation.evaluator INFO: Inference done 519/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:01:25 +[03/31 22:01:13] d2.evaluation.evaluator INFO: Inference done 592/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:01:20 +[03/31 22:01:18] d2.evaluation.evaluator INFO: Inference done 664/1751. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0030 s/iter. Total: 0.0694 s/iter. ETA=0:01:15 +[03/31 22:01:23] d2.evaluation.evaluator INFO: Inference done 737/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:01:10 +[03/31 22:01:28] d2.evaluation.evaluator INFO: Inference done 810/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:01:05 +[03/31 22:01:33] d2.evaluation.evaluator INFO: Inference done 882/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:01:00 +[03/31 22:01:38] d2.evaluation.evaluator INFO: Inference done 955/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:55 +[03/31 22:01:44] d2.evaluation.evaluator INFO: Inference done 1028/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:50 +[03/31 22:01:49] d2.evaluation.evaluator INFO: Inference done 1101/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:45 +[03/31 22:01:54] d2.evaluation.evaluator INFO: Inference done 1174/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:40 +[03/31 22:01:59] d2.evaluation.evaluator INFO: Inference done 1247/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:34 +[03/31 22:02:04] d2.evaluation.evaluator INFO: Inference done 1320/1751. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:29 +[03/31 22:02:09] d2.evaluation.evaluator INFO: Inference done 1392/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:24 +[03/31 22:02:14] d2.evaluation.evaluator INFO: Inference done 1463/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:00:19 +[03/31 22:02:19] d2.evaluation.evaluator INFO: Inference done 1536/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:00:14 +[03/31 22:02:24] d2.evaluation.evaluator INFO: Inference done 1608/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:00:09 +[03/31 22:02:29] d2.evaluation.evaluator INFO: Inference done 1680/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:00:04 +[03/31 22:02:35] d2.evaluation.evaluator INFO: Total inference time: 0:02:02.087965 (0.069924 s / iter per device, on 4 devices) +[03/31 22:02:35] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:01:53 (0.064822 s / iter per device, on 4 devices) +[03/31 22:02:35] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 39.694122122378744, 'fwIoU': 50.05796250297351, 'IoU-airplane': 48.17838479855261, 'BoundaryIoU-airplane': 71.59677724452919, 'min(IoU, B-Iou)-airplane': 48.17838479855261, 'IoU-bare soil': 27.509514102339022, 'BoundaryIoU-bare soil': 10.897915579614274, 'min(IoU, B-Iou)-bare soil': 10.897915579614274, 'IoU-buildings': 56.41722143188622, 'BoundaryIoU-buildings': 29.222446764386632, 'min(IoU, B-Iou)-buildings': 29.222446764386632, 'IoU-cars': 59.24740806813252, 'BoundaryIoU-cars': 8.394931188145902, 'min(IoU, B-Iou)-cars': 8.394931188145902, 'IoU-chaparral': 0.25538529868976234, 'BoundaryIoU-chaparral': 1.9178153439635952, 'min(IoU, B-Iou)-chaparral': 0.25538529868976234, 'IoU-court': 51.739364130369694, 'BoundaryIoU-court': 12.66537727793301, 'min(IoU, B-Iou)-court': 12.66537727793301, 'IoU-dock': 42.76500356714381, 'BoundaryIoU-dock': 13.245722358947152, 'min(IoU, B-Iou)-dock': 13.245722358947152, 'IoU-field': 4.141520448986045, 'BoundaryIoU-field': 26.06692706901061, 'min(IoU, B-Iou)-field': 4.141520448986045, 'IoU-grass': 45.96083641389744, 'BoundaryIoU-grass': 25.132430189768844, 'min(IoU, B-Iou)-grass': 25.132430189768844, 'IoU-mobile home': 11.712842866819887, 'BoundaryIoU-mobile home': 8.782643392401297, 'min(IoU, B-Iou)-mobile home': 8.782643392401297, 'IoU-pavement': 66.70395104276578, 'BoundaryIoU-pavement': 49.45874446693043, 'min(IoU, B-Iou)-pavement': 49.45874446693043, 'IoU-sand': 27.7245978697814, 'BoundaryIoU-sand': 9.187881493152798, 'min(IoU, B-Iou)-sand': 9.187881493152798, 'IoU-sea': 34.90516677086709, 'BoundaryIoU-sea': 11.964818091763405, 'min(IoU, B-Iou)-sea': 11.964818091763405, 'IoU-ship': 70.6704921350994, 'BoundaryIoU-ship': 22.065312587867915, 'min(IoU, B-Iou)-ship': 22.065312587867915, 'IoU-tanks': 16.990841527585278, 'BoundaryIoU-tanks': 0.0, 'min(IoU, B-Iou)-tanks': 0.0, 'IoU-trees': 57.40050767501119, 'BoundaryIoU-trees': 0.0, 'min(IoU, B-Iou)-trees': 0.0, 'IoU-water': 52.47703793251149, 'BoundaryIoU-water': 0.0, 'min(IoU, B-Iou)-water': 0.0, 'mACC': 58.32826124662244, 'pACC': 65.59403132656853, 'ACC-airplane': 97.5333905054639, 'ACC-bare soil': 36.56578261270926, 'ACC-buildings': 80.53829846388662, 'ACC-cars': 76.44930544612532, 'ACC-chaparral': 0.27993251497857086, 'ACC-court': 56.05143338222085, 'ACC-dock': 73.33579730527781, 'ACC-field': 68.78824874702714, 'ACC-grass': 61.05679241384213, 'ACC-mobile home': 21.254290884567794, 'ACC-pavement': 83.63515147477266, 'ACC-sand': 44.14223531590902, 'ACC-sea': 37.47438850622617, 'ACC-ship': 83.2035837490788, 'ACC-tanks': 17.168428149252065, 'ACC-trees': 69.03683387843557, 'ACC-water': 85.06654784280792})]) +[03/31 22:02:35] d2.engine.defaults INFO: Evaluation results for DLRSD_all_sem_seg in csv format: +[03/31 22:02:35] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:02:35] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:02:35] d2.evaluation.testing INFO: copypaste: 39.6941,50.0580,58.3283,65.5940 diff --git a/OVRSIS/eval-DLRSD/log.txt.rank1 b/OVRSIS/eval-DLRSD/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..2ab2c1bbba12edd6b68f8ed7acedba751c39824b --- /dev/null +++ b/OVRSIS/eval-DLRSD/log.txt.rank1 @@ -0,0 +1,587 @@ +[03/31 22:00:21] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 22:00:22] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:00:22] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-DLRSD', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/DLRSD.json', 'DATASETS.TEST', '("DLRSD_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:00:22] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:00:22] detectron2.utils.env INFO: Using a generated random seed 26617571 +[03/31 22:00:25] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:00:25] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:00:25] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:00:26] detectron2.data.datasets.coco INFO: Loaded 7002 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/DLRSD/imgs +[03/31 22:00:26] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:00:26] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:00:26] detectron2.data.common INFO: Serializing 7002 elements to byte tensors and concatenating them all ... +[03/31 22:00:26] detectron2.data.common INFO: Serialized dataset takes 1.68 MiB +[03/31 22:00:26] detectron2.data.datasets.coco INFO: Loaded 7002 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/DLRSD/imgs +[03/31 22:00:26] detectron2.evaluation.evaluator INFO: Start inference on 1751 batches +[03/31 22:00:33] detectron2.evaluation.evaluator INFO: Inference done 11/1751. Dataloading: 0.0008 s/iter. Inference: 0.0647 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:01:58 +[03/31 22:00:38] detectron2.evaluation.evaluator INFO: Inference done 84/1751. Dataloading: 0.0012 s/iter. Inference: 0.0646 s/iter. Eval: 0.0031 s/iter. Total: 0.0689 s/iter. ETA=0:01:54 +[03/31 22:00:43] detectron2.evaluation.evaluator INFO: Inference done 157/1751. Dataloading: 0.0013 s/iter. Inference: 0.0646 s/iter. Eval: 0.0032 s/iter. Total: 0.0691 s/iter. ETA=0:01:50 +[03/31 22:00:48] detectron2.evaluation.evaluator INFO: Inference done 230/1751. Dataloading: 0.0013 s/iter. Inference: 0.0645 s/iter. Eval: 0.0032 s/iter. Total: 0.0691 s/iter. ETA=0:01:45 +[03/31 22:00:53] detectron2.evaluation.evaluator INFO: Inference done 303/1751. Dataloading: 0.0014 s/iter. Inference: 0.0646 s/iter. Eval: 0.0032 s/iter. Total: 0.0692 s/iter. ETA=0:01:40 +[03/31 22:00:58] detectron2.evaluation.evaluator INFO: Inference done 376/1751. Dataloading: 0.0014 s/iter. Inference: 0.0646 s/iter. Eval: 0.0032 s/iter. Total: 0.0692 s/iter. ETA=0:01:35 +[03/31 22:01:03] detectron2.evaluation.evaluator INFO: Inference done 449/1751. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0692 s/iter. ETA=0:01:30 +[03/31 22:01:08] detectron2.evaluation.evaluator INFO: Inference done 521/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:01:25 +[03/31 22:01:13] detectron2.evaluation.evaluator INFO: Inference done 593/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:01:20 +[03/31 22:01:18] detectron2.evaluation.evaluator INFO: Inference done 665/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:01:15 +[03/31 22:01:23] detectron2.evaluation.evaluator INFO: Inference done 738/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:01:10 +[03/31 22:01:29] detectron2.evaluation.evaluator INFO: Inference done 811/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:01:05 +[03/31 22:01:34] detectron2.evaluation.evaluator INFO: Inference done 883/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:01:00 +[03/31 22:01:39] detectron2.evaluation.evaluator INFO: Inference done 956/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:55 +[03/31 22:01:44] detectron2.evaluation.evaluator INFO: Inference done 1029/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:00:50 +[03/31 22:01:49] detectron2.evaluation.evaluator INFO: Inference done 1102/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:00:44 +[03/31 22:01:54] detectron2.evaluation.evaluator INFO: Inference done 1175/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:00:39 +[03/31 22:01:59] detectron2.evaluation.evaluator INFO: Inference done 1247/1751. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:00:34 +[03/31 22:02:04] detectron2.evaluation.evaluator INFO: Inference done 1320/1751. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:00:29 +[03/31 22:02:09] detectron2.evaluation.evaluator INFO: Inference done 1392/1751. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0030 s/iter. Total: 0.0694 s/iter. ETA=0:00:24 +[03/31 22:02:14] detectron2.evaluation.evaluator INFO: Inference done 1462/1751. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0031 s/iter. Total: 0.0695 s/iter. ETA=0:00:20 +[03/31 22:02:19] detectron2.evaluation.evaluator INFO: Inference done 1535/1751. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0031 s/iter. Total: 0.0695 s/iter. ETA=0:00:15 +[03/31 22:02:24] detectron2.evaluation.evaluator INFO: Inference done 1608/1751. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0031 s/iter. Total: 0.0695 s/iter. ETA=0:00:09 +[03/31 22:02:29] detectron2.evaluation.evaluator INFO: Inference done 1680/1751. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0030 s/iter. Total: 0.0695 s/iter. ETA=0:00:04 +[03/31 22:02:35] detectron2.evaluation.evaluator INFO: Total inference time: 0:02:02.179808 (0.069977 s / iter per device, on 4 devices) +[03/31 22:02:35] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:01:53 (0.064949 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-DLRSD/log.txt.rank2 b/OVRSIS/eval-DLRSD/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..118ce3576e00d4482cbe1a11a447a119394b4399 --- /dev/null +++ b/OVRSIS/eval-DLRSD/log.txt.rank2 @@ -0,0 +1,587 @@ +[03/31 22:00:21] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 22:00:23] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:00:23] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-DLRSD', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/DLRSD.json', 'DATASETS.TEST', '("DLRSD_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:00:23] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:00:23] detectron2.utils.env INFO: Using a generated random seed 27168754 +[03/31 22:00:25] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:00:25] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:00:25] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:00:26] detectron2.data.datasets.coco INFO: Loaded 7002 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/DLRSD/imgs +[03/31 22:00:26] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:00:26] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:00:26] detectron2.data.common INFO: Serializing 7002 elements to byte tensors and concatenating them all ... +[03/31 22:00:26] detectron2.data.common INFO: Serialized dataset takes 1.68 MiB +[03/31 22:00:26] detectron2.data.datasets.coco INFO: Loaded 7002 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/DLRSD/imgs +[03/31 22:00:26] detectron2.evaluation.evaluator INFO: Start inference on 1750 batches +[03/31 22:00:33] detectron2.evaluation.evaluator INFO: Inference done 11/1750. Dataloading: 0.0007 s/iter. Inference: 0.0645 s/iter. Eval: 0.0022 s/iter. Total: 0.0674 s/iter. ETA=0:01:57 +[03/31 22:00:38] detectron2.evaluation.evaluator INFO: Inference done 84/1750. Dataloading: 0.0012 s/iter. Inference: 0.0645 s/iter. Eval: 0.0030 s/iter. Total: 0.0688 s/iter. ETA=0:01:54 +[03/31 22:00:43] detectron2.evaluation.evaluator INFO: Inference done 157/1750. Dataloading: 0.0013 s/iter. Inference: 0.0646 s/iter. Eval: 0.0031 s/iter. Total: 0.0691 s/iter. ETA=0:01:50 +[03/31 22:00:48] detectron2.evaluation.evaluator INFO: Inference done 230/1750. Dataloading: 0.0014 s/iter. Inference: 0.0646 s/iter. Eval: 0.0031 s/iter. Total: 0.0691 s/iter. ETA=0:01:45 +[03/31 22:00:53] detectron2.evaluation.evaluator INFO: Inference done 303/1750. Dataloading: 0.0014 s/iter. Inference: 0.0646 s/iter. Eval: 0.0031 s/iter. Total: 0.0692 s/iter. ETA=0:01:40 +[03/31 22:00:58] detectron2.evaluation.evaluator INFO: Inference done 376/1750. Dataloading: 0.0014 s/iter. Inference: 0.0646 s/iter. Eval: 0.0031 s/iter. Total: 0.0691 s/iter. ETA=0:01:34 +[03/31 22:01:03] detectron2.evaluation.evaluator INFO: Inference done 449/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0030 s/iter. Total: 0.0692 s/iter. ETA=0:01:29 +[03/31 22:01:08] detectron2.evaluation.evaluator INFO: Inference done 521/1750. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0030 s/iter. Total: 0.0693 s/iter. ETA=0:01:25 +[03/31 22:01:13] detectron2.evaluation.evaluator INFO: Inference done 594/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0692 s/iter. ETA=0:01:20 +[03/31 22:01:18] detectron2.evaluation.evaluator INFO: Inference done 666/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:01:15 +[03/31 22:01:23] detectron2.evaluation.evaluator INFO: Inference done 739/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:01:10 +[03/31 22:01:28] detectron2.evaluation.evaluator INFO: Inference done 811/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:01:05 +[03/31 22:01:33] detectron2.evaluation.evaluator INFO: Inference done 883/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:01:00 +[03/31 22:01:38] detectron2.evaluation.evaluator INFO: Inference done 956/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:55 +[03/31 22:01:44] detectron2.evaluation.evaluator INFO: Inference done 1029/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:49 +[03/31 22:01:49] detectron2.evaluation.evaluator INFO: Inference done 1102/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:44 +[03/31 22:01:54] detectron2.evaluation.evaluator INFO: Inference done 1175/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:39 +[03/31 22:01:59] detectron2.evaluation.evaluator INFO: Inference done 1247/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:34 +[03/31 22:02:04] detectron2.evaluation.evaluator INFO: Inference done 1320/1750. Dataloading: 0.0014 s/iter. Inference: 0.0647 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:29 +[03/31 22:02:09] detectron2.evaluation.evaluator INFO: Inference done 1392/1750. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0693 s/iter. ETA=0:00:24 +[03/31 22:02:14] detectron2.evaluation.evaluator INFO: Inference done 1463/1750. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:00:19 +[03/31 22:02:19] detectron2.evaluation.evaluator INFO: Inference done 1536/1750. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:00:14 +[03/31 22:02:24] detectron2.evaluation.evaluator INFO: Inference done 1608/1750. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:00:09 +[03/31 22:02:29] detectron2.evaluation.evaluator INFO: Inference done 1681/1750. Dataloading: 0.0014 s/iter. Inference: 0.0648 s/iter. Eval: 0.0031 s/iter. Total: 0.0694 s/iter. ETA=0:00:04 +[03/31 22:02:35] detectron2.evaluation.evaluator INFO: Total inference time: 0:02:02.053521 (0.069945 s / iter per device, on 4 devices) +[03/31 22:02:35] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:01:53 (0.064813 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-DLRSD/log.txt.rank3 b/OVRSIS/eval-DLRSD/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..581b9e9305d2bf4a4a4d7107ca6c2af9b17ea1f6 --- /dev/null +++ b/OVRSIS/eval-DLRSD/log.txt.rank3 @@ -0,0 +1,587 @@ +[03/31 22:00:21] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 22:00:22] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:00:22] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-DLRSD', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/DLRSD.json', 'DATASETS.TEST', '("DLRSD_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:00:22] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:00:22] detectron2.utils.env INFO: Using a generated random seed 26593116 +[03/31 22:00:25] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:00:25] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:00:25] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:00:26] detectron2.data.datasets.coco INFO: Loaded 7002 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/DLRSD/imgs +[03/31 22:00:26] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:00:26] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:00:26] detectron2.data.common INFO: Serializing 7002 elements to byte tensors and concatenating them all ... +[03/31 22:00:26] detectron2.data.common INFO: Serialized dataset takes 1.68 MiB +[03/31 22:00:26] detectron2.data.datasets.coco INFO: Loaded 7002 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/DLRSD/imgs +[03/31 22:00:26] detectron2.evaluation.evaluator INFO: Start inference on 1750 batches +[03/31 22:00:33] detectron2.evaluation.evaluator INFO: Inference done 11/1750. Dataloading: 0.0007 s/iter. Inference: 0.0648 s/iter. Eval: 0.0034 s/iter. Total: 0.0688 s/iter. ETA=0:01:59 +[03/31 22:00:38] detectron2.evaluation.evaluator INFO: Inference done 84/1750. Dataloading: 0.0012 s/iter. Inference: 0.0646 s/iter. Eval: 0.0028 s/iter. Total: 0.0687 s/iter. ETA=0:01:54 +[03/31 22:00:43] detectron2.evaluation.evaluator INFO: Inference done 156/1750. Dataloading: 0.0013 s/iter. Inference: 0.0648 s/iter. Eval: 0.0029 s/iter. Total: 0.0691 s/iter. ETA=0:01:50 +[03/31 22:00:48] detectron2.evaluation.evaluator INFO: Inference done 229/1750. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0028 s/iter. Total: 0.0691 s/iter. ETA=0:01:45 +[03/31 22:00:53] detectron2.evaluation.evaluator INFO: Inference done 301/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0027 s/iter. Total: 0.0692 s/iter. ETA=0:01:40 +[03/31 22:00:58] detectron2.evaluation.evaluator INFO: Inference done 374/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0027 s/iter. Total: 0.0691 s/iter. ETA=0:01:35 +[03/31 22:01:03] detectron2.evaluation.evaluator INFO: Inference done 447/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0027 s/iter. Total: 0.0691 s/iter. ETA=0:01:30 +[03/31 22:01:08] detectron2.evaluation.evaluator INFO: Inference done 519/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0028 s/iter. Total: 0.0692 s/iter. ETA=0:01:25 +[03/31 22:01:13] detectron2.evaluation.evaluator INFO: Inference done 592/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0028 s/iter. Total: 0.0693 s/iter. ETA=0:01:20 +[03/31 22:01:18] detectron2.evaluation.evaluator INFO: Inference done 664/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0028 s/iter. Total: 0.0693 s/iter. ETA=0:01:15 +[03/31 22:01:23] detectron2.evaluation.evaluator INFO: Inference done 737/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0028 s/iter. Total: 0.0693 s/iter. ETA=0:01:10 +[03/31 22:01:28] detectron2.evaluation.evaluator INFO: Inference done 810/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0028 s/iter. Total: 0.0693 s/iter. ETA=0:01:05 +[03/31 22:01:33] detectron2.evaluation.evaluator INFO: Inference done 882/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0028 s/iter. Total: 0.0693 s/iter. ETA=0:01:00 +[03/31 22:01:38] detectron2.evaluation.evaluator INFO: Inference done 955/1750. Dataloading: 0.0014 s/iter. Inference: 0.0650 s/iter. Eval: 0.0029 s/iter. Total: 0.0693 s/iter. ETA=0:00:55 +[03/31 22:01:43] detectron2.evaluation.evaluator INFO: Inference done 1027/1750. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0029 s/iter. Total: 0.0693 s/iter. ETA=0:00:50 +[03/31 22:01:48] detectron2.evaluation.evaluator INFO: Inference done 1100/1750. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0029 s/iter. Total: 0.0693 s/iter. ETA=0:00:45 +[03/31 22:01:53] detectron2.evaluation.evaluator INFO: Inference done 1173/1750. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0029 s/iter. Total: 0.0693 s/iter. ETA=0:00:39 +[03/31 22:01:58] detectron2.evaluation.evaluator INFO: Inference done 1246/1750. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0029 s/iter. Total: 0.0693 s/iter. ETA=0:00:34 +[03/31 22:02:03] detectron2.evaluation.evaluator INFO: Inference done 1319/1750. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0029 s/iter. Total: 0.0693 s/iter. ETA=0:00:29 +[03/31 22:02:08] detectron2.evaluation.evaluator INFO: Inference done 1391/1750. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0029 s/iter. Total: 0.0693 s/iter. ETA=0:00:24 +[03/31 22:02:14] detectron2.evaluation.evaluator INFO: Inference done 1462/1750. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0030 s/iter. Total: 0.0694 s/iter. ETA=0:00:19 +[03/31 22:02:19] detectron2.evaluation.evaluator INFO: Inference done 1535/1750. Dataloading: 0.0014 s/iter. Inference: 0.0649 s/iter. Eval: 0.0030 s/iter. Total: 0.0694 s/iter. ETA=0:00:14 +[03/31 22:02:24] detectron2.evaluation.evaluator INFO: Inference done 1607/1750. Dataloading: 0.0015 s/iter. Inference: 0.0649 s/iter. Eval: 0.0030 s/iter. Total: 0.0694 s/iter. ETA=0:00:09 +[03/31 22:02:29] detectron2.evaluation.evaluator INFO: Inference done 1680/1750. Dataloading: 0.0015 s/iter. Inference: 0.0649 s/iter. Eval: 0.0030 s/iter. Total: 0.0694 s/iter. ETA=0:00:04 +[03/31 22:02:35] detectron2.evaluation.evaluator INFO: Total inference time: 0:02:02.181478 (0.070018 s / iter per device, on 4 devices) +[03/31 22:02:35] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:01:53 (0.064931 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-LoveDA/config.yaml b/OVRSIS/eval-LoveDA/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..896a7537f8ed6343a6f9787c0f1fd16af5bc4a64 --- /dev/null +++ b/OVRSIS/eval-LoveDA/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - LoveDA_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/LoveDA.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-LoveDA +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-LoveDA/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-LoveDA/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-LoveDA/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-LoveDA/inference/sem_seg_evaluation.pth b/OVRSIS/eval-LoveDA/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..a078a3d30b96e6cefbfdc62dfb0e31d5b1b55616 --- /dev/null +++ b/OVRSIS/eval-LoveDA/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37cbc205a0defd29081d4dc998b4bf21078cfedeff812adbbb351a7d3a6b2bbb +size 2912 diff --git a/OVRSIS/eval-LoveDA/inference/sem_seg_predictions.json b/OVRSIS/eval-LoveDA/inference/sem_seg_predictions.json new file mode 100644 index 0000000000000000000000000000000000000000..0573e778a43fe25acc3edecf206cc570228791b7 --- /dev/null +++ b/OVRSIS/eval-LoveDA/inference/sem_seg_predictions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d068f3112298a98fa4ad06d644da5c452fc375c25d5e7efeedda61b84abece68 +size 84235991 diff --git a/OVRSIS/eval-LoveDA/log.txt b/OVRSIS/eval-LoveDA/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b85c51100617020fe4075712b0553ce16c4e1d1 --- /dev/null +++ b/OVRSIS/eval-LoveDA/log.txt @@ -0,0 +1,1007 @@ +[03/31 22:10:27] detectron2 INFO: Rank of current process: 0. World size: 4 +[03/31 22:10:29] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:10:29] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-LoveDA', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/LoveDA.json', 'DATASETS.TEST', '("LoveDA_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:10:29] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:10:29] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - LoveDA_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/LoveDA.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-LoveDA +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 22:10:29] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-LoveDA/config.yaml +[03/31 22:10:29] d2.utils.env INFO: Using a generated random seed 33973010 +[03/31 22:10:32] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:10:32] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:10:32] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:10:33] d2.data.datasets.coco INFO: Loaded 2522 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/LoveDA/images_png +[03/31 22:10:33] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:10:33] d2.data.common INFO: Serializing the dataset using: +[03/31 22:10:33] d2.data.common INFO: Serializing 2522 elements to byte tensors and concatenating them all ... +[03/31 22:10:33] d2.data.common INFO: Serialized dataset takes 0.60 MiB +[03/31 22:10:33] d2.data.datasets.coco INFO: Loaded 2522 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/LoveDA/images_png +[03/31 22:10:33] d2.evaluation.evaluator INFO: Start inference on 631 batches +[03/31 22:10:40] d2.evaluation.evaluator INFO: Inference done 11/631. Dataloading: 0.0013 s/iter. Inference: 0.0621 s/iter. Eval: 0.0361 s/iter. Total: 0.0996 s/iter. ETA=0:01:01 +[03/31 22:10:45] d2.evaluation.evaluator INFO: Inference done 62/631. Dataloading: 0.0015 s/iter. Inference: 0.0606 s/iter. Eval: 0.0379 s/iter. Total: 0.1000 s/iter. ETA=0:00:56 +[03/31 22:10:51] d2.evaluation.evaluator INFO: Inference done 112/631. Dataloading: 0.0015 s/iter. Inference: 0.0610 s/iter. Eval: 0.0383 s/iter. Total: 0.1008 s/iter. ETA=0:00:52 +[03/31 22:10:56] d2.evaluation.evaluator INFO: Inference done 164/631. Dataloading: 0.0016 s/iter. Inference: 0.0608 s/iter. Eval: 0.0375 s/iter. Total: 0.0999 s/iter. ETA=0:00:46 +[03/31 22:11:01] d2.evaluation.evaluator INFO: Inference done 214/631. Dataloading: 0.0016 s/iter. Inference: 0.0607 s/iter. Eval: 0.0376 s/iter. Total: 0.1000 s/iter. ETA=0:00:41 +[03/31 22:11:06] d2.evaluation.evaluator INFO: Inference done 265/631. Dataloading: 0.0016 s/iter. Inference: 0.0608 s/iter. Eval: 0.0375 s/iter. Total: 0.0999 s/iter. ETA=0:00:36 +[03/31 22:11:11] d2.evaluation.evaluator INFO: Inference done 315/631. Dataloading: 0.0016 s/iter. Inference: 0.0608 s/iter. Eval: 0.0375 s/iter. Total: 0.1000 s/iter. ETA=0:00:31 +[03/31 22:11:16] d2.evaluation.evaluator INFO: Inference done 365/631. Dataloading: 0.0016 s/iter. Inference: 0.0608 s/iter. Eval: 0.0376 s/iter. Total: 0.1001 s/iter. ETA=0:00:26 +[03/31 22:11:21] d2.evaluation.evaluator INFO: Inference done 416/631. Dataloading: 0.0016 s/iter. Inference: 0.0607 s/iter. Eval: 0.0377 s/iter. Total: 0.1001 s/iter. ETA=0:00:21 +[03/31 22:11:26] d2.evaluation.evaluator INFO: Inference done 464/631. Dataloading: 0.0016 s/iter. Inference: 0.0607 s/iter. Eval: 0.0382 s/iter. Total: 0.1006 s/iter. ETA=0:00:16 +[03/31 22:11:31] d2.evaluation.evaluator INFO: Inference done 512/631. Dataloading: 0.0016 s/iter. Inference: 0.0607 s/iter. Eval: 0.0386 s/iter. Total: 0.1010 s/iter. ETA=0:00:12 +[03/31 22:11:36] d2.evaluation.evaluator INFO: Inference done 558/631. Dataloading: 0.0016 s/iter. Inference: 0.0607 s/iter. Eval: 0.0392 s/iter. Total: 0.1018 s/iter. ETA=0:00:07 +[03/31 22:11:41] d2.evaluation.evaluator INFO: Inference done 603/631. Dataloading: 0.0016 s/iter. Inference: 0.0607 s/iter. Eval: 0.0400 s/iter. Total: 0.1025 s/iter. ETA=0:00:02 +[03/31 22:11:45] d2.evaluation.evaluator INFO: Total inference time: 0:01:05.413807 (0.104495 s / iter per device, on 4 devices) +[03/31 22:11:45] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:38 (0.060743 s / iter per device, on 4 devices) +[03/31 22:11:47] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 45.92044085159198, 'fwIoU': 45.942286755992725, 'IoU-Background': 39.179939524723984, 'BoundaryIoU-Background': 64.32442261632254, 'min(IoU, B-Iou)-Background': 39.179939524723984, 'IoU-Building': 52.976047436097915, 'BoundaryIoU-Building': 33.383799697019704, 'min(IoU, B-Iou)-Building': 33.383799697019704, 'IoU-Road': 48.34714019911621, 'BoundaryIoU-Road': 33.28580561650003, 'min(IoU, B-Iou)-Road': 33.28580561650003, 'IoU-Water': 69.32989861454489, 'BoundaryIoU-Water': 26.62519430310365, 'min(IoU, B-Iou)-Water': 26.62519430310365, 'IoU-Barren': 5.724441964655022, 'BoundaryIoU-Barren': 8.360679559452665, 'min(IoU, B-Iou)-Barren': 5.724441964655022, 'IoU-Forest': 56.74764607592222, 'BoundaryIoU-Forest': 24.979097251516393, 'min(IoU, B-Iou)-Forest': 24.979097251516393, 'IoU-Agriculture Land': 49.137972146083634, 'BoundaryIoU-Agriculture Land': 22.017285664479914, 'min(IoU, B-Iou)-Agriculture Land': 22.017285664479914, 'mACC': 63.55563169115168, 'pACC': 63.62722610122717, 'ACC-Background': 46.61009102745075, 'ACC-Building': 72.64798044644188, 'ACC-Road': 78.5683563190081, 'ACC-Water': 74.79706460299639, 'ACC-Barren': 6.085867842914835, 'ACC-Forest': 78.27576423006106, 'ACC-Agriculture Land': 87.90429736918874})]) +[03/31 22:11:47] d2.engine.defaults INFO: Evaluation results for LoveDA_all_sem_seg in csv format: +[03/31 22:11:47] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:11:47] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:11:47] d2.evaluation.testing INFO: copypaste: 45.9204,45.9423,63.5556,63.6272 diff --git a/OVRSIS/eval-LoveDA/log.txt.rank1 b/OVRSIS/eval-LoveDA/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..aa45af894235c189cf1ba596b2ac571f5acfb629 --- /dev/null +++ b/OVRSIS/eval-LoveDA/log.txt.rank1 @@ -0,0 +1,576 @@ +[03/31 22:10:27] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 22:10:29] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:10:29] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-LoveDA', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/LoveDA.json', 'DATASETS.TEST', '("LoveDA_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:10:29] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:10:29] detectron2.utils.env INFO: Using a generated random seed 33245471 +[03/31 22:10:32] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:10:32] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:10:32] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:10:33] detectron2.data.datasets.coco INFO: Loaded 2522 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/LoveDA/images_png +[03/31 22:10:33] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:10:33] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:10:33] detectron2.data.common INFO: Serializing 2522 elements to byte tensors and concatenating them all ... +[03/31 22:10:33] detectron2.data.common INFO: Serialized dataset takes 0.60 MiB +[03/31 22:10:33] detectron2.data.datasets.coco INFO: Loaded 2522 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/LoveDA/images_png +[03/31 22:10:33] detectron2.evaluation.evaluator INFO: Start inference on 631 batches +[03/31 22:10:41] detectron2.evaluation.evaluator INFO: Inference done 11/631. Dataloading: 0.0014 s/iter. Inference: 0.0620 s/iter. Eval: 0.0390 s/iter. Total: 0.1024 s/iter. ETA=0:01:03 +[03/31 22:10:46] detectron2.evaluation.evaluator INFO: Inference done 59/631. Dataloading: 0.0015 s/iter. Inference: 0.0612 s/iter. Eval: 0.0427 s/iter. Total: 0.1055 s/iter. ETA=0:01:00 +[03/31 22:10:51] detectron2.evaluation.evaluator INFO: Inference done 108/631. Dataloading: 0.0016 s/iter. Inference: 0.0612 s/iter. Eval: 0.0415 s/iter. Total: 0.1044 s/iter. ETA=0:00:54 +[03/31 22:10:56] detectron2.evaluation.evaluator INFO: Inference done 156/631. Dataloading: 0.0016 s/iter. Inference: 0.0609 s/iter. Eval: 0.0419 s/iter. Total: 0.1045 s/iter. ETA=0:00:49 +[03/31 22:11:01] detectron2.evaluation.evaluator INFO: Inference done 203/631. Dataloading: 0.0016 s/iter. Inference: 0.0610 s/iter. Eval: 0.0427 s/iter. Total: 0.1054 s/iter. ETA=0:00:45 +[03/31 22:11:06] detectron2.evaluation.evaluator INFO: Inference done 251/631. Dataloading: 0.0016 s/iter. Inference: 0.0610 s/iter. Eval: 0.0427 s/iter. Total: 0.1053 s/iter. ETA=0:00:40 +[03/31 22:11:11] detectron2.evaluation.evaluator INFO: Inference done 299/631. Dataloading: 0.0016 s/iter. Inference: 0.0610 s/iter. Eval: 0.0427 s/iter. Total: 0.1053 s/iter. ETA=0:00:34 +[03/31 22:11:16] detectron2.evaluation.evaluator INFO: Inference done 347/631. Dataloading: 0.0016 s/iter. Inference: 0.0609 s/iter. Eval: 0.0425 s/iter. Total: 0.1051 s/iter. ETA=0:00:29 +[03/31 22:11:21] detectron2.evaluation.evaluator INFO: Inference done 395/631. Dataloading: 0.0016 s/iter. Inference: 0.0609 s/iter. Eval: 0.0425 s/iter. Total: 0.1051 s/iter. ETA=0:00:24 +[03/31 22:11:26] detectron2.evaluation.evaluator INFO: Inference done 443/631. Dataloading: 0.0016 s/iter. Inference: 0.0609 s/iter. Eval: 0.0426 s/iter. Total: 0.1052 s/iter. ETA=0:00:19 +[03/31 22:11:31] detectron2.evaluation.evaluator INFO: Inference done 491/631. Dataloading: 0.0016 s/iter. Inference: 0.0610 s/iter. Eval: 0.0426 s/iter. Total: 0.1052 s/iter. ETA=0:00:14 +[03/31 22:11:36] detectron2.evaluation.evaluator INFO: Inference done 540/631. Dataloading: 0.0016 s/iter. Inference: 0.0609 s/iter. Eval: 0.0425 s/iter. Total: 0.1050 s/iter. ETA=0:00:09 +[03/31 22:11:41] detectron2.evaluation.evaluator INFO: Inference done 588/631. Dataloading: 0.0016 s/iter. Inference: 0.0609 s/iter. Eval: 0.0425 s/iter. Total: 0.1050 s/iter. ETA=0:00:04 +[03/31 22:11:47] detectron2.evaluation.evaluator INFO: Total inference time: 0:01:06.452420 (0.106154 s / iter per device, on 4 devices) +[03/31 22:11:47] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:38 (0.060841 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-LoveDA/log.txt.rank2 b/OVRSIS/eval-LoveDA/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..c2b3a4605f12a467e62597799178252c65453bda --- /dev/null +++ b/OVRSIS/eval-LoveDA/log.txt.rank2 @@ -0,0 +1,576 @@ +[03/31 22:10:27] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 22:10:29] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:10:29] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-LoveDA', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/LoveDA.json', 'DATASETS.TEST', '("LoveDA_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:10:29] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:10:29] detectron2.utils.env INFO: Using a generated random seed 33912279 +[03/31 22:10:32] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:10:32] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:10:32] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:10:33] detectron2.data.datasets.coco INFO: Loaded 2522 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/LoveDA/images_png +[03/31 22:10:33] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:10:33] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:10:33] detectron2.data.common INFO: Serializing 2522 elements to byte tensors and concatenating them all ... +[03/31 22:10:33] detectron2.data.common INFO: Serialized dataset takes 0.60 MiB +[03/31 22:10:33] detectron2.data.datasets.coco INFO: Loaded 2522 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/LoveDA/images_png +[03/31 22:10:33] detectron2.evaluation.evaluator INFO: Start inference on 630 batches +[03/31 22:10:41] detectron2.evaluation.evaluator INFO: Inference done 11/630. Dataloading: 0.0011 s/iter. Inference: 0.0606 s/iter. Eval: 0.0399 s/iter. Total: 0.1017 s/iter. ETA=0:01:02 +[03/31 22:10:46] detectron2.evaluation.evaluator INFO: Inference done 59/630. Dataloading: 0.0015 s/iter. Inference: 0.0604 s/iter. Eval: 0.0433 s/iter. Total: 0.1053 s/iter. ETA=0:01:00 +[03/31 22:10:51] detectron2.evaluation.evaluator INFO: Inference done 107/630. Dataloading: 0.0016 s/iter. Inference: 0.0608 s/iter. Eval: 0.0428 s/iter. Total: 0.1053 s/iter. ETA=0:00:55 +[03/31 22:10:56] detectron2.evaluation.evaluator INFO: Inference done 154/630. Dataloading: 0.0016 s/iter. Inference: 0.0607 s/iter. Eval: 0.0433 s/iter. Total: 0.1056 s/iter. ETA=0:00:50 +[03/31 22:11:01] detectron2.evaluation.evaluator INFO: Inference done 201/630. Dataloading: 0.0017 s/iter. Inference: 0.0608 s/iter. Eval: 0.0437 s/iter. Total: 0.1062 s/iter. ETA=0:00:45 +[03/31 22:11:06] detectron2.evaluation.evaluator INFO: Inference done 249/630. Dataloading: 0.0017 s/iter. Inference: 0.0608 s/iter. Eval: 0.0435 s/iter. Total: 0.1060 s/iter. ETA=0:00:40 +[03/31 22:11:11] detectron2.evaluation.evaluator INFO: Inference done 297/630. Dataloading: 0.0017 s/iter. Inference: 0.0608 s/iter. Eval: 0.0435 s/iter. Total: 0.1060 s/iter. ETA=0:00:35 +[03/31 22:11:16] detectron2.evaluation.evaluator INFO: Inference done 346/630. Dataloading: 0.0017 s/iter. Inference: 0.0607 s/iter. Eval: 0.0430 s/iter. Total: 0.1054 s/iter. ETA=0:00:29 +[03/31 22:11:21] detectron2.evaluation.evaluator INFO: Inference done 393/630. Dataloading: 0.0017 s/iter. Inference: 0.0607 s/iter. Eval: 0.0432 s/iter. Total: 0.1057 s/iter. ETA=0:00:25 +[03/31 22:11:26] detectron2.evaluation.evaluator INFO: Inference done 441/630. Dataloading: 0.0017 s/iter. Inference: 0.0608 s/iter. Eval: 0.0431 s/iter. Total: 0.1056 s/iter. ETA=0:00:19 +[03/31 22:11:31] detectron2.evaluation.evaluator INFO: Inference done 492/630. Dataloading: 0.0017 s/iter. Inference: 0.0608 s/iter. Eval: 0.0425 s/iter. Total: 0.1050 s/iter. ETA=0:00:14 +[03/31 22:11:36] detectron2.evaluation.evaluator INFO: Inference done 541/630. Dataloading: 0.0017 s/iter. Inference: 0.0607 s/iter. Eval: 0.0423 s/iter. Total: 0.1047 s/iter. ETA=0:00:09 +[03/31 22:11:41] detectron2.evaluation.evaluator INFO: Inference done 590/630. Dataloading: 0.0017 s/iter. Inference: 0.0608 s/iter. Eval: 0.0421 s/iter. Total: 0.1046 s/iter. ETA=0:00:04 +[03/31 22:11:46] detectron2.evaluation.evaluator INFO: Total inference time: 0:01:06.030631 (0.105649 s / iter per device, on 4 devices) +[03/31 22:11:46] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:37 (0.060747 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-LoveDA/log.txt.rank3 b/OVRSIS/eval-LoveDA/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..5a4e69ccee2b0010e7400bb3ab618d83b7c72473 --- /dev/null +++ b/OVRSIS/eval-LoveDA/log.txt.rank3 @@ -0,0 +1,576 @@ +[03/31 22:10:27] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 22:10:29] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:10:29] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-LoveDA', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/LoveDA.json', 'DATASETS.TEST', '("LoveDA_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:10:29] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:10:29] detectron2.utils.env INFO: Using a generated random seed 33202045 +[03/31 22:10:32] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:10:32] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:10:32] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:10:33] detectron2.data.datasets.coco INFO: Loaded 2522 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/LoveDA/images_png +[03/31 22:10:33] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:10:33] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:10:33] detectron2.data.common INFO: Serializing 2522 elements to byte tensors and concatenating them all ... +[03/31 22:10:33] detectron2.data.common INFO: Serialized dataset takes 0.60 MiB +[03/31 22:10:33] detectron2.data.datasets.coco INFO: Loaded 2522 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/LoveDA/images_png +[03/31 22:10:33] detectron2.evaluation.evaluator INFO: Start inference on 630 batches +[03/31 22:10:41] detectron2.evaluation.evaluator INFO: Inference done 11/630. Dataloading: 0.0011 s/iter. Inference: 0.0651 s/iter. Eval: 0.0370 s/iter. Total: 0.1032 s/iter. ETA=0:01:03 +[03/31 22:10:46] detectron2.evaluation.evaluator INFO: Inference done 59/630. Dataloading: 0.0015 s/iter. Inference: 0.0622 s/iter. Eval: 0.0410 s/iter. Total: 0.1048 s/iter. ETA=0:00:59 +[03/31 22:10:51] detectron2.evaluation.evaluator INFO: Inference done 110/630. Dataloading: 0.0016 s/iter. Inference: 0.0617 s/iter. Eval: 0.0384 s/iter. Total: 0.1017 s/iter. ETA=0:00:52 +[03/31 22:10:56] detectron2.evaluation.evaluator INFO: Inference done 160/630. Dataloading: 0.0016 s/iter. Inference: 0.0612 s/iter. Eval: 0.0386 s/iter. Total: 0.1014 s/iter. ETA=0:00:47 +[03/31 22:11:01] detectron2.evaluation.evaluator INFO: Inference done 208/630. Dataloading: 0.0017 s/iter. Inference: 0.0612 s/iter. Eval: 0.0395 s/iter. Total: 0.1023 s/iter. ETA=0:00:43 +[03/31 22:11:06] detectron2.evaluation.evaluator INFO: Inference done 259/630. Dataloading: 0.0016 s/iter. Inference: 0.0611 s/iter. Eval: 0.0389 s/iter. Total: 0.1017 s/iter. ETA=0:00:37 +[03/31 22:11:11] detectron2.evaluation.evaluator INFO: Inference done 309/630. Dataloading: 0.0016 s/iter. Inference: 0.0611 s/iter. Eval: 0.0389 s/iter. Total: 0.1016 s/iter. ETA=0:00:32 +[03/31 22:11:16] detectron2.evaluation.evaluator INFO: Inference done 359/630. Dataloading: 0.0017 s/iter. Inference: 0.0611 s/iter. Eval: 0.0387 s/iter. Total: 0.1015 s/iter. ETA=0:00:27 +[03/31 22:11:21] detectron2.evaluation.evaluator INFO: Inference done 411/630. Dataloading: 0.0017 s/iter. Inference: 0.0611 s/iter. Eval: 0.0381 s/iter. Total: 0.1009 s/iter. ETA=0:00:22 +[03/31 22:11:26] detectron2.evaluation.evaluator INFO: Inference done 461/630. Dataloading: 0.0017 s/iter. Inference: 0.0611 s/iter. Eval: 0.0381 s/iter. Total: 0.1008 s/iter. ETA=0:00:17 +[03/31 22:11:31] detectron2.evaluation.evaluator INFO: Inference done 512/630. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 0.0380 s/iter. Total: 0.1007 s/iter. ETA=0:00:11 +[03/31 22:11:36] detectron2.evaluation.evaluator INFO: Inference done 562/630. Dataloading: 0.0016 s/iter. Inference: 0.0610 s/iter. Eval: 0.0380 s/iter. Total: 0.1006 s/iter. ETA=0:00:06 +[03/31 22:11:41] detectron2.evaluation.evaluator INFO: Inference done 612/630. Dataloading: 0.0016 s/iter. Inference: 0.0609 s/iter. Eval: 0.0380 s/iter. Total: 0.1006 s/iter. ETA=0:00:01 +[03/31 22:11:44] detectron2.evaluation.evaluator INFO: Total inference time: 0:01:03.567451 (0.101708 s / iter per device, on 4 devices) +[03/31 22:11:44] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:38 (0.060875 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-Potsdam/config.yaml b/OVRSIS/eval-Potsdam/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..27e7d6048bf25b61501fb673fa87d56cacc00fbe --- /dev/null +++ b/OVRSIS/eval-Potsdam/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - Potsdam_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Potsdam.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Potsdam +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-Potsdam/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-Potsdam/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-Potsdam/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-Potsdam/inference/sem_seg_evaluation.pth b/OVRSIS/eval-Potsdam/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..5e602d9474f94a6194d2ed496b1a5808db901062 --- /dev/null +++ b/OVRSIS/eval-Potsdam/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e4cdd846a4de824aff4fa25891e7a7d154ea2edfb99dc0de002b2e307982a44d +size 2720 diff --git a/OVRSIS/eval-Potsdam/inference/sem_seg_predictions.json b/OVRSIS/eval-Potsdam/inference/sem_seg_predictions.json new file mode 100644 index 0000000000000000000000000000000000000000..e273fc5f29984d5b40f6df09bd2c38cde45ce009 --- /dev/null +++ b/OVRSIS/eval-Potsdam/inference/sem_seg_predictions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15fefd1e638b2247d92982e07c0ecf3759d9d24dac59454e8bd8d2dfc72f2e1c +size 44462015 diff --git a/OVRSIS/eval-Potsdam/log.txt b/OVRSIS/eval-Potsdam/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf019fd0587f24912968ceabd6de56c64bea9f8d --- /dev/null +++ b/OVRSIS/eval-Potsdam/log.txt @@ -0,0 +1,1058 @@ +[03/31 22:02:45] detectron2 INFO: Rank of current process: 0. World size: 4 +[03/31 22:02:47] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:02:47] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Potsdam', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Potsdam.json', 'DATASETS.TEST', '("Potsdam_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:02:47] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:02:47] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - Potsdam_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Potsdam.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Potsdam +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 22:02:47] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Potsdam/config.yaml +[03/31 22:02:47] d2.utils.env INFO: Using a generated random seed 51154834 +[03/31 22:02:50] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:02:50] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:02:50] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:02:51] d2.data.datasets.coco INFO: Loaded 20102 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Potsdam/imgs +[03/31 22:02:51] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:02:51] d2.data.common INFO: Serializing the dataset using: +[03/31 22:02:51] d2.data.common INFO: Serializing 20102 elements to byte tensors and concatenating them all ... +[03/31 22:02:51] d2.data.common INFO: Serialized dataset takes 5.49 MiB +[03/31 22:02:51] d2.data.datasets.coco INFO: Loaded 20102 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Potsdam/imgs +[03/31 22:02:51] d2.evaluation.evaluator INFO: Start inference on 5026 batches +[03/31 22:02:58] d2.evaluation.evaluator INFO: Inference done 11/5026. Dataloading: 0.0005 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0624 s/iter. ETA=0:05:12 +[03/31 22:03:03] d2.evaluation.evaluator INFO: Inference done 91/5026. Dataloading: 0.0013 s/iter. Inference: 0.0591 s/iter. Eval: 0.0028 s/iter. Total: 0.0632 s/iter. ETA=0:05:11 +[03/31 22:03:08] d2.evaluation.evaluator INFO: Inference done 170/5026. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:05:07 +[03/31 22:03:13] d2.evaluation.evaluator INFO: Inference done 250/5026. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0026 s/iter. Total: 0.0632 s/iter. ETA=0:05:01 +[03/31 22:03:18] d2.evaluation.evaluator INFO: Inference done 329/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:57 +[03/31 22:03:23] d2.evaluation.evaluator INFO: Inference done 409/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. 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ETA=0:00:35 +[03/31 22:07:45] d2.evaluation.evaluator INFO: Inference done 4542/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:00:30 +[03/31 22:07:50] d2.evaluation.evaluator INFO: Inference done 4621/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:00:25 +[03/31 22:07:55] d2.evaluation.evaluator INFO: Inference done 4700/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:00:20 +[03/31 22:08:00] d2.evaluation.evaluator INFO: Inference done 4779/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:00:15 +[03/31 22:08:05] d2.evaluation.evaluator INFO: Inference done 4859/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:00:10 +[03/31 22:08:10] d2.evaluation.evaluator INFO: Inference done 4938/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:00:05 +[03/31 22:08:15] d2.evaluation.evaluator INFO: Inference done 5018/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:00:00 +[03/31 22:08:16] d2.evaluation.evaluator INFO: Total inference time: 0:05:18.682142 (0.063470 s / iter per device, on 4 devices) +[03/31 22:08:16] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:04:57 (0.059253 s / iter per device, on 4 devices) +[03/31 22:08:17] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 30.384505572373893, 'fwIoU': 30.423545279490178, 'IoU-Impervious surfaces': 39.41337628599996, 'BoundaryIoU-Impervious surfaces': 83.4619293401432, 'min(IoU, B-Iou)-Impervious surfaces': 39.41337628599996, 'IoU-Building': 35.03693701565268, 'BoundaryIoU-Building': 12.87645695213116, 'min(IoU, B-Iou)-Building': 12.87645695213116, 'IoU-Low vegetation': 31.939890242024443, 'BoundaryIoU-Low vegetation': 18.262761698035618, 'min(IoU, B-Iou)-Low vegetation': 18.262761698035618, 'IoU-Tree': 7.4678879029960274, 'BoundaryIoU-Tree': 4.001483240131601, 'min(IoU, B-Iou)-Tree': 4.001483240131601, 'IoU-Car': 60.8232128036676, 'BoundaryIoU-Car': 43.80803533653517, 'min(IoU, B-Iou)-Car': 43.80803533653517, 'IoU-Clutter/background': 7.625729183902674, 'BoundaryIoU-Clutter/background': 3.612327206541135, 'min(IoU, B-Iou)-Clutter/background': 3.612327206541135, 'mACC': 45.30339229675397, 'pACC': 48.724040957655326, 'ACC-Impervious surfaces': 89.76671428202177, 'ACC-Building': 36.04142985005178, 'ACC-Low vegetation': 42.88318439839896, 'ACC-Tree': 8.08239967805886, 'ACC-Car': 82.35442703377248, 'ACC-Clutter/background': 12.692198538219984})]) +[03/31 22:08:17] d2.engine.defaults INFO: Evaluation results for Potsdam_all_sem_seg in csv format: +[03/31 22:08:17] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:08:17] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:08:17] d2.evaluation.testing INFO: copypaste: 30.3845,30.4235,45.3034,48.7240 diff --git a/OVRSIS/eval-Potsdam/log.txt.rank1 b/OVRSIS/eval-Potsdam/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..3d6f22f814aab42bd39aa081de6cf3e56a6af61d --- /dev/null +++ b/OVRSIS/eval-Potsdam/log.txt.rank1 @@ -0,0 +1,627 @@ +[03/31 22:02:45] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 22:02:46] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:02:46] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Potsdam', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Potsdam.json', 'DATASETS.TEST', '("Potsdam_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:02:46] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:02:46] detectron2.utils.env INFO: Using a generated random seed 50693285 +[03/31 22:02:50] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:02:50] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:02:50] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:02:51] detectron2.data.datasets.coco INFO: Loaded 20102 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Potsdam/imgs +[03/31 22:02:51] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:02:51] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:02:51] detectron2.data.common INFO: Serializing 20102 elements to byte tensors and concatenating them all ... +[03/31 22:02:51] detectron2.data.common INFO: Serialized dataset takes 5.49 MiB +[03/31 22:02:51] detectron2.data.datasets.coco INFO: Loaded 20102 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Potsdam/imgs +[03/31 22:02:51] detectron2.evaluation.evaluator INFO: Start inference on 5026 batches +[03/31 22:02:58] detectron2.evaluation.evaluator INFO: Inference done 11/5026. Dataloading: 0.0008 s/iter. Inference: 0.0595 s/iter. Eval: 0.0025 s/iter. Total: 0.0628 s/iter. ETA=0:05:14 +[03/31 22:03:03] detectron2.evaluation.evaluator INFO: Inference done 90/5026. Dataloading: 0.0013 s/iter. Inference: 0.0594 s/iter. Eval: 0.0027 s/iter. Total: 0.0635 s/iter. ETA=0:05:13 +[03/31 22:03:08] detectron2.evaluation.evaluator INFO: Inference done 169/5026. Dataloading: 0.0014 s/iter. Inference: 0.0594 s/iter. Eval: 0.0026 s/iter. Total: 0.0634 s/iter. ETA=0:05:07 +[03/31 22:03:13] detectron2.evaluation.evaluator INFO: Inference done 248/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0634 s/iter. ETA=0:05:02 +[03/31 22:03:18] detectron2.evaluation.evaluator INFO: Inference done 327/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0635 s/iter. ETA=0:04:58 +[03/31 22:03:23] detectron2.evaluation.evaluator INFO: Inference done 407/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:52 +[03/31 22:03:28] detectron2.evaluation.evaluator INFO: Inference done 486/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:47 +[03/31 22:03:33] detectron2.evaluation.evaluator INFO: Inference done 566/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:42 +[03/31 22:03:38] detectron2.evaluation.evaluator INFO: Inference done 646/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:37 +[03/31 22:03:43] detectron2.evaluation.evaluator INFO: Inference done 725/5026. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:32 +[03/31 22:03:48] detectron2.evaluation.evaluator INFO: Inference done 805/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:27 +[03/31 22:03:53] detectron2.evaluation.evaluator INFO: Inference done 884/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:22 +[03/31 22:03:58] detectron2.evaluation.evaluator INFO: Inference done 964/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:17 +[03/31 22:04:03] detectron2.evaluation.evaluator INFO: Inference done 1043/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:12 +[03/31 22:04:08] detectron2.evaluation.evaluator INFO: Inference done 1122/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:07 +[03/31 22:04:13] detectron2.evaluation.evaluator INFO: Inference done 1202/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:02 +[03/31 22:04:19] detectron2.evaluation.evaluator INFO: Inference done 1282/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:03:57 +[03/31 22:04:24] detectron2.evaluation.evaluator INFO: Inference done 1361/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:03:52 +[03/31 22:04:29] detectron2.evaluation.evaluator INFO: Inference done 1441/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:47 +[03/31 22:04:34] detectron2.evaluation.evaluator INFO: Inference done 1520/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:03:42 +[03/31 22:04:39] detectron2.evaluation.evaluator INFO: Inference done 1599/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:03:37 +[03/31 22:04:44] detectron2.evaluation.evaluator INFO: Inference done 1679/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:31 +[03/31 22:04:49] detectron2.evaluation.evaluator INFO: Inference done 1758/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:27 +[03/31 22:04:54] detectron2.evaluation.evaluator INFO: Inference done 1838/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:21 +[03/31 22:04:59] detectron2.evaluation.evaluator INFO: Inference done 1917/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:16 +[03/31 22:05:04] detectron2.evaluation.evaluator INFO: Inference done 1997/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:11 +[03/31 22:05:09] detectron2.evaluation.evaluator INFO: Inference done 2077/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:06 +[03/31 22:05:14] detectron2.evaluation.evaluator INFO: Inference done 2156/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:01 +[03/31 22:05:19] detectron2.evaluation.evaluator INFO: Inference done 2235/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:02:56 +[03/31 22:05:24] detectron2.evaluation.evaluator INFO: Inference done 2314/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:02:51 +[03/31 22:05:29] detectron2.evaluation.evaluator INFO: Inference done 2394/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:46 +[03/31 22:05:34] detectron2.evaluation.evaluator INFO: Inference done 2474/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:41 +[03/31 22:05:39] detectron2.evaluation.evaluator INFO: Inference done 2554/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:36 +[03/31 22:05:44] detectron2.evaluation.evaluator INFO: Inference done 2634/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:31 +[03/31 22:05:49] detectron2.evaluation.evaluator INFO: Inference done 2713/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:26 +[03/31 22:05:54] detectron2.evaluation.evaluator INFO: Inference done 2793/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:21 +[03/31 22:05:59] detectron2.evaluation.evaluator INFO: Inference done 2873/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:16 +[03/31 22:06:04] detectron2.evaluation.evaluator INFO: Inference done 2952/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:11 +[03/31 22:06:09] detectron2.evaluation.evaluator INFO: Inference done 3032/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:06 +[03/31 22:06:14] detectron2.evaluation.evaluator INFO: Inference done 3112/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:01 +[03/31 22:06:19] detectron2.evaluation.evaluator INFO: Inference done 3192/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:56 +[03/31 22:06:24] detectron2.evaluation.evaluator INFO: Inference done 3272/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:50 +[03/31 22:06:29] detectron2.evaluation.evaluator INFO: Inference done 3351/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:46 +[03/31 22:06:34] detectron2.evaluation.evaluator INFO: Inference done 3431/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:40 +[03/31 22:06:39] detectron2.evaluation.evaluator INFO: Inference done 3511/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:35 +[03/31 22:06:45] detectron2.evaluation.evaluator INFO: Inference done 3590/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:30 +[03/31 22:06:50] detectron2.evaluation.evaluator INFO: Inference done 3670/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:25 +[03/31 22:06:55] detectron2.evaluation.evaluator INFO: Inference done 3749/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:20 +[03/31 22:07:00] detectron2.evaluation.evaluator INFO: Inference done 3829/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:15 +[03/31 22:07:05] detectron2.evaluation.evaluator INFO: Inference done 3909/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:10 +[03/31 22:07:10] detectron2.evaluation.evaluator INFO: Inference done 3988/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:05 +[03/31 22:07:15] detectron2.evaluation.evaluator INFO: Inference done 4068/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:00 +[03/31 22:07:20] detectron2.evaluation.evaluator INFO: Inference done 4147/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:55 +[03/31 22:07:25] detectron2.evaluation.evaluator INFO: Inference done 4226/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:50 +[03/31 22:07:30] detectron2.evaluation.evaluator INFO: Inference done 4306/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:45 +[03/31 22:07:35] detectron2.evaluation.evaluator INFO: Inference done 4385/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:40 +[03/31 22:07:40] detectron2.evaluation.evaluator INFO: Inference done 4465/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:35 +[03/31 22:07:45] detectron2.evaluation.evaluator INFO: Inference done 4544/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:30 +[03/31 22:07:50] detectron2.evaluation.evaluator INFO: Inference done 4624/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:25 +[03/31 22:07:55] detectron2.evaluation.evaluator INFO: Inference done 4704/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:20 +[03/31 22:08:00] detectron2.evaluation.evaluator INFO: Inference done 4783/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:15 +[03/31 22:08:05] detectron2.evaluation.evaluator INFO: Inference done 4863/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:10 +[03/31 22:08:10] detectron2.evaluation.evaluator INFO: Inference done 4943/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:05 +[03/31 22:08:15] detectron2.evaluation.evaluator INFO: Inference done 5023/5026. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:00 +[03/31 22:08:16] detectron2.evaluation.evaluator INFO: Total inference time: 0:05:18.527563 (0.063439 s / iter per device, on 4 devices) +[03/31 22:08:16] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:04:57 (0.059186 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-Potsdam/log.txt.rank2 b/OVRSIS/eval-Potsdam/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..0267f7bcf8c28cfdae153f598f26f6af42223bdb --- /dev/null +++ b/OVRSIS/eval-Potsdam/log.txt.rank2 @@ -0,0 +1,627 @@ +[03/31 22:02:45] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 22:02:47] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:02:47] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Potsdam', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Potsdam.json', 'DATASETS.TEST', '("Potsdam_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:02:47] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:02:47] detectron2.utils.env INFO: Using a generated random seed 51883048 +[03/31 22:02:50] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:02:50] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:02:50] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:02:51] detectron2.data.datasets.coco INFO: Loaded 20102 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Potsdam/imgs +[03/31 22:02:51] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:02:51] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:02:51] detectron2.data.common INFO: Serializing 20102 elements to byte tensors and concatenating them all ... +[03/31 22:02:51] detectron2.data.common INFO: Serialized dataset takes 5.49 MiB +[03/31 22:02:51] detectron2.data.datasets.coco INFO: Loaded 20102 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Potsdam/imgs +[03/31 22:02:51] detectron2.evaluation.evaluator INFO: Start inference on 5025 batches +[03/31 22:02:58] detectron2.evaluation.evaluator INFO: Inference done 11/5025. Dataloading: 0.0007 s/iter. Inference: 0.0592 s/iter. Eval: 0.0030 s/iter. Total: 0.0628 s/iter. ETA=0:05:15 +[03/31 22:03:03] detectron2.evaluation.evaluator INFO: Inference done 90/5025. Dataloading: 0.0013 s/iter. Inference: 0.0593 s/iter. Eval: 0.0029 s/iter. Total: 0.0635 s/iter. ETA=0:05:13 +[03/31 22:03:08] detectron2.evaluation.evaluator INFO: Inference done 170/5025. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:05:07 +[03/31 22:03:13] detectron2.evaluation.evaluator INFO: Inference done 250/5025. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0632 s/iter. ETA=0:05:01 +[03/31 22:03:18] detectron2.evaluation.evaluator INFO: Inference done 329/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:57 +[03/31 22:03:23] detectron2.evaluation.evaluator INFO: Inference done 409/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:04:52 +[03/31 22:03:28] detectron2.evaluation.evaluator INFO: Inference done 488/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0634 s/iter. ETA=0:04:47 +[03/31 22:03:33] detectron2.evaluation.evaluator INFO: Inference done 568/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:04:42 +[03/31 22:03:38] detectron2.evaluation.evaluator INFO: Inference done 647/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:04:37 +[03/31 22:03:43] detectron2.evaluation.evaluator INFO: Inference done 726/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:32 +[03/31 22:03:48] detectron2.evaluation.evaluator INFO: Inference done 806/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:27 +[03/31 22:03:53] detectron2.evaluation.evaluator INFO: Inference done 885/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:22 +[03/31 22:03:58] detectron2.evaluation.evaluator INFO: Inference done 964/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:17 +[03/31 22:04:03] detectron2.evaluation.evaluator INFO: Inference done 1043/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:12 +[03/31 22:04:08] detectron2.evaluation.evaluator INFO: Inference done 1122/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:07 +[03/31 22:04:14] detectron2.evaluation.evaluator INFO: Inference done 1202/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:02 +[03/31 22:04:19] detectron2.evaluation.evaluator INFO: Inference done 1282/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:57 +[03/31 22:04:24] detectron2.evaluation.evaluator INFO: Inference done 1362/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:52 +[03/31 22:04:29] detectron2.evaluation.evaluator INFO: Inference done 1442/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:46 +[03/31 22:04:34] detectron2.evaluation.evaluator INFO: Inference done 1521/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:41 +[03/31 22:04:39] detectron2.evaluation.evaluator INFO: Inference done 1600/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:36 +[03/31 22:04:44] detectron2.evaluation.evaluator INFO: Inference done 1680/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:31 +[03/31 22:04:49] detectron2.evaluation.evaluator INFO: Inference done 1759/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:26 +[03/31 22:04:54] detectron2.evaluation.evaluator INFO: Inference done 1839/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:21 +[03/31 22:04:59] detectron2.evaluation.evaluator INFO: Inference done 1918/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:16 +[03/31 22:05:04] detectron2.evaluation.evaluator INFO: Inference done 1998/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:11 +[03/31 22:05:09] detectron2.evaluation.evaluator INFO: Inference done 2077/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:06 +[03/31 22:05:14] detectron2.evaluation.evaluator INFO: Inference done 2157/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:01 +[03/31 22:05:19] detectron2.evaluation.evaluator INFO: Inference done 2236/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:56 +[03/31 22:05:24] detectron2.evaluation.evaluator INFO: Inference done 2316/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:51 +[03/31 22:05:29] detectron2.evaluation.evaluator INFO: Inference done 2396/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:46 +[03/31 22:05:34] detectron2.evaluation.evaluator INFO: Inference done 2476/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:41 +[03/31 22:05:39] detectron2.evaluation.evaluator INFO: Inference done 2556/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:36 +[03/31 22:05:44] detectron2.evaluation.evaluator INFO: Inference done 2636/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:31 +[03/31 22:05:49] detectron2.evaluation.evaluator INFO: Inference done 2715/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:26 +[03/31 22:05:54] detectron2.evaluation.evaluator INFO: Inference done 2795/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:21 +[03/31 22:05:59] detectron2.evaluation.evaluator INFO: Inference done 2875/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:16 +[03/31 22:06:04] detectron2.evaluation.evaluator INFO: Inference done 2954/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:11 +[03/31 22:06:09] detectron2.evaluation.evaluator INFO: Inference done 3034/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:05 +[03/31 22:06:14] detectron2.evaluation.evaluator INFO: Inference done 3114/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:00 +[03/31 22:06:19] detectron2.evaluation.evaluator INFO: Inference done 3194/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:55 +[03/31 22:06:24] detectron2.evaluation.evaluator INFO: Inference done 3274/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:50 +[03/31 22:06:29] detectron2.evaluation.evaluator INFO: Inference done 3353/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:45 +[03/31 22:06:35] detectron2.evaluation.evaluator INFO: Inference done 3433/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:40 +[03/31 22:06:40] detectron2.evaluation.evaluator INFO: Inference done 3513/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:35 +[03/31 22:06:45] detectron2.evaluation.evaluator INFO: Inference done 3592/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:30 +[03/31 22:06:50] detectron2.evaluation.evaluator INFO: Inference done 3672/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:25 +[03/31 22:06:55] detectron2.evaluation.evaluator INFO: Inference done 3751/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:20 +[03/31 22:07:00] detectron2.evaluation.evaluator INFO: Inference done 3830/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:15 +[03/31 22:07:05] detectron2.evaluation.evaluator INFO: Inference done 3910/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:10 +[03/31 22:07:10] detectron2.evaluation.evaluator INFO: Inference done 3989/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:05 +[03/31 22:07:15] detectron2.evaluation.evaluator INFO: Inference done 4069/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:01:00 +[03/31 22:07:20] detectron2.evaluation.evaluator INFO: Inference done 4148/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:55 +[03/31 22:07:25] detectron2.evaluation.evaluator INFO: Inference done 4228/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:50 +[03/31 22:07:30] detectron2.evaluation.evaluator INFO: Inference done 4307/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:45 +[03/31 22:07:35] detectron2.evaluation.evaluator INFO: Inference done 4386/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:40 +[03/31 22:07:40] detectron2.evaluation.evaluator INFO: Inference done 4466/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:35 +[03/31 22:07:45] detectron2.evaluation.evaluator INFO: Inference done 4545/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:30 +[03/31 22:07:50] detectron2.evaluation.evaluator INFO: Inference done 4624/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:25 +[03/31 22:07:55] detectron2.evaluation.evaluator INFO: Inference done 4704/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:20 +[03/31 22:08:00] detectron2.evaluation.evaluator INFO: Inference done 4783/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:15 +[03/31 22:08:05] detectron2.evaluation.evaluator INFO: Inference done 4863/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:10 +[03/31 22:08:10] detectron2.evaluation.evaluator INFO: Inference done 4943/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:05 +[03/31 22:08:15] detectron2.evaluation.evaluator INFO: Inference done 5023/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:00 +[03/31 22:08:16] detectron2.evaluation.evaluator INFO: Total inference time: 0:05:18.456981 (0.063438 s / iter per device, on 4 devices) +[03/31 22:08:16] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:04:56 (0.059140 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-Potsdam/log.txt.rank3 b/OVRSIS/eval-Potsdam/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..e830d9d31ef54ba7635362f6e489751afc43648e --- /dev/null +++ b/OVRSIS/eval-Potsdam/log.txt.rank3 @@ -0,0 +1,626 @@ +[03/31 22:02:45] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 22:02:47] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:02:47] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Potsdam', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Potsdam.json', 'DATASETS.TEST', '("Potsdam_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:02:47] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:02:47] detectron2.utils.env INFO: Using a generated random seed 51895742 +[03/31 22:02:50] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:02:50] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:02:50] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:02:51] detectron2.data.datasets.coco INFO: Loaded 20102 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Potsdam/imgs +[03/31 22:02:51] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:02:51] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:02:51] detectron2.data.common INFO: Serializing 20102 elements to byte tensors and concatenating them all ... +[03/31 22:02:51] detectron2.data.common INFO: Serialized dataset takes 5.49 MiB +[03/31 22:02:51] detectron2.data.datasets.coco INFO: Loaded 20102 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Potsdam/imgs +[03/31 22:02:51] detectron2.evaluation.evaluator INFO: Start inference on 5025 batches +[03/31 22:02:58] detectron2.evaluation.evaluator INFO: Inference done 11/5025. Dataloading: 0.0014 s/iter. Inference: 0.0591 s/iter. Eval: 0.0026 s/iter. Total: 0.0630 s/iter. ETA=0:05:15 +[03/31 22:03:03] detectron2.evaluation.evaluator INFO: Inference done 90/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0028 s/iter. Total: 0.0635 s/iter. ETA=0:05:13 +[03/31 22:03:08] detectron2.evaluation.evaluator INFO: Inference done 170/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:05:07 +[03/31 22:03:13] detectron2.evaluation.evaluator INFO: Inference done 250/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:05:02 +[03/31 22:03:18] detectron2.evaluation.evaluator INFO: Inference done 329/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0028 s/iter. Total: 0.0634 s/iter. ETA=0:04:57 +[03/31 22:03:23] detectron2.evaluation.evaluator INFO: Inference done 409/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0028 s/iter. Total: 0.0633 s/iter. ETA=0:04:52 +[03/31 22:03:28] detectron2.evaluation.evaluator INFO: Inference done 488/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0028 s/iter. Total: 0.0634 s/iter. ETA=0:04:47 +[03/31 22:03:33] detectron2.evaluation.evaluator INFO: Inference done 568/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:04:42 +[03/31 22:03:38] detectron2.evaluation.evaluator INFO: Inference done 647/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:04:37 +[03/31 22:03:43] detectron2.evaluation.evaluator INFO: Inference done 726/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:32 +[03/31 22:03:48] detectron2.evaluation.evaluator INFO: Inference done 806/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:27 +[03/31 22:03:53] detectron2.evaluation.evaluator INFO: Inference done 885/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:22 +[03/31 22:03:58] detectron2.evaluation.evaluator INFO: Inference done 964/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:17 +[03/31 22:04:03] detectron2.evaluation.evaluator INFO: Inference done 1044/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:12 +[03/31 22:04:08] detectron2.evaluation.evaluator INFO: Inference done 1123/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:07 +[03/31 22:04:13] detectron2.evaluation.evaluator INFO: Inference done 1203/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:04:02 +[03/31 22:04:18] detectron2.evaluation.evaluator INFO: Inference done 1283/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:03:57 +[03/31 22:04:23] detectron2.evaluation.evaluator INFO: Inference done 1363/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:51 +[03/31 22:04:29] detectron2.evaluation.evaluator INFO: Inference done 1443/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:46 +[03/31 22:04:34] detectron2.evaluation.evaluator INFO: Inference done 1522/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:41 +[03/31 22:04:39] detectron2.evaluation.evaluator INFO: Inference done 1601/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:03:36 +[03/31 22:04:44] detectron2.evaluation.evaluator INFO: Inference done 1681/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:31 +[03/31 22:04:49] detectron2.evaluation.evaluator INFO: Inference done 1760/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:03:26 +[03/31 22:04:54] detectron2.evaluation.evaluator INFO: Inference done 1840/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:21 +[03/31 22:04:59] detectron2.evaluation.evaluator INFO: Inference done 1919/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:16 +[03/31 22:05:04] detectron2.evaluation.evaluator INFO: Inference done 1999/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:11 +[03/31 22:05:09] detectron2.evaluation.evaluator INFO: Inference done 2079/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:06 +[03/31 22:05:14] detectron2.evaluation.evaluator INFO: Inference done 2159/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:03:01 +[03/31 22:05:19] detectron2.evaluation.evaluator INFO: Inference done 2238/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:56 +[03/31 22:05:24] detectron2.evaluation.evaluator INFO: Inference done 2317/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:51 +[03/31 22:05:29] detectron2.evaluation.evaluator INFO: Inference done 2397/5025. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:46 +[03/31 22:05:34] detectron2.evaluation.evaluator INFO: Inference done 2477/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:41 +[03/31 22:05:39] detectron2.evaluation.evaluator INFO: Inference done 2557/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:36 +[03/31 22:05:44] detectron2.evaluation.evaluator INFO: Inference done 2637/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:31 +[03/31 22:05:49] detectron2.evaluation.evaluator INFO: Inference done 2716/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:26 +[03/31 22:05:54] detectron2.evaluation.evaluator INFO: Inference done 2796/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:02:21 +[03/31 22:05:59] detectron2.evaluation.evaluator INFO: Inference done 2876/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:02:16 +[03/31 22:06:04] detectron2.evaluation.evaluator INFO: Inference done 2955/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:02:11 +[03/31 22:06:09] detectron2.evaluation.evaluator INFO: Inference done 3035/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:02:05 +[03/31 22:06:14] detectron2.evaluation.evaluator INFO: Inference done 3115/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. ETA=0:02:00 +[03/31 22:06:19] detectron2.evaluation.evaluator INFO: Inference done 3195/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0026 s/iter. Total: 0.0633 s/iter. 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ETA=0:00:15 +[03/31 22:08:05] detectron2.evaluation.evaluator INFO: Inference done 4867/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:09 +[03/31 22:08:10] detectron2.evaluation.evaluator INFO: Inference done 4947/5025. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:04 +[03/31 22:08:16] detectron2.evaluation.evaluator INFO: Total inference time: 0:05:18.670636 (0.063480 s / iter per device, on 4 devices) +[03/31 22:08:16] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:04:57 (0.059214 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-UDD5/config.yaml b/OVRSIS/eval-UDD5/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14c3bb719de09b738df3c626d87bddedefc63f04 --- /dev/null +++ b/OVRSIS/eval-UDD5/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - UDD5_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/UDD5.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-UDD5 +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-UDD5/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-UDD5/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-UDD5/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-UDD5/inference/sem_seg_evaluation.pth b/OVRSIS/eval-UDD5/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..d075589501b445ab9961ecfa5625f6caf603dc7c --- /dev/null +++ b/OVRSIS/eval-UDD5/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2db303b63174f319abc425198477feff43a1955d3fbdd029c631f87a44d2a3e6 +size 2400 diff --git a/OVRSIS/eval-UDD5/inference/sem_seg_predictions.json b/OVRSIS/eval-UDD5/inference/sem_seg_predictions.json new file mode 100644 index 0000000000000000000000000000000000000000..ce3f03368425ac61afb7014d0f66ea314e87934e --- /dev/null +++ b/OVRSIS/eval-UDD5/inference/sem_seg_predictions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2d51739fedd80502b2a9ba918192c860352c37d3714f4f61aeb1ab0cfb0f3d2d +size 18142452 diff --git a/OVRSIS/eval-UDD5/log.txt b/OVRSIS/eval-UDD5/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..0af12f1c03d85ff74c169ebc64de3874f83c424a --- /dev/null +++ b/OVRSIS/eval-UDD5/log.txt @@ -0,0 +1,999 @@ +[03/31 22:09:25] detectron2 INFO: Rank of current process: 0. World size: 4 +[03/31 22:09:26] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:09:26] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-UDD5', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/UDD5.json', 'DATASETS.TEST', '("UDD5_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:09:26] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:09:26] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - UDD5_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/UDD5.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-UDD5 +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 22:09:26] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-UDD5/config.yaml +[03/31 22:09:26] d2.utils.env INFO: Using a generated random seed 30192119 +[03/31 22:09:29] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:09:29] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:09:29] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:09:30] d2.data.datasets.coco INFO: Loaded 160 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/UDD5/all/src +[03/31 22:09:30] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:09:30] d2.data.common INFO: Serializing the dataset using: +[03/31 22:09:30] d2.data.common INFO: Serializing 160 elements to byte tensors and concatenating them all ... +[03/31 22:09:30] d2.data.common INFO: Serialized dataset takes 0.04 MiB +[03/31 22:09:30] d2.data.datasets.coco INFO: Loaded 160 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/UDD5/all/src +[03/31 22:09:30] d2.evaluation.evaluator INFO: Start inference on 40 batches +[03/31 22:09:46] d2.evaluation.evaluator INFO: Inference done 11/40. Dataloading: 0.0016 s/iter. Inference: 0.0608 s/iter. Eval: 0.8312 s/iter. Total: 0.8936 s/iter. ETA=0:00:25 +[03/31 22:09:51] d2.evaluation.evaluator INFO: Inference done 17/40. Dataloading: 0.0016 s/iter. Inference: 0.0605 s/iter. Eval: 0.8403 s/iter. Total: 0.9028 s/iter. ETA=0:00:20 +[03/31 22:09:57] d2.evaluation.evaluator INFO: Inference done 23/40. Dataloading: 0.0017 s/iter. Inference: 0.0604 s/iter. Eval: 0.8579 s/iter. Total: 0.9203 s/iter. ETA=0:00:15 +[03/31 22:10:02] d2.evaluation.evaluator INFO: Inference done 29/40. Dataloading: 0.0017 s/iter. Inference: 0.0607 s/iter. Eval: 0.8378 s/iter. Total: 0.9005 s/iter. ETA=0:00:09 +[03/31 22:10:07] d2.evaluation.evaluator INFO: Inference done 36/40. Dataloading: 0.0017 s/iter. Inference: 0.0605 s/iter. Eval: 0.8044 s/iter. Total: 0.8670 s/iter. ETA=0:00:03 +[03/31 22:10:10] d2.evaluation.evaluator INFO: Total inference time: 0:00:30.000476 (0.857156 s / iter per device, on 4 devices) +[03/31 22:10:10] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:02 (0.060435 s / iter per device, on 4 devices) +[03/31 22:10:17] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 59.60776805187569, 'fwIoU': 69.98418359209396, 'IoU-Vegetation': 78.9238305267063, 'BoundaryIoU-Vegetation': 82.72796849583206, 'min(IoU, B-Iou)-Vegetation': 78.9238305267063, 'IoU-Building': 86.00224799890009, 'BoundaryIoU-Building': 55.51235759628754, 'min(IoU, B-Iou)-Building': 55.51235759628754, 'IoU-Road': 61.48263674539641, 'BoundaryIoU-Road': 48.40124333656595, 'min(IoU, B-Iou)-Road': 48.40124333656595, 'IoU-Vehicle': 45.2516780745327, 'BoundaryIoU-Vehicle': 22.537716669338977, 'min(IoU, B-Iou)-Vehicle': 22.537716669338977, 'IoU-Background/Clutter': 26.378446913842918, 'BoundaryIoU-Background/Clutter': 16.131391824645867, 'min(IoU, B-Iou)-Background/Clutter': 16.131391824645867, 'mACC': 77.62141335360786, 'pACC': 81.86599439335454, 'ACC-Vegetation': 91.7838018340542, 'ACC-Building': 94.54844333239252, 'ACC-Road': 89.4324383474609, 'ACC-Vehicle': 81.85504119305527, 'ACC-Background/Clutter': 30.487342061076355})]) +[03/31 22:10:17] d2.engine.defaults INFO: Evaluation results for UDD5_all_sem_seg in csv format: +[03/31 22:10:17] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:10:17] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:10:17] d2.evaluation.testing INFO: copypaste: 59.6078,69.9842,77.6214,81.8660 diff --git a/OVRSIS/eval-UDD5/log.txt.rank1 b/OVRSIS/eval-UDD5/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..57a02d2e11280695b4e217d4272b6299331dda90 --- /dev/null +++ b/OVRSIS/eval-UDD5/log.txt.rank1 @@ -0,0 +1,569 @@ +[03/31 22:09:25] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 22:09:26] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:09:26] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-UDD5', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/UDD5.json', 'DATASETS.TEST', '("UDD5_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:09:26] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:09:27] detectron2.utils.env INFO: Using a generated random seed 31003652 +[03/31 22:09:29] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:09:29] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:09:29] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:09:30] detectron2.data.datasets.coco INFO: Loaded 160 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/UDD5/all/src +[03/31 22:09:30] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:09:30] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:09:30] detectron2.data.common INFO: Serializing 160 elements to byte tensors and concatenating them all ... +[03/31 22:09:30] detectron2.data.common INFO: Serialized dataset takes 0.04 MiB +[03/31 22:09:30] detectron2.data.datasets.coco INFO: Loaded 160 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/UDD5/all/src +[03/31 22:09:30] detectron2.evaluation.evaluator INFO: Start inference on 40 batches +[03/31 22:09:43] detectron2.evaluation.evaluator INFO: Inference done 11/40. Dataloading: 0.0017 s/iter. Inference: 0.0599 s/iter. Eval: 0.7377 s/iter. Total: 0.7993 s/iter. ETA=0:00:23 +[03/31 22:09:49] detectron2.evaluation.evaluator INFO: Inference done 17/40. Dataloading: 0.0013 s/iter. Inference: 0.0598 s/iter. Eval: 0.8300 s/iter. Total: 0.8913 s/iter. ETA=0:00:20 +[03/31 22:09:54] detectron2.evaluation.evaluator INFO: Inference done 22/40. Dataloading: 0.0015 s/iter. Inference: 0.0600 s/iter. Eval: 0.8704 s/iter. Total: 0.9319 s/iter. ETA=0:00:16 +[03/31 22:10:00] detectron2.evaluation.evaluator INFO: Inference done 28/40. Dataloading: 0.0015 s/iter. Inference: 0.0601 s/iter. Eval: 0.8819 s/iter. Total: 0.9435 s/iter. ETA=0:00:11 +[03/31 22:10:05] detectron2.evaluation.evaluator INFO: Inference done 34/40. Dataloading: 0.0015 s/iter. Inference: 0.0599 s/iter. Eval: 0.8755 s/iter. Total: 0.9371 s/iter. ETA=0:00:05 +[03/31 22:10:11] detectron2.evaluation.evaluator INFO: Inference done 40/40. Dataloading: 0.0015 s/iter. Inference: 0.0599 s/iter. Eval: 0.8668 s/iter. Total: 0.9283 s/iter. ETA=0:00:00 +[03/31 22:10:11] detectron2.evaluation.evaluator INFO: Total inference time: 0:00:33.172301 (0.947780 s / iter per device, on 4 devices) +[03/31 22:10:11] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:02 (0.059869 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-UDD5/log.txt.rank2 b/OVRSIS/eval-UDD5/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..f30c36158a35d68862ea1fc23f8011d833a6a297 --- /dev/null +++ b/OVRSIS/eval-UDD5/log.txt.rank2 @@ -0,0 +1,569 @@ +[03/31 22:09:25] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 22:09:26] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:09:26] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-UDD5', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/UDD5.json', 'DATASETS.TEST', '("UDD5_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:09:26] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:09:27] detectron2.utils.env INFO: Using a generated random seed 31002391 +[03/31 22:09:29] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:09:29] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:09:29] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:09:30] detectron2.data.datasets.coco INFO: Loaded 160 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/UDD5/all/src +[03/31 22:09:30] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:09:30] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:09:30] detectron2.data.common INFO: Serializing 160 elements to byte tensors and concatenating them all ... +[03/31 22:09:30] detectron2.data.common INFO: Serialized dataset takes 0.04 MiB +[03/31 22:09:30] detectron2.data.datasets.coco INFO: Loaded 160 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/UDD5/all/src +[03/31 22:09:30] detectron2.evaluation.evaluator INFO: Start inference on 40 batches +[03/31 22:09:46] detectron2.evaluation.evaluator INFO: Inference done 11/40. Dataloading: 0.0008 s/iter. Inference: 0.0597 s/iter. Eval: 0.9595 s/iter. Total: 1.0201 s/iter. ETA=0:00:29 +[03/31 22:09:52] detectron2.evaluation.evaluator INFO: Inference done 17/40. Dataloading: 0.0011 s/iter. Inference: 0.0598 s/iter. Eval: 0.9086 s/iter. Total: 0.9696 s/iter. ETA=0:00:22 +[03/31 22:09:58] detectron2.evaluation.evaluator INFO: Inference done 23/40. Dataloading: 0.0013 s/iter. Inference: 0.0598 s/iter. Eval: 0.9144 s/iter. Total: 0.9755 s/iter. ETA=0:00:16 +[03/31 22:10:04] detectron2.evaluation.evaluator INFO: Inference done 29/40. Dataloading: 0.0014 s/iter. Inference: 0.0597 s/iter. Eval: 0.9193 s/iter. Total: 0.9805 s/iter. ETA=0:00:10 +[03/31 22:10:09] detectron2.evaluation.evaluator INFO: Inference done 34/40. Dataloading: 0.0015 s/iter. Inference: 0.0597 s/iter. Eval: 0.9386 s/iter. Total: 0.9998 s/iter. ETA=0:00:05 +[03/31 22:10:14] detectron2.evaluation.evaluator INFO: Inference done 39/40. Dataloading: 0.0015 s/iter. Inference: 0.0597 s/iter. Eval: 0.9406 s/iter. Total: 1.0020 s/iter. ETA=0:00:01 +[03/31 22:10:16] detectron2.evaluation.evaluator INFO: Total inference time: 0:00:35.779648 (1.022276 s / iter per device, on 4 devices) +[03/31 22:10:16] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:02 (0.059679 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-UDD5/log.txt.rank3 b/OVRSIS/eval-UDD5/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..5aa2ff09f77ea6d24bd730702619b196f5627910 --- /dev/null +++ b/OVRSIS/eval-UDD5/log.txt.rank3 @@ -0,0 +1,569 @@ +[03/31 22:09:25] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 22:09:27] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:09:27] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-UDD5', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/UDD5.json', 'DATASETS.TEST', '("UDD5_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:09:27] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:09:27] detectron2.utils.env INFO: Using a generated random seed 31296481 +[03/31 22:09:29] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:09:29] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:09:29] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:09:30] detectron2.data.datasets.coco INFO: Loaded 160 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/UDD5/all/src +[03/31 22:09:30] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:09:30] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:09:30] detectron2.data.common INFO: Serializing 160 elements to byte tensors and concatenating them all ... +[03/31 22:09:30] detectron2.data.common INFO: Serialized dataset takes 0.04 MiB +[03/31 22:09:30] detectron2.data.datasets.coco INFO: Loaded 160 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/UDD5/all/src +[03/31 22:09:30] detectron2.evaluation.evaluator INFO: Start inference on 40 batches +[03/31 22:09:47] detectron2.evaluation.evaluator INFO: Inference done 11/40. Dataloading: 0.0011 s/iter. Inference: 0.0596 s/iter. Eval: 0.9709 s/iter. Total: 1.0316 s/iter. ETA=0:00:29 +[03/31 22:09:53] detectron2.evaluation.evaluator INFO: Inference done 17/40. Dataloading: 0.0014 s/iter. Inference: 0.0596 s/iter. Eval: 0.9720 s/iter. Total: 1.0331 s/iter. ETA=0:00:23 +[03/31 22:09:58] detectron2.evaluation.evaluator INFO: Inference done 22/40. Dataloading: 0.0015 s/iter. Inference: 0.0597 s/iter. Eval: 0.9778 s/iter. Total: 1.0392 s/iter. ETA=0:00:18 +[03/31 22:10:04] detectron2.evaluation.evaluator INFO: Inference done 27/40. Dataloading: 0.0016 s/iter. Inference: 0.0597 s/iter. Eval: 0.9874 s/iter. Total: 1.0488 s/iter. ETA=0:00:13 +[03/31 22:10:09] detectron2.evaluation.evaluator INFO: Inference done 32/40. Dataloading: 0.0016 s/iter. Inference: 0.0597 s/iter. Eval: 0.9811 s/iter. Total: 1.0426 s/iter. ETA=0:00:08 +[03/31 22:10:14] detectron2.evaluation.evaluator INFO: Inference done 38/40. Dataloading: 0.0016 s/iter. Inference: 0.0597 s/iter. Eval: 0.9629 s/iter. Total: 1.0244 s/iter. ETA=0:00:02 +[03/31 22:10:17] detectron2.evaluation.evaluator INFO: Total inference time: 0:00:36.382385 (1.039497 s / iter per device, on 4 devices) +[03/31 22:10:17] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:02 (0.059833 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-VDD/config.yaml b/OVRSIS/eval-VDD/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5373d1ef4ca85e54e8e1227abd40e00f6fd0fb07 --- /dev/null +++ b/OVRSIS/eval-VDD/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - VDD_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/VDD.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-VDD +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-VDD/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-VDD/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-VDD/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-VDD/inference/sem_seg_evaluation.pth b/OVRSIS/eval-VDD/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..b96863a393f32d917c9f9863bd86da302d32ef72 --- /dev/null +++ b/OVRSIS/eval-VDD/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a29b457972aee0069856f31193b86e006138867b8fe3250cc64f861d9d3da8a +size 2912 diff --git a/OVRSIS/eval-VDD/inference/sem_seg_predictions.json b/OVRSIS/eval-VDD/inference/sem_seg_predictions.json new file mode 100644 index 0000000000000000000000000000000000000000..9c9465aeffc5290d5adb91d945115fe9b0ccc442 --- /dev/null +++ b/OVRSIS/eval-VDD/inference/sem_seg_predictions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c3df389fb770dfb4aebfdce0c19976eec73c15cd1997d0e551b6e038e35d04d +size 39136967 diff --git a/OVRSIS/eval-VDD/log.txt b/OVRSIS/eval-VDD/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..2b64e06cbab7dfae9108345a76c9cdd2cfec9011 --- /dev/null +++ b/OVRSIS/eval-VDD/log.txt @@ -0,0 +1,1013 @@ +[03/31 22:13:14] detectron2 INFO: Rank of current process: 0. World size: 4 +[03/31 22:13:16] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:13:16] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-VDD', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/VDD.json', 'DATASETS.TEST', '("VDD_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:13:16] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:13:16] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - VDD_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/VDD.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-VDD +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 22:13:16] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-VDD/config.yaml +[03/31 22:13:16] d2.utils.env INFO: Using a generated random seed 20634435 +[03/31 22:13:18] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:13:18] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:13:18] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:13:19] d2.data.datasets.coco INFO: Loaded 400 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/VDD/src +[03/31 22:13:19] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:13:19] d2.data.common INFO: Serializing the dataset using: +[03/31 22:13:19] d2.data.common INFO: Serializing 400 elements to byte tensors and concatenating them all ... +[03/31 22:13:19] d2.data.common INFO: Serialized dataset takes 0.09 MiB +[03/31 22:13:19] d2.data.datasets.coco INFO: Loaded 400 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/VDD/src +[03/31 22:13:19] d2.evaluation.evaluator INFO: Start inference on 100 batches +[03/31 22:13:37] d2.evaluation.evaluator INFO: Inference done 11/100. Dataloading: 0.0011 s/iter. Inference: 0.0613 s/iter. Eval: 1.0018 s/iter. Total: 1.0642 s/iter. ETA=0:01:34 +[03/31 22:13:42] d2.evaluation.evaluator INFO: Inference done 15/100. Dataloading: 0.0012 s/iter. Inference: 0.0613 s/iter. Eval: 1.1111 s/iter. Total: 1.1740 s/iter. ETA=0:01:39 +[03/31 22:13:47] d2.evaluation.evaluator INFO: Inference done 19/100. Dataloading: 0.0013 s/iter. Inference: 0.0614 s/iter. Eval: 1.1625 s/iter. Total: 1.2255 s/iter. ETA=0:01:39 +[03/31 22:13:53] d2.evaluation.evaluator INFO: Inference done 24/100. Dataloading: 0.0015 s/iter. Inference: 0.0613 s/iter. Eval: 1.1118 s/iter. Total: 1.1747 s/iter. ETA=0:01:29 +[03/31 22:13:59] d2.evaluation.evaluator INFO: Inference done 29/100. Dataloading: 0.0015 s/iter. Inference: 0.0612 s/iter. Eval: 1.1200 s/iter. Total: 1.1831 s/iter. ETA=0:01:23 +[03/31 22:14:04] d2.evaluation.evaluator INFO: Inference done 34/100. Dataloading: 0.0016 s/iter. Inference: 0.0612 s/iter. Eval: 1.0966 s/iter. Total: 1.1596 s/iter. ETA=0:01:16 +[03/31 22:14:10] d2.evaluation.evaluator INFO: Inference done 39/100. Dataloading: 0.0016 s/iter. Inference: 0.0611 s/iter. Eval: 1.0933 s/iter. Total: 1.1564 s/iter. ETA=0:01:10 +[03/31 22:14:15] d2.evaluation.evaluator INFO: Inference done 44/100. Dataloading: 0.0016 s/iter. Inference: 0.0611 s/iter. Eval: 1.0912 s/iter. Total: 1.1542 s/iter. ETA=0:01:04 +[03/31 22:14:21] d2.evaluation.evaluator INFO: Inference done 49/100. Dataloading: 0.0016 s/iter. Inference: 0.0610 s/iter. Eval: 1.0998 s/iter. Total: 1.1628 s/iter. ETA=0:00:59 +[03/31 22:14:27] d2.evaluation.evaluator INFO: Inference done 53/100. Dataloading: 0.0016 s/iter. Inference: 0.0610 s/iter. Eval: 1.1107 s/iter. Total: 1.1737 s/iter. ETA=0:00:55 +[03/31 22:14:32] d2.evaluation.evaluator INFO: Inference done 58/100. Dataloading: 0.0016 s/iter. Inference: 0.0610 s/iter. Eval: 1.1077 s/iter. Total: 1.1706 s/iter. ETA=0:00:49 +[03/31 22:14:38] d2.evaluation.evaluator INFO: Inference done 63/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.0998 s/iter. Total: 1.1628 s/iter. ETA=0:00:43 +[03/31 22:14:43] d2.evaluation.evaluator INFO: Inference done 68/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.0900 s/iter. Total: 1.1530 s/iter. ETA=0:00:36 +[03/31 22:14:48] d2.evaluation.evaluator INFO: Inference done 73/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.0865 s/iter. Total: 1.1495 s/iter. ETA=0:00:31 +[03/31 22:14:54] d2.evaluation.evaluator INFO: Inference done 78/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.0894 s/iter. Total: 1.1524 s/iter. ETA=0:00:25 +[03/31 22:15:00] d2.evaluation.evaluator INFO: Inference done 84/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.0797 s/iter. Total: 1.1427 s/iter. ETA=0:00:18 +[03/31 22:15:06] d2.evaluation.evaluator INFO: Inference done 89/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.0771 s/iter. Total: 1.1401 s/iter. ETA=0:00:12 +[03/31 22:15:12] d2.evaluation.evaluator INFO: Inference done 94/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.0791 s/iter. Total: 1.1421 s/iter. ETA=0:00:06 +[03/31 22:15:17] d2.evaluation.evaluator INFO: Inference done 99/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.0755 s/iter. Total: 1.1385 s/iter. ETA=0:00:01 +[03/31 22:15:19] d2.evaluation.evaluator INFO: Total inference time: 0:01:48.829111 (1.145570 s / iter per device, on 4 devices) +[03/31 22:15:19] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:05 (0.060983 s / iter per device, on 4 devices) +[03/31 22:15:19] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 44.38932127422142, 'fwIoU': 41.661318351190516, 'IoU-other, Background': 30.3980490584949, 'BoundaryIoU-other, Background': 66.40982427179324, 'min(IoU, B-Iou)-other, Background': 30.3980490584949, 'IoU-facade, wall': 18.94969128149274, 'BoundaryIoU-facade, wall': 13.599063165693234, 'min(IoU, B-Iou)-facade, wall': 13.599063165693234, 'IoU-road': 62.460042656704786, 'BoundaryIoU-road': 37.84068358206263, 'min(IoU, B-Iou)-road': 37.84068358206263, 'IoU-vegetation': 43.973626190921685, 'BoundaryIoU-vegetation': 24.97728304522803, 'min(IoU, B-Iou)-vegetation': 24.97728304522803, 'IoU-vehicle, car': 54.636252094432734, 'BoundaryIoU-vehicle, car': 23.780801830043217, 'min(IoU, B-Iou)-vehicle, car': 23.780801830043217, 'IoU-roof': 7.337946454203964, 'BoundaryIoU-roof': 5.596965074377448, 'min(IoU, B-Iou)-roof': 5.596965074377448, 'IoU-water': 92.96964118329917, 'BoundaryIoU-water': 61.46825727419871, 'min(IoU, B-Iou)-water': 61.46825727419871, 'mACC': 63.79706308391363, 'pACC': 55.58558247916666, 'ACC-other, Background': 85.35967138364845, 'ACC-facade, wall': 48.330901877468854, 'ACC-road': 83.79903536640073, 'ACC-vegetation': 44.312788850089106, 'ACC-vehicle, car': 81.68995224698979, 'ACC-roof': 7.351061637133863, 'ACC-water': 95.73603022566462})]) +[03/31 22:15:19] d2.engine.defaults INFO: Evaluation results for VDD_all_sem_seg in csv format: +[03/31 22:15:19] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:15:19] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:15:19] d2.evaluation.testing INFO: copypaste: 44.3893,41.6613,63.7971,55.5856 diff --git a/OVRSIS/eval-VDD/log.txt.rank1 b/OVRSIS/eval-VDD/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..72e3a5f0d0de9ff668cf356699b0cde901c21a61 --- /dev/null +++ b/OVRSIS/eval-VDD/log.txt.rank1 @@ -0,0 +1,581 @@ +[03/31 22:13:14] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 22:13:16] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:13:16] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-VDD', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/VDD.json', 'DATASETS.TEST', '("VDD_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:13:16] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:13:16] detectron2.utils.env INFO: Using a generated random seed 20542573 +[03/31 22:13:18] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:13:18] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:13:18] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:13:19] detectron2.data.datasets.coco INFO: Loaded 400 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/VDD/src +[03/31 22:13:19] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:13:19] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:13:19] detectron2.data.common INFO: Serializing 400 elements to byte tensors and concatenating them all ... +[03/31 22:13:19] detectron2.data.common INFO: Serialized dataset takes 0.09 MiB +[03/31 22:13:19] detectron2.data.datasets.coco INFO: Loaded 400 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/VDD/src +[03/31 22:13:19] detectron2.evaluation.evaluator INFO: Start inference on 100 batches +[03/31 22:13:38] detectron2.evaluation.evaluator INFO: Inference done 11/100. Dataloading: 0.0017 s/iter. Inference: 0.0616 s/iter. Eval: 1.2207 s/iter. Total: 1.2840 s/iter. ETA=0:01:54 +[03/31 22:13:43] detectron2.evaluation.evaluator INFO: Inference done 15/100. Dataloading: 0.0014 s/iter. Inference: 0.0617 s/iter. Eval: 1.2299 s/iter. Total: 1.2931 s/iter. ETA=0:01:49 +[03/31 22:13:49] detectron2.evaluation.evaluator INFO: Inference done 19/100. Dataloading: 0.0014 s/iter. Inference: 0.0613 s/iter. Eval: 1.2558 s/iter. Total: 1.3187 s/iter. ETA=0:01:46 +[03/31 22:13:55] detectron2.evaluation.evaluator INFO: Inference done 24/100. Dataloading: 0.0015 s/iter. Inference: 0.0611 s/iter. Eval: 1.2306 s/iter. Total: 1.2934 s/iter. ETA=0:01:38 +[03/31 22:14:00] detectron2.evaluation.evaluator INFO: Inference done 28/100. Dataloading: 0.0016 s/iter. Inference: 0.0613 s/iter. Eval: 1.2371 s/iter. Total: 1.3001 s/iter. ETA=0:01:33 +[03/31 22:14:06] detectron2.evaluation.evaluator INFO: Inference done 33/100. Dataloading: 0.0016 s/iter. Inference: 0.0611 s/iter. Eval: 1.2036 s/iter. Total: 1.2665 s/iter. ETA=0:01:24 +[03/31 22:14:12] detectron2.evaluation.evaluator INFO: Inference done 38/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.1939 s/iter. Total: 1.2568 s/iter. ETA=0:01:17 +[03/31 22:14:17] detectron2.evaluation.evaluator INFO: Inference done 42/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.1966 s/iter. Total: 1.2595 s/iter. ETA=0:01:13 +[03/31 22:14:22] detectron2.evaluation.evaluator INFO: Inference done 46/100. Dataloading: 0.0017 s/iter. Inference: 0.0609 s/iter. Eval: 1.2020 s/iter. Total: 1.2647 s/iter. ETA=0:01:08 +[03/31 22:14:28] detectron2.evaluation.evaluator INFO: Inference done 50/100. Dataloading: 0.0017 s/iter. Inference: 0.0610 s/iter. Eval: 1.2113 s/iter. Total: 1.2741 s/iter. ETA=0:01:03 +[03/31 22:14:33] detectron2.evaluation.evaluator INFO: Inference done 56/100. Dataloading: 0.0017 s/iter. Inference: 0.0609 s/iter. Eval: 1.1704 s/iter. Total: 1.2332 s/iter. ETA=0:00:54 +[03/31 22:14:38] detectron2.evaluation.evaluator INFO: Inference done 61/100. Dataloading: 0.0017 s/iter. Inference: 0.0609 s/iter. Eval: 1.1536 s/iter. Total: 1.2164 s/iter. ETA=0:00:47 +[03/31 22:14:44] detectron2.evaluation.evaluator INFO: Inference done 66/100. Dataloading: 0.0017 s/iter. Inference: 0.0609 s/iter. Eval: 1.1429 s/iter. Total: 1.2057 s/iter. ETA=0:00:40 +[03/31 22:14:49] detectron2.evaluation.evaluator INFO: Inference done 72/100. Dataloading: 0.0017 s/iter. Inference: 0.0609 s/iter. Eval: 1.1172 s/iter. Total: 1.1800 s/iter. ETA=0:00:33 +[03/31 22:14:55] detectron2.evaluation.evaluator INFO: Inference done 80/100. Dataloading: 0.0017 s/iter. Inference: 0.0608 s/iter. Eval: 1.0684 s/iter. Total: 1.1312 s/iter. ETA=0:00:22 +[03/31 22:15:00] detectron2.evaluation.evaluator INFO: Inference done 86/100. Dataloading: 0.0017 s/iter. Inference: 0.0609 s/iter. Eval: 1.0500 s/iter. Total: 1.1127 s/iter. ETA=0:00:15 +[03/31 22:15:06] detectron2.evaluation.evaluator INFO: Inference done 91/100. Dataloading: 0.0017 s/iter. Inference: 0.0609 s/iter. Eval: 1.0495 s/iter. Total: 1.1123 s/iter. ETA=0:00:10 +[03/31 22:15:12] detectron2.evaluation.evaluator INFO: Inference done 97/100. Dataloading: 0.0017 s/iter. Inference: 0.0609 s/iter. Eval: 1.0432 s/iter. Total: 1.1059 s/iter. ETA=0:00:03 +[03/31 22:15:16] detectron2.evaluation.evaluator INFO: Total inference time: 0:01:45.982897 (1.115609 s / iter per device, on 4 devices) +[03/31 22:15:16] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:05 (0.060868 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-VDD/log.txt.rank2 b/OVRSIS/eval-VDD/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..2a206c91db2ade94bf05cb0608766027f4583f4e --- /dev/null +++ b/OVRSIS/eval-VDD/log.txt.rank2 @@ -0,0 +1,581 @@ +[03/31 22:13:14] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 22:13:15] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:13:15] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-VDD', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/VDD.json', 'DATASETS.TEST', '("VDD_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:13:15] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:13:15] detectron2.utils.env INFO: Using a generated random seed 19440203 +[03/31 22:13:18] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:13:18] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:13:18] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:13:19] detectron2.data.datasets.coco INFO: Loaded 400 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/VDD/src +[03/31 22:13:19] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:13:19] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:13:19] detectron2.data.common INFO: Serializing 400 elements to byte tensors and concatenating them all ... +[03/31 22:13:19] detectron2.data.common INFO: Serialized dataset takes 0.09 MiB +[03/31 22:13:19] detectron2.data.datasets.coco INFO: Loaded 400 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/VDD/src +[03/31 22:13:19] detectron2.evaluation.evaluator INFO: Start inference on 100 batches +[03/31 22:13:37] detectron2.evaluation.evaluator INFO: Inference done 11/100. Dataloading: 0.0007 s/iter. Inference: 0.0609 s/iter. Eval: 1.0840 s/iter. Total: 1.1456 s/iter. ETA=0:01:41 +[03/31 22:13:42] detectron2.evaluation.evaluator INFO: Inference done 16/100. Dataloading: 0.0008 s/iter. Inference: 0.0607 s/iter. Eval: 1.0369 s/iter. Total: 1.0985 s/iter. ETA=0:01:32 +[03/31 22:13:47] detectron2.evaluation.evaluator INFO: Inference done 21/100. Dataloading: 0.0011 s/iter. Inference: 0.0607 s/iter. Eval: 1.0354 s/iter. Total: 1.0973 s/iter. ETA=0:01:26 +[03/31 22:13:52] detectron2.evaluation.evaluator INFO: Inference done 25/100. Dataloading: 0.0012 s/iter. Inference: 0.0613 s/iter. Eval: 1.0720 s/iter. Total: 1.1346 s/iter. ETA=0:01:25 +[03/31 22:13:58] detectron2.evaluation.evaluator INFO: Inference done 29/100. Dataloading: 0.0012 s/iter. Inference: 0.0612 s/iter. Eval: 1.0975 s/iter. Total: 1.1601 s/iter. ETA=0:01:22 +[03/31 22:14:03] detectron2.evaluation.evaluator INFO: Inference done 34/100. Dataloading: 0.0013 s/iter. Inference: 0.0611 s/iter. Eval: 1.0871 s/iter. Total: 1.1496 s/iter. ETA=0:01:15 +[03/31 22:14:08] detectron2.evaluation.evaluator INFO: Inference done 39/100. Dataloading: 0.0014 s/iter. Inference: 0.0611 s/iter. Eval: 1.0680 s/iter. Total: 1.1306 s/iter. ETA=0:01:08 +[03/31 22:14:14] detectron2.evaluation.evaluator INFO: Inference done 44/100. Dataloading: 0.0014 s/iter. Inference: 0.0610 s/iter. Eval: 1.0659 s/iter. Total: 1.1284 s/iter. ETA=0:01:03 +[03/31 22:14:19] detectron2.evaluation.evaluator INFO: Inference done 49/100. Dataloading: 0.0014 s/iter. Inference: 0.0610 s/iter. Eval: 1.0659 s/iter. Total: 1.1284 s/iter. ETA=0:00:57 +[03/31 22:14:26] detectron2.evaluation.evaluator INFO: Inference done 55/100. Dataloading: 0.0015 s/iter. Inference: 0.0609 s/iter. Eval: 1.0538 s/iter. Total: 1.1164 s/iter. ETA=0:00:50 +[03/31 22:14:31] detectron2.evaluation.evaluator INFO: Inference done 60/100. Dataloading: 0.0015 s/iter. Inference: 0.0609 s/iter. Eval: 1.0596 s/iter. Total: 1.1222 s/iter. ETA=0:00:44 +[03/31 22:14:37] detectron2.evaluation.evaluator INFO: Inference done 65/100. Dataloading: 0.0015 s/iter. Inference: 0.0609 s/iter. Eval: 1.0535 s/iter. Total: 1.1160 s/iter. ETA=0:00:39 +[03/31 22:14:42] detectron2.evaluation.evaluator INFO: Inference done 70/100. Dataloading: 0.0015 s/iter. Inference: 0.0608 s/iter. Eval: 1.0541 s/iter. Total: 1.1166 s/iter. ETA=0:00:33 +[03/31 22:14:48] detectron2.evaluation.evaluator INFO: Inference done 76/100. Dataloading: 0.0015 s/iter. Inference: 0.0608 s/iter. Eval: 1.0467 s/iter. Total: 1.1092 s/iter. ETA=0:00:26 +[03/31 22:14:54] detectron2.evaluation.evaluator INFO: Inference done 81/100. Dataloading: 0.0015 s/iter. Inference: 0.0609 s/iter. Eval: 1.0437 s/iter. Total: 1.1062 s/iter. ETA=0:00:21 +[03/31 22:15:00] detectron2.evaluation.evaluator INFO: Inference done 87/100. Dataloading: 0.0015 s/iter. Inference: 0.0608 s/iter. Eval: 1.0333 s/iter. Total: 1.0958 s/iter. ETA=0:00:14 +[03/31 22:15:05] detectron2.evaluation.evaluator INFO: Inference done 92/100. Dataloading: 0.0015 s/iter. Inference: 0.0608 s/iter. Eval: 1.0295 s/iter. Total: 1.0920 s/iter. ETA=0:00:08 +[03/31 22:15:10] detectron2.evaluation.evaluator INFO: Inference done 96/100. Dataloading: 0.0015 s/iter. Inference: 0.0608 s/iter. Eval: 1.0373 s/iter. Total: 1.0998 s/iter. ETA=0:00:04 +[03/31 22:15:15] detectron2.evaluation.evaluator INFO: Total inference time: 0:01:45.685538 (1.112479 s / iter per device, on 4 devices) +[03/31 22:15:15] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:05 (0.060788 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-VDD/log.txt.rank3 b/OVRSIS/eval-VDD/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..6636f5422409b628af39914d1c7e7b96e11c4173 --- /dev/null +++ b/OVRSIS/eval-VDD/log.txt.rank3 @@ -0,0 +1,579 @@ +[03/31 22:13:14] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 22:13:15] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:13:15] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-VDD', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/VDD.json', 'DATASETS.TEST', '("VDD_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:13:15] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:13:15] detectron2.utils.env INFO: Using a generated random seed 19998808 +[03/31 22:13:18] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:13:18] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:13:18] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:13:19] detectron2.data.datasets.coco INFO: Loaded 400 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/VDD/src +[03/31 22:13:19] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:13:19] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:13:19] detectron2.data.common INFO: Serializing 400 elements to byte tensors and concatenating them all ... +[03/31 22:13:19] detectron2.data.common INFO: Serialized dataset takes 0.09 MiB +[03/31 22:13:19] detectron2.data.datasets.coco INFO: Loaded 400 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/VDD/src +[03/31 22:13:19] detectron2.evaluation.evaluator INFO: Start inference on 100 batches +[03/31 22:13:38] detectron2.evaluation.evaluator INFO: Inference done 11/100. Dataloading: 0.0014 s/iter. Inference: 0.0618 s/iter. Eval: 1.1356 s/iter. Total: 1.1988 s/iter. ETA=0:01:46 +[03/31 22:13:44] detectron2.evaluation.evaluator INFO: Inference done 16/100. Dataloading: 0.0012 s/iter. Inference: 0.0619 s/iter. Eval: 1.1246 s/iter. Total: 1.1878 s/iter. ETA=0:01:39 +[03/31 22:13:49] detectron2.evaluation.evaluator INFO: Inference done 21/100. Dataloading: 0.0014 s/iter. Inference: 0.0618 s/iter. Eval: 1.0726 s/iter. Total: 1.1359 s/iter. ETA=0:01:29 +[03/31 22:13:55] detectron2.evaluation.evaluator INFO: Inference done 26/100. Dataloading: 0.0015 s/iter. Inference: 0.0623 s/iter. Eval: 1.0962 s/iter. Total: 1.1602 s/iter. ETA=0:01:25 +[03/31 22:14:00] detectron2.evaluation.evaluator INFO: Inference done 30/100. Dataloading: 0.0016 s/iter. Inference: 0.0622 s/iter. Eval: 1.1295 s/iter. Total: 1.1935 s/iter. ETA=0:01:23 +[03/31 22:14:06] detectron2.evaluation.evaluator INFO: Inference done 34/100. Dataloading: 0.0016 s/iter. Inference: 0.0620 s/iter. Eval: 1.1498 s/iter. Total: 1.2137 s/iter. ETA=0:01:20 +[03/31 22:14:11] detectron2.evaluation.evaluator INFO: Inference done 39/100. Dataloading: 0.0016 s/iter. Inference: 0.0618 s/iter. Eval: 1.1201 s/iter. Total: 1.1837 s/iter. ETA=0:01:12 +[03/31 22:14:16] detectron2.evaluation.evaluator INFO: Inference done 46/100. Dataloading: 0.0016 s/iter. Inference: 0.0616 s/iter. Eval: 1.0410 s/iter. Total: 1.1045 s/iter. ETA=0:00:59 +[03/31 22:14:22] detectron2.evaluation.evaluator INFO: Inference done 52/100. Dataloading: 0.0017 s/iter. Inference: 0.0616 s/iter. Eval: 1.0212 s/iter. Total: 1.0846 s/iter. ETA=0:00:52 +[03/31 22:14:27] detectron2.evaluation.evaluator INFO: Inference done 59/100. Dataloading: 0.0017 s/iter. Inference: 0.0614 s/iter. Eval: 0.9850 s/iter. Total: 1.0482 s/iter. ETA=0:00:42 +[03/31 22:14:32] detectron2.evaluation.evaluator INFO: Inference done 65/100. Dataloading: 0.0017 s/iter. Inference: 0.0613 s/iter. Eval: 0.9664 s/iter. Total: 1.0296 s/iter. ETA=0:00:36 +[03/31 22:14:38] detectron2.evaluation.evaluator INFO: Inference done 73/100. Dataloading: 0.0017 s/iter. Inference: 0.0612 s/iter. Eval: 0.9283 s/iter. Total: 0.9914 s/iter. ETA=0:00:26 +[03/31 22:14:43] detectron2.evaluation.evaluator INFO: Inference done 81/100. Dataloading: 0.0017 s/iter. Inference: 0.0612 s/iter. Eval: 0.8914 s/iter. Total: 0.9544 s/iter. ETA=0:00:18 +[03/31 22:14:49] detectron2.evaluation.evaluator INFO: Inference done 90/100. Dataloading: 0.0017 s/iter. Inference: 0.0612 s/iter. Eval: 0.8613 s/iter. Total: 0.9243 s/iter. ETA=0:00:09 +[03/31 22:14:54] detectron2.evaluation.evaluator INFO: Inference done 94/100. Dataloading: 0.0017 s/iter. Inference: 0.0612 s/iter. Eval: 0.8795 s/iter. Total: 0.9426 s/iter. ETA=0:00:05 +[03/31 22:15:00] detectron2.evaluation.evaluator INFO: Inference done 99/100. Dataloading: 0.0017 s/iter. Inference: 0.0611 s/iter. Eval: 0.8895 s/iter. Total: 0.9525 s/iter. ETA=0:00:00 +[03/31 22:15:02] detectron2.evaluation.evaluator INFO: Total inference time: 0:01:31.074694 (0.958681 s / iter per device, on 4 devices) +[03/31 22:15:02] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:05 (0.061136 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-Vaihingen/config.yaml b/OVRSIS/eval-Vaihingen/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cca1ff1175b3ba1d0023f1ca7ee529ee15259fde --- /dev/null +++ b/OVRSIS/eval-Vaihingen/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - Vaihingen_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Vaihingen.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Vaihingen +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-Vaihingen/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-Vaihingen/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-Vaihingen/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-Vaihingen/inference/sem_seg_evaluation.pth b/OVRSIS/eval-Vaihingen/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..626477c0272381e5133437aea3fce1cfac27d3c3 --- /dev/null +++ b/OVRSIS/eval-Vaihingen/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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World size: 4 +[03/31 22:08:27] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:08:27] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Vaihingen', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Vaihingen.json', 'DATASETS.TEST', '("Vaihingen_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:08:27] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:08:27] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - Vaihingen_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Vaihingen.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Vaihingen +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 22:08:27] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Vaihingen/config.yaml +[03/31 22:08:28] d2.utils.env INFO: Using a generated random seed 32014447 +[03/31 22:08:31] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:08:31] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:08:31] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:08:31] d2.data.datasets.coco WARNING: Directory /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs and /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/D2masks has 2254 and 3280 files, respectively. +[03/31 22:08:31] d2.data.datasets.coco WARNING: Will use their intersection of 2254 files. +[03/31 22:08:31] d2.data.datasets.coco INFO: Loaded 2254 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs +[03/31 22:08:31] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:08:31] d2.data.common INFO: Serializing the dataset using: +[03/31 22:08:31] d2.data.common INFO: Serializing 2254 elements to byte tensors and concatenating them all ... +[03/31 22:08:31] d2.data.common INFO: Serialized dataset takes 0.62 MiB +[03/31 22:08:31] d2.data.datasets.coco WARNING: Directory /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs and /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/D2masks has 2254 and 3280 files, respectively. +[03/31 22:08:31] d2.data.datasets.coco WARNING: Will use their intersection of 2254 files. +[03/31 22:08:31] d2.data.datasets.coco INFO: Loaded 2254 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs +[03/31 22:08:31] d2.evaluation.evaluator INFO: Start inference on 564 batches +[03/31 22:08:38] d2.evaluation.evaluator INFO: Inference done 11/564. Dataloading: 0.0008 s/iter. Inference: 0.0592 s/iter. Eval: 0.0030 s/iter. Total: 0.0631 s/iter. ETA=0:00:34 +[03/31 22:08:43] d2.evaluation.evaluator INFO: Inference done 90/564. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0028 s/iter. Total: 0.0633 s/iter. ETA=0:00:30 +[03/31 22:08:48] d2.evaluation.evaluator INFO: Inference done 170/564. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:24 +[03/31 22:08:53] d2.evaluation.evaluator INFO: Inference done 249/564. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:19 +[03/31 22:08:58] d2.evaluation.evaluator INFO: Inference done 329/564. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:14 +[03/31 22:09:03] d2.evaluation.evaluator INFO: Inference done 408/564. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:09 +[03/31 22:09:08] d2.evaluation.evaluator INFO: Inference done 488/564. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:04 +[03/31 22:09:14] d2.evaluation.evaluator INFO: Total inference time: 0:00:36.309376 (0.064954 s / iter per device, on 4 devices) +[03/31 22:09:14] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:33 (0.059164 s / iter per device, on 4 devices) +[03/31 22:09:14] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 31.70234941905093, 'fwIoU': 36.3159382773237, 'IoU-Impervious surfaces': 37.10293645562226, 'BoundaryIoU-Impervious surfaces': 75.05448158651384, 'min(IoU, B-Iou)-Impervious surfaces': 37.10293645562226, 'IoU-Building': 52.73790868984706, 'BoundaryIoU-Building': 14.822688987871397, 'min(IoU, B-Iou)-Building': 14.822688987871397, 'IoU-Low vegetation': 4.032721820395608, 'BoundaryIoU-Low vegetation': 2.074781256901028, 'min(IoU, B-Iou)-Low vegetation': 2.074781256901028, 'IoU-Tree': 46.49049103667895, 'BoundaryIoU-Tree': 17.445417126660697, 'min(IoU, B-Iou)-Tree': 17.445417126660697, 'IoU-Car': 47.8994686180112, 'BoundaryIoU-Car': 35.97414255003561, 'min(IoU, B-Iou)-Car': 35.97414255003561, 'IoU-Clutter/background': 1.9505698937505027, 'BoundaryIoU-Clutter/background': 0.9180499993354687, 'min(IoU, B-Iou)-Clutter/background': 0.9180499993354687, 'mACC': 46.30991663127399, 'pACC': 52.36202602166461, 'ACC-Impervious surfaces': 88.31712839702313, 'ACC-Building': 54.623167341654664, 'ACC-Low vegetation': 4.06127699172526, 'ACC-Tree': 50.52892086680385, 'ACC-Car': 64.23097039159913, 'ACC-Clutter/background': 16.09803579883792})]) +[03/31 22:09:14] d2.engine.defaults INFO: Evaluation results for Vaihingen_all_sem_seg in csv format: +[03/31 22:09:14] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:09:14] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:09:14] d2.evaluation.testing INFO: copypaste: 31.7023,36.3159,46.3099,52.3620 diff --git a/OVRSIS/eval-Vaihingen/log.txt.rank1 b/OVRSIS/eval-Vaihingen/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..894ea180afb1735a2c994de22c4ad988cc03f831 --- /dev/null +++ b/OVRSIS/eval-Vaihingen/log.txt.rank1 @@ -0,0 +1,574 @@ +[03/31 22:08:26] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 22:08:28] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:08:28] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Vaihingen', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Vaihingen.json', 'DATASETS.TEST', '("Vaihingen_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:08:28] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:08:28] detectron2.utils.env INFO: Using a generated random seed 32667767 +[03/31 22:08:31] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:08:31] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:08:31] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Directory /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs and /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/D2masks has 2254 and 3280 files, respectively. +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Will use their intersection of 2254 files. +[03/31 22:08:31] detectron2.data.datasets.coco INFO: Loaded 2254 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs +[03/31 22:08:31] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:08:31] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:08:31] detectron2.data.common INFO: Serializing 2254 elements to byte tensors and concatenating them all ... +[03/31 22:08:31] detectron2.data.common INFO: Serialized dataset takes 0.62 MiB +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Directory /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs and /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/D2masks has 2254 and 3280 files, respectively. +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Will use their intersection of 2254 files. +[03/31 22:08:31] detectron2.data.datasets.coco INFO: Loaded 2254 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs +[03/31 22:08:31] detectron2.evaluation.evaluator INFO: Start inference on 564 batches +[03/31 22:08:38] detectron2.evaluation.evaluator INFO: Inference done 11/564. Dataloading: 0.0007 s/iter. Inference: 0.0595 s/iter. Eval: 0.0033 s/iter. Total: 0.0635 s/iter. ETA=0:00:35 +[03/31 22:08:43] detectron2.evaluation.evaluator INFO: Inference done 91/564. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0632 s/iter. ETA=0:00:29 +[03/31 22:08:48] detectron2.evaluation.evaluator INFO: Inference done 170/564. Dataloading: 0.0013 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:24 +[03/31 22:08:53] detectron2.evaluation.evaluator INFO: Inference done 249/564. Dataloading: 0.0013 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:19 +[03/31 22:08:58] detectron2.evaluation.evaluator INFO: Inference done 329/564. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:14 +[03/31 22:09:03] detectron2.evaluation.evaluator INFO: Inference done 408/564. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:09 +[03/31 22:09:08] detectron2.evaluation.evaluator INFO: Inference done 488/564. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:04 +[03/31 22:09:14] detectron2.evaluation.evaluator INFO: Total inference time: 0:00:36.380982 (0.065082 s / iter per device, on 4 devices) +[03/31 22:09:14] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:33 (0.059250 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-Vaihingen/log.txt.rank2 b/OVRSIS/eval-Vaihingen/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..514fd9fec4c6a494ba72336a54b60c88e0abc9c3 --- /dev/null +++ b/OVRSIS/eval-Vaihingen/log.txt.rank2 @@ -0,0 +1,574 @@ +[03/31 22:08:26] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 22:08:28] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:08:28] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Vaihingen', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Vaihingen.json', 'DATASETS.TEST', '("Vaihingen_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:08:28] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:08:28] detectron2.utils.env INFO: Using a generated random seed 32390088 +[03/31 22:08:31] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:08:31] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:08:31] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Directory /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs and /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/D2masks has 2254 and 3280 files, respectively. +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Will use their intersection of 2254 files. +[03/31 22:08:31] detectron2.data.datasets.coco INFO: Loaded 2254 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs +[03/31 22:08:31] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:08:31] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:08:31] detectron2.data.common INFO: Serializing 2254 elements to byte tensors and concatenating them all ... +[03/31 22:08:31] detectron2.data.common INFO: Serialized dataset takes 0.62 MiB +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Directory /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs and /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/D2masks has 2254 and 3280 files, respectively. +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Will use their intersection of 2254 files. +[03/31 22:08:31] detectron2.data.datasets.coco INFO: Loaded 2254 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs +[03/31 22:08:31] detectron2.evaluation.evaluator INFO: Start inference on 563 batches +[03/31 22:08:38] detectron2.evaluation.evaluator INFO: Inference done 11/563. Dataloading: 0.0009 s/iter. Inference: 0.0593 s/iter. Eval: 0.0026 s/iter. Total: 0.0629 s/iter. ETA=0:00:34 +[03/31 22:08:43] detectron2.evaluation.evaluator INFO: Inference done 91/563. Dataloading: 0.0013 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0631 s/iter. ETA=0:00:29 +[03/31 22:08:48] detectron2.evaluation.evaluator INFO: Inference done 170/563. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:24 +[03/31 22:08:53] detectron2.evaluation.evaluator INFO: Inference done 249/563. Dataloading: 0.0013 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:19 +[03/31 22:08:58] detectron2.evaluation.evaluator INFO: Inference done 329/563. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:14 +[03/31 22:09:03] detectron2.evaluation.evaluator INFO: Inference done 408/563. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:09 +[03/31 22:09:08] detectron2.evaluation.evaluator INFO: Inference done 488/563. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:04 +[03/31 22:09:14] detectron2.evaluation.evaluator INFO: Total inference time: 0:00:36.135594 (0.064759 s / iter per device, on 4 devices) +[03/31 22:09:14] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:33 (0.059192 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-Vaihingen/log.txt.rank3 b/OVRSIS/eval-Vaihingen/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..9880e183c2788ad8b313913ec7e12b4cb66eddce --- /dev/null +++ b/OVRSIS/eval-Vaihingen/log.txt.rank3 @@ -0,0 +1,574 @@ +[03/31 22:08:26] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 22:08:28] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:08:28] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-Vaihingen', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/Vaihingen.json', 'DATASETS.TEST', '("Vaihingen_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:08:28] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:08:28] detectron2.utils.env INFO: Using a generated random seed 32360516 +[03/31 22:08:31] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:08:31] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:08:31] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Directory /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs and /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/D2masks has 2254 and 3280 files, respectively. +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Will use their intersection of 2254 files. +[03/31 22:08:31] detectron2.data.datasets.coco INFO: Loaded 2254 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs +[03/31 22:08:31] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:08:31] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:08:31] detectron2.data.common INFO: Serializing 2254 elements to byte tensors and concatenating them all ... +[03/31 22:08:31] detectron2.data.common INFO: Serialized dataset takes 0.62 MiB +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Directory /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs and /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/D2masks has 2254 and 3280 files, respectively. +[03/31 22:08:31] detectron2.data.datasets.coco WARNING: Will use their intersection of 2254 files. +[03/31 22:08:31] detectron2.data.datasets.coco INFO: Loaded 2254 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/Vaihingen/imgs +[03/31 22:08:31] detectron2.evaluation.evaluator INFO: Start inference on 563 batches +[03/31 22:08:38] detectron2.evaluation.evaluator INFO: Inference done 11/563. Dataloading: 0.0007 s/iter. Inference: 0.0591 s/iter. Eval: 0.0027 s/iter. Total: 0.0625 s/iter. ETA=0:00:34 +[03/31 22:08:43] detectron2.evaluation.evaluator INFO: Inference done 90/563. Dataloading: 0.0012 s/iter. Inference: 0.0592 s/iter. Eval: 0.0028 s/iter. Total: 0.0633 s/iter. ETA=0:00:29 +[03/31 22:08:48] detectron2.evaluation.evaluator INFO: Inference done 170/563. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0028 s/iter. Total: 0.0633 s/iter. ETA=0:00:24 +[03/31 22:08:53] detectron2.evaluation.evaluator INFO: Inference done 249/563. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0028 s/iter. Total: 0.0634 s/iter. ETA=0:00:19 +[03/31 22:08:58] detectron2.evaluation.evaluator INFO: Inference done 329/563. Dataloading: 0.0013 s/iter. Inference: 0.0592 s/iter. Eval: 0.0028 s/iter. Total: 0.0633 s/iter. ETA=0:00:14 +[03/31 22:09:03] detectron2.evaluation.evaluator INFO: Inference done 408/563. Dataloading: 0.0014 s/iter. Inference: 0.0593 s/iter. Eval: 0.0027 s/iter. Total: 0.0634 s/iter. ETA=0:00:09 +[03/31 22:09:08] detectron2.evaluation.evaluator INFO: Inference done 488/563. Dataloading: 0.0014 s/iter. Inference: 0.0592 s/iter. Eval: 0.0027 s/iter. Total: 0.0633 s/iter. ETA=0:00:04 +[03/31 22:09:14] detectron2.evaluation.evaluator INFO: Total inference time: 0:00:36.165905 (0.064813 s / iter per device, on 4 devices) +[03/31 22:09:14] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:33 (0.059237 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-flair/config.yaml b/OVRSIS/eval-flair/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..98ad27c4dd331406ff035595f443cb318ed80d63 --- /dev/null +++ b/OVRSIS/eval-flair/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - FLAIR_test_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/flair.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-flair +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-flair/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-flair/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-flair/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-flair/inference/sem_seg_evaluation.pth b/OVRSIS/eval-flair/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..a061e07bc9ea1eeb508c9d62b5d61443f4ea7f71 --- /dev/null +++ b/OVRSIS/eval-flair/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:512e89f8fa94ecdb5616afe2631501412a09008bb219ff8fa8d47af28a6e0a7a +size 4512 diff --git a/OVRSIS/eval-flair/inference/sem_seg_predictions.json b/OVRSIS/eval-flair/inference/sem_seg_predictions.json new file mode 100644 index 0000000000000000000000000000000000000000..681d47ae72e69d8e72258c6644e4fec9e7273603 --- /dev/null +++ b/OVRSIS/eval-flair/inference/sem_seg_predictions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7370e3891b93663fced46ca64a9b7f4eadf8fe6a50fcb91034c97b0b6d9e25b5 +size 210075222 diff --git a/OVRSIS/eval-flair/log.txt b/OVRSIS/eval-flair/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..f1bccbb21a62b6ae46161c2ee9a7234389e573d6 --- /dev/null +++ b/OVRSIS/eval-flair/log.txt @@ -0,0 +1,1053 @@ +[03/31 22:15:30] detectron2 INFO: Rank of current process: 0. World size: 4 +[03/31 22:15:31] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:15:31] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-flair', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/flair.json', 'DATASETS.TEST', '("FLAIR_test_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:15:31] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:15:31] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - FLAIR_test_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/flair.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-flair +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 22:15:31] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-flair/config.yaml +[03/31 22:15:31] d2.utils.env INFO: Using a generated random seed 35835003 +[03/31 22:15:34] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:15:34] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:15:34] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:15:35] d2.data.datasets.coco INFO: Loaded 15700 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/FLAIR_test/image +[03/31 22:15:35] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:15:35] d2.data.common INFO: Serializing the dataset using: +[03/31 22:15:35] d2.data.common INFO: Serializing 15700 elements to byte tensors and concatenating them all ... +[03/31 22:15:35] d2.data.common INFO: Serialized dataset takes 3.79 MiB +[03/31 22:15:36] d2.data.datasets.coco INFO: Loaded 15700 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/FLAIR_test/image +[03/31 22:15:36] d2.evaluation.evaluator INFO: Start inference on 3925 batches +[03/31 22:15:42] d2.evaluation.evaluator INFO: Inference done 11/3925. 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ETA=0:00:13 +[03/31 22:20:30] d2.evaluation.evaluator INFO: Inference done 3814/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:00:08 +[03/31 22:20:35] d2.evaluation.evaluator INFO: Inference done 3881/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0112 s/iter. Total: 0.0755 s/iter. ETA=0:00:03 +[03/31 22:20:39] d2.evaluation.evaluator INFO: Total inference time: 0:04:57.117525 (0.075795 s / iter per device, on 4 devices) +[03/31 22:20:39] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:04:05 (0.062718 s / iter per device, on 4 devices) +[03/31 22:20:41] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 22.96535219604059, 'fwIoU': 26.419427457549823, 'IoU-building': 72.50846275727609, 'BoundaryIoU-building': 76.76097691000206, 'min(IoU, B-Iou)-building': 72.50846275727609, 'IoU-pervious surface': 4.128157408368091, 'BoundaryIoU-pervious surface': 2.672250736042672, 'min(IoU, B-Iou)-pervious surface': 2.672250736042672, 'IoU-impervious surface': 34.220005517570016, 'BoundaryIoU-impervious surface': 23.319244698130664, 'min(IoU, B-Iou)-impervious surface': 23.319244698130664, 'IoU-bare soil': 5.687006868614864, 'BoundaryIoU-bare soil': 5.1347796134255335, 'min(IoU, B-Iou)-bare soil': 5.1347796134255335, 'IoU-water': 58.12465815974135, 'BoundaryIoU-water': 19.679063549661603, 'min(IoU, B-Iou)-water': 19.679063549661603, 'IoU-coniferous': 14.112902677412425, 'BoundaryIoU-coniferous': 5.813869188472643, 'min(IoU, B-Iou)-coniferous': 5.813869188472643, 'IoU-deciduous': 50.353750171438264, 'BoundaryIoU-deciduous': 27.74085268862882, 'min(IoU, B-Iou)-deciduous': 27.74085268862882, 'IoU-brushwood': 5.13752497606008, 'BoundaryIoU-brushwood': 5.997634234997526, 'min(IoU, B-Iou)-brushwood': 5.13752497606008, 'IoU-vineyard': 0.2019535233503148, 'BoundaryIoU-vineyard': 4.79421130208077, 'min(IoU, B-Iou)-vineyard': 0.2019535233503148, 'IoU-herbaceous vegetation': 7.733338587274659, 'BoundaryIoU-herbaceous vegetation': 8.22943844104365, 'min(IoU, B-Iou)-herbaceous vegetation': 7.733338587274659, 'IoU-agricultural land': 21.431678057328867, 'BoundaryIoU-agricultural land': 19.166473487399223, 'min(IoU, B-Iou)-agricultural land': 19.166473487399223, 'IoU-plowed land': 1.944787648052019, 'BoundaryIoU-plowed land': 2.130086998552636, 'min(IoU, B-Iou)-plowed land': 1.944787648052019, 'mACC': 37.475831010377796, 'pACC': 41.23387119294828, 'ACC-building': 84.8214809258018, 'ACC-pervious surface': 6.875530747115098, 'ACC-impervious surface': 58.5691931402876, 'ACC-bare soil': 11.212809745569267, 'ACC-water': 89.25496317745069, 'ACC-coniferous': 23.09286810199037, 'ACC-deciduous': 68.72096016503569, 'ACC-brushwood': 6.171499807893832, 'ACC-vineyard': 0.20546163055372899, 'ACC-herbaceous vegetation': 8.669378049056833, 'ACC-agricultural land': 88.81191065037962, 'ACC-plowed land': 3.303915983399001})]) +[03/31 22:20:41] d2.engine.defaults INFO: Evaluation results for FLAIR_test_sem_seg in csv format: +[03/31 22:20:41] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:20:41] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:20:41] d2.evaluation.testing INFO: copypaste: 22.9654,26.4194,37.4758,41.2339 diff --git a/OVRSIS/eval-flair/log.txt.rank1 b/OVRSIS/eval-flair/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..409f25859d17a4c5156dfb05c11e8a659394d697 --- /dev/null +++ b/OVRSIS/eval-flair/log.txt.rank1 @@ -0,0 +1,622 @@ +[03/31 22:15:30] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 22:15:32] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:15:32] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-flair', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/flair.json', 'DATASETS.TEST', '("FLAIR_test_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:15:32] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:15:32] detectron2.utils.env INFO: Using a generated random seed 36150020 +[03/31 22:15:34] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:15:34] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:15:34] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:15:35] detectron2.data.datasets.coco INFO: Loaded 15700 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/FLAIR_test/image +[03/31 22:15:35] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:15:35] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:15:35] detectron2.data.common INFO: Serializing 15700 elements to byte tensors and concatenating them all ... +[03/31 22:15:35] detectron2.data.common INFO: Serialized dataset takes 3.79 MiB +[03/31 22:15:36] detectron2.data.datasets.coco INFO: Loaded 15700 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/FLAIR_test/image +[03/31 22:15:36] detectron2.evaluation.evaluator INFO: Start inference on 3925 batches +[03/31 22:15:42] detectron2.evaluation.evaluator INFO: Inference done 11/3925. Dataloading: 0.0008 s/iter. Inference: 0.0625 s/iter. Eval: 0.0108 s/iter. Total: 0.0741 s/iter. ETA=0:04:49 +[03/31 22:15:48] detectron2.evaluation.evaluator INFO: Inference done 78/3925. Dataloading: 0.0014 s/iter. Inference: 0.0628 s/iter. Eval: 0.0113 s/iter. Total: 0.0755 s/iter. ETA=0:04:50 +[03/31 22:15:53] detectron2.evaluation.evaluator INFO: Inference done 145/3925. Dataloading: 0.0014 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0755 s/iter. ETA=0:04:45 +[03/31 22:15:58] detectron2.evaluation.evaluator INFO: Inference done 213/3925. Dataloading: 0.0015 s/iter. Inference: 0.0628 s/iter. Eval: 0.0106 s/iter. Total: 0.0749 s/iter. ETA=0:04:38 +[03/31 22:16:03] detectron2.evaluation.evaluator INFO: Inference done 282/3925. Dataloading: 0.0015 s/iter. Inference: 0.0629 s/iter. Eval: 0.0100 s/iter. Total: 0.0745 s/iter. ETA=0:04:31 +[03/31 22:16:08] detectron2.evaluation.evaluator INFO: Inference done 350/3925. Dataloading: 0.0015 s/iter. Inference: 0.0629 s/iter. Eval: 0.0099 s/iter. Total: 0.0743 s/iter. ETA=0:04:25 +[03/31 22:16:13] detectron2.evaluation.evaluator INFO: Inference done 417/3925. Dataloading: 0.0015 s/iter. Inference: 0.0630 s/iter. Eval: 0.0100 s/iter. Total: 0.0745 s/iter. ETA=0:04:21 +[03/31 22:16:18] detectron2.evaluation.evaluator INFO: Inference done 481/3925. Dataloading: 0.0015 s/iter. Inference: 0.0631 s/iter. Eval: 0.0103 s/iter. Total: 0.0750 s/iter. ETA=0:04:18 +[03/31 22:16:23] detectron2.evaluation.evaluator INFO: Inference done 548/3925. Dataloading: 0.0015 s/iter. Inference: 0.0630 s/iter. Eval: 0.0104 s/iter. Total: 0.0750 s/iter. ETA=0:04:13 +[03/31 22:16:28] detectron2.evaluation.evaluator INFO: Inference done 616/3925. Dataloading: 0.0016 s/iter. Inference: 0.0631 s/iter. Eval: 0.0103 s/iter. Total: 0.0750 s/iter. ETA=0:04:08 +[03/31 22:16:33] detectron2.evaluation.evaluator INFO: Inference done 684/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0103 s/iter. Total: 0.0749 s/iter. ETA=0:04:02 +[03/31 22:16:38] detectron2.evaluation.evaluator INFO: Inference done 751/3925. Dataloading: 0.0016 s/iter. Inference: 0.0631 s/iter. Eval: 0.0102 s/iter. Total: 0.0749 s/iter. ETA=0:03:57 +[03/31 22:16:43] detectron2.evaluation.evaluator INFO: Inference done 818/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0103 s/iter. Total: 0.0749 s/iter. ETA=0:03:52 +[03/31 22:16:48] detectron2.evaluation.evaluator INFO: Inference done 884/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0104 s/iter. Total: 0.0750 s/iter. ETA=0:03:48 +[03/31 22:16:53] detectron2.evaluation.evaluator INFO: Inference done 951/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0104 s/iter. Total: 0.0750 s/iter. ETA=0:03:42 +[03/31 22:16:58] detectron2.evaluation.evaluator INFO: Inference done 1018/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:03:37 +[03/31 22:17:03] detectron2.evaluation.evaluator INFO: Inference done 1085/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:03:32 +[03/31 22:17:08] detectron2.evaluation.evaluator INFO: Inference done 1152/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:03:27 +[03/31 22:17:13] detectron2.evaluation.evaluator INFO: Inference done 1218/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0751 s/iter. ETA=0:03:23 +[03/31 22:17:18] detectron2.evaluation.evaluator INFO: Inference done 1285/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:03:18 +[03/31 22:17:23] detectron2.evaluation.evaluator INFO: Inference done 1352/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:03:13 +[03/31 22:17:28] detectron2.evaluation.evaluator INFO: Inference done 1418/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0106 s/iter. Total: 0.0751 s/iter. ETA=0:03:08 +[03/31 22:17:33] detectron2.evaluation.evaluator INFO: Inference done 1486/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0751 s/iter. ETA=0:03:03 +[03/31 22:17:38] detectron2.evaluation.evaluator INFO: Inference done 1554/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:02:57 +[03/31 22:17:43] detectron2.evaluation.evaluator INFO: Inference done 1616/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0107 s/iter. Total: 0.0753 s/iter. ETA=0:02:53 +[03/31 22:17:48] detectron2.evaluation.evaluator INFO: Inference done 1683/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0107 s/iter. Total: 0.0753 s/iter. ETA=0:02:48 +[03/31 22:17:53] detectron2.evaluation.evaluator INFO: Inference done 1751/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0107 s/iter. Total: 0.0752 s/iter. ETA=0:02:43 +[03/31 22:17:58] detectron2.evaluation.evaluator INFO: Inference done 1819/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0106 s/iter. Total: 0.0752 s/iter. ETA=0:02:38 +[03/31 22:18:03] detectron2.evaluation.evaluator INFO: Inference done 1888/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0105 s/iter. Total: 0.0751 s/iter. ETA=0:02:32 +[03/31 22:18:08] detectron2.evaluation.evaluator INFO: Inference done 1955/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0105 s/iter. Total: 0.0751 s/iter. ETA=0:02:27 +[03/31 22:18:13] detectron2.evaluation.evaluator INFO: Inference done 2023/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0105 s/iter. Total: 0.0751 s/iter. ETA=0:02:22 +[03/31 22:18:19] detectron2.evaluation.evaluator INFO: Inference done 2091/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0104 s/iter. Total: 0.0750 s/iter. ETA=0:02:17 +[03/31 22:18:24] detectron2.evaluation.evaluator INFO: Inference done 2159/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0104 s/iter. Total: 0.0750 s/iter. ETA=0:02:12 +[03/31 22:18:29] detectron2.evaluation.evaluator INFO: Inference done 2227/3925. Dataloading: 0.0016 s/iter. Inference: 0.0630 s/iter. Eval: 0.0104 s/iter. Total: 0.0750 s/iter. ETA=0:02:07 +[03/31 22:18:34] detectron2.evaluation.evaluator INFO: Inference done 2294/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0104 s/iter. Total: 0.0750 s/iter. ETA=0:02:02 +[03/31 22:18:39] detectron2.evaluation.evaluator INFO: Inference done 2360/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:57 +[03/31 22:18:44] detectron2.evaluation.evaluator INFO: Inference done 2427/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:52 +[03/31 22:18:49] detectron2.evaluation.evaluator INFO: Inference done 2494/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:47 +[03/31 22:18:54] detectron2.evaluation.evaluator INFO: Inference done 2561/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:42 +[03/31 22:18:59] detectron2.evaluation.evaluator INFO: Inference done 2628/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:37 +[03/31 22:19:04] detectron2.evaluation.evaluator INFO: Inference done 2696/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:32 +[03/31 22:19:09] detectron2.evaluation.evaluator INFO: Inference done 2765/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:26 +[03/31 22:19:14] detectron2.evaluation.evaluator INFO: Inference done 2832/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:21 +[03/31 22:19:19] detectron2.evaluation.evaluator INFO: Inference done 2899/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:16 +[03/31 22:19:24] detectron2.evaluation.evaluator INFO: Inference done 2966/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:11 +[03/31 22:19:29] detectron2.evaluation.evaluator INFO: Inference done 3034/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:06 +[03/31 22:19:34] detectron2.evaluation.evaluator INFO: Inference done 3101/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0750 s/iter. ETA=0:01:01 +[03/31 22:19:39] detectron2.evaluation.evaluator INFO: Inference done 3169/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0749 s/iter. ETA=0:00:56 +[03/31 22:19:44] detectron2.evaluation.evaluator INFO: Inference done 3236/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0749 s/iter. ETA=0:00:51 +[03/31 22:19:49] detectron2.evaluation.evaluator INFO: Inference done 3304/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0749 s/iter. ETA=0:00:46 +[03/31 22:19:54] detectron2.evaluation.evaluator INFO: Inference done 3372/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0749 s/iter. ETA=0:00:41 +[03/31 22:19:59] detectron2.evaluation.evaluator INFO: Inference done 3440/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0749 s/iter. ETA=0:00:36 +[03/31 22:20:04] detectron2.evaluation.evaluator INFO: Inference done 3507/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0749 s/iter. ETA=0:00:31 +[03/31 22:20:09] detectron2.evaluation.evaluator INFO: Inference done 3575/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0749 s/iter. ETA=0:00:26 +[03/31 22:20:14] detectron2.evaluation.evaluator INFO: Inference done 3643/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0104 s/iter. Total: 0.0749 s/iter. ETA=0:00:21 +[03/31 22:20:19] detectron2.evaluation.evaluator INFO: Inference done 3711/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0104 s/iter. Total: 0.0748 s/iter. ETA=0:00:16 +[03/31 22:20:24] detectron2.evaluation.evaluator INFO: Inference done 3778/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0104 s/iter. Total: 0.0749 s/iter. ETA=0:00:11 +[03/31 22:20:29] detectron2.evaluation.evaluator INFO: Inference done 3845/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0749 s/iter. ETA=0:00:05 +[03/31 22:20:35] detectron2.evaluation.evaluator INFO: Inference done 3912/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0105 s/iter. Total: 0.0749 s/iter. ETA=0:00:00 +[03/31 22:20:36] detectron2.evaluation.evaluator INFO: Total inference time: 0:04:54.335444 (0.075086 s / iter per device, on 4 devices) +[03/31 22:20:36] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:04:06 (0.062801 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-flair/log.txt.rank2 b/OVRSIS/eval-flair/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..678b08a31961507655aa47e25192ada913f49992 --- /dev/null +++ b/OVRSIS/eval-flair/log.txt.rank2 @@ -0,0 +1,622 @@ +[03/31 22:15:30] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 22:15:31] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:15:31] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-flair', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/flair.json', 'DATASETS.TEST', '("FLAIR_test_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:15:31] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:15:31] detectron2.utils.env INFO: Using a generated random seed 35838860 +[03/31 22:15:34] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:15:34] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:15:34] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:15:35] detectron2.data.datasets.coco INFO: Loaded 15700 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/FLAIR_test/image +[03/31 22:15:35] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:15:35] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:15:35] detectron2.data.common INFO: Serializing 15700 elements to byte tensors and concatenating them all ... +[03/31 22:15:35] detectron2.data.common INFO: Serialized dataset takes 3.79 MiB +[03/31 22:15:36] detectron2.data.datasets.coco INFO: Loaded 15700 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/FLAIR_test/image +[03/31 22:15:36] detectron2.evaluation.evaluator INFO: Start inference on 3925 batches +[03/31 22:15:43] detectron2.evaluation.evaluator INFO: Inference done 11/3925. Dataloading: 0.0010 s/iter. Inference: 0.0623 s/iter. Eval: 0.0102 s/iter. Total: 0.0735 s/iter. ETA=0:04:47 +[03/31 22:15:48] detectron2.evaluation.evaluator INFO: Inference done 78/3925. Dataloading: 0.0015 s/iter. Inference: 0.0631 s/iter. Eval: 0.0104 s/iter. Total: 0.0750 s/iter. ETA=0:04:48 +[03/31 22:15:53] detectron2.evaluation.evaluator INFO: Inference done 144/3925. Dataloading: 0.0015 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0755 s/iter. ETA=0:04:45 +[03/31 22:15:58] detectron2.evaluation.evaluator INFO: Inference done 210/3925. Dataloading: 0.0015 s/iter. Inference: 0.0627 s/iter. Eval: 0.0115 s/iter. Total: 0.0758 s/iter. ETA=0:04:41 +[03/31 22:16:03] detectron2.evaluation.evaluator INFO: Inference done 276/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0116 s/iter. Total: 0.0760 s/iter. ETA=0:04:37 +[03/31 22:16:08] detectron2.evaluation.evaluator INFO: Inference done 342/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0116 s/iter. Total: 0.0760 s/iter. ETA=0:04:32 +[03/31 22:16:13] detectron2.evaluation.evaluator INFO: Inference done 408/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0114 s/iter. Total: 0.0759 s/iter. ETA=0:04:27 +[03/31 22:16:18] detectron2.evaluation.evaluator INFO: Inference done 473/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0117 s/iter. Total: 0.0762 s/iter. ETA=0:04:23 +[03/31 22:16:23] detectron2.evaluation.evaluator INFO: Inference done 540/3925. Dataloading: 0.0016 s/iter. Inference: 0.0629 s/iter. Eval: 0.0116 s/iter. Total: 0.0761 s/iter. ETA=0:04:17 +[03/31 22:16:28] detectron2.evaluation.evaluator INFO: Inference done 608/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0114 s/iter. Total: 0.0759 s/iter. ETA=0:04:11 +[03/31 22:16:33] detectron2.evaluation.evaluator INFO: Inference done 676/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0757 s/iter. ETA=0:04:05 +[03/31 22:16:38] detectron2.evaluation.evaluator INFO: Inference done 742/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0757 s/iter. ETA=0:04:01 +[03/31 22:16:43] detectron2.evaluation.evaluator INFO: Inference done 809/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0113 s/iter. Total: 0.0757 s/iter. ETA=0:03:55 +[03/31 22:16:48] detectron2.evaluation.evaluator INFO: Inference done 876/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0757 s/iter. ETA=0:03:50 +[03/31 22:16:53] detectron2.evaluation.evaluator INFO: Inference done 943/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:03:45 +[03/31 22:16:58] detectron2.evaluation.evaluator INFO: Inference done 1010/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:03:40 +[03/31 22:17:03] detectron2.evaluation.evaluator INFO: Inference done 1077/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:03:35 +[03/31 22:17:08] detectron2.evaluation.evaluator INFO: Inference done 1145/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:03:29 +[03/31 22:17:13] detectron2.evaluation.evaluator INFO: Inference done 1212/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0111 s/iter. Total: 0.0754 s/iter. ETA=0:03:24 +[03/31 22:17:18] detectron2.evaluation.evaluator INFO: Inference done 1279/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0111 s/iter. Total: 0.0754 s/iter. ETA=0:03:19 +[03/31 22:17:23] detectron2.evaluation.evaluator INFO: Inference done 1347/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0111 s/iter. Total: 0.0754 s/iter. ETA=0:03:14 +[03/31 22:17:28] detectron2.evaluation.evaluator INFO: Inference done 1414/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0110 s/iter. Total: 0.0754 s/iter. ETA=0:03:09 +[03/31 22:17:33] detectron2.evaluation.evaluator INFO: Inference done 1481/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0110 s/iter. Total: 0.0754 s/iter. ETA=0:03:04 +[03/31 22:17:38] detectron2.evaluation.evaluator INFO: Inference done 1548/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0110 s/iter. Total: 0.0754 s/iter. ETA=0:02:59 +[03/31 22:17:43] detectron2.evaluation.evaluator INFO: Inference done 1610/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0756 s/iter. ETA=0:02:54 +[03/31 22:17:48] detectron2.evaluation.evaluator INFO: Inference done 1677/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:02:49 +[03/31 22:17:53] detectron2.evaluation.evaluator INFO: Inference done 1744/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:02:44 +[03/31 22:17:59] detectron2.evaluation.evaluator INFO: Inference done 1811/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0756 s/iter. ETA=0:02:39 +[03/31 22:18:04] detectron2.evaluation.evaluator INFO: Inference done 1877/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:02:34 +[03/31 22:18:09] detectron2.evaluation.evaluator INFO: Inference done 1942/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:02:30 +[03/31 22:18:14] detectron2.evaluation.evaluator INFO: Inference done 2008/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0757 s/iter. ETA=0:02:25 +[03/31 22:18:19] detectron2.evaluation.evaluator INFO: Inference done 2075/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0757 s/iter. ETA=0:02:19 +[03/31 22:18:24] detectron2.evaluation.evaluator INFO: Inference done 2141/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0757 s/iter. ETA=0:02:14 +[03/31 22:18:29] detectron2.evaluation.evaluator INFO: Inference done 2208/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0113 s/iter. Total: 0.0757 s/iter. ETA=0:02:09 +[03/31 22:18:34] detectron2.evaluation.evaluator INFO: Inference done 2275/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0113 s/iter. Total: 0.0757 s/iter. ETA=0:02:04 +[03/31 22:18:39] detectron2.evaluation.evaluator INFO: Inference done 2341/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0113 s/iter. Total: 0.0757 s/iter. ETA=0:01:59 +[03/31 22:18:44] detectron2.evaluation.evaluator INFO: Inference done 2408/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0113 s/iter. Total: 0.0757 s/iter. ETA=0:01:54 +[03/31 22:18:49] detectron2.evaluation.evaluator INFO: Inference done 2475/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0113 s/iter. Total: 0.0757 s/iter. ETA=0:01:49 +[03/31 22:18:54] detectron2.evaluation.evaluator INFO: Inference done 2542/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0757 s/iter. ETA=0:01:44 +[03/31 22:18:59] detectron2.evaluation.evaluator INFO: Inference done 2609/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:01:39 +[03/31 22:19:04] detectron2.evaluation.evaluator INFO: Inference done 2676/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:01:34 +[03/31 22:19:09] detectron2.evaluation.evaluator INFO: Inference done 2745/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0756 s/iter. ETA=0:01:29 +[03/31 22:19:14] detectron2.evaluation.evaluator INFO: Inference done 2811/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:01:24 +[03/31 22:19:19] detectron2.evaluation.evaluator INFO: Inference done 2878/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0756 s/iter. ETA=0:01:19 +[03/31 22:19:24] detectron2.evaluation.evaluator INFO: Inference done 2945/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0756 s/iter. ETA=0:01:14 +[03/31 22:19:29] detectron2.evaluation.evaluator INFO: Inference done 3012/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0112 s/iter. Total: 0.0755 s/iter. ETA=0:01:08 +[03/31 22:19:34] detectron2.evaluation.evaluator INFO: Inference done 3079/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:01:03 +[03/31 22:19:39] detectron2.evaluation.evaluator INFO: Inference done 3146/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:58 +[03/31 22:19:44] detectron2.evaluation.evaluator INFO: Inference done 3213/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:53 +[03/31 22:19:49] detectron2.evaluation.evaluator INFO: Inference done 3280/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:48 +[03/31 22:19:54] detectron2.evaluation.evaluator INFO: Inference done 3346/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:43 +[03/31 22:20:00] detectron2.evaluation.evaluator INFO: Inference done 3414/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:38 +[03/31 22:20:05] detectron2.evaluation.evaluator INFO: Inference done 3481/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:33 +[03/31 22:20:10] detectron2.evaluation.evaluator INFO: Inference done 3548/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:28 +[03/31 22:20:15] detectron2.evaluation.evaluator INFO: Inference done 3615/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:23 +[03/31 22:20:20] detectron2.evaluation.evaluator INFO: Inference done 3682/3925. Dataloading: 0.0016 s/iter. Inference: 0.0628 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:18 +[03/31 22:20:25] detectron2.evaluation.evaluator INFO: Inference done 3750/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0111 s/iter. Total: 0.0755 s/iter. ETA=0:00:13 +[03/31 22:20:30] detectron2.evaluation.evaluator INFO: Inference done 3817/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0111 s/iter. Total: 0.0754 s/iter. ETA=0:00:08 +[03/31 22:20:35] detectron2.evaluation.evaluator INFO: Inference done 3885/3925. Dataloading: 0.0016 s/iter. Inference: 0.0627 s/iter. Eval: 0.0111 s/iter. Total: 0.0754 s/iter. ETA=0:00:03 +[03/31 22:20:39] detectron2.evaluation.evaluator INFO: Total inference time: 0:04:56.649759 (0.075676 s / iter per device, on 4 devices) +[03/31 22:20:39] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:04:05 (0.062735 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-flair/log.txt.rank3 b/OVRSIS/eval-flair/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..852d31b3d9ae685da7b49cbe28b62a50006a2565 --- /dev/null +++ b/OVRSIS/eval-flair/log.txt.rank3 @@ -0,0 +1,622 @@ +[03/31 22:15:30] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 22:15:32] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:15:32] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-flair', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/flair.json', 'DATASETS.TEST', '("FLAIR_test_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:15:32] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:15:32] detectron2.utils.env INFO: Using a generated random seed 36542543 +[03/31 22:15:34] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:15:34] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:15:34] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:15:35] detectron2.data.datasets.coco INFO: Loaded 15700 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/FLAIR_test/image +[03/31 22:15:35] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:15:35] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:15:35] detectron2.data.common INFO: Serializing 15700 elements to byte tensors and concatenating them all ... +[03/31 22:15:35] detectron2.data.common INFO: Serialized dataset takes 3.79 MiB +[03/31 22:15:36] detectron2.data.datasets.coco INFO: Loaded 15700 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/FLAIR_test/image +[03/31 22:15:36] detectron2.evaluation.evaluator INFO: Start inference on 3925 batches +[03/31 22:15:42] detectron2.evaluation.evaluator INFO: Inference done 11/3925. Dataloading: 0.0009 s/iter. Inference: 0.0630 s/iter. Eval: 0.0098 s/iter. Total: 0.0738 s/iter. ETA=0:04:48 +[03/31 22:15:48] detectron2.evaluation.evaluator INFO: Inference done 78/3925. Dataloading: 0.0014 s/iter. Inference: 0.0625 s/iter. Eval: 0.0109 s/iter. Total: 0.0749 s/iter. ETA=0:04:48 +[03/31 22:15:53] detectron2.evaluation.evaluator INFO: Inference done 146/3925. Dataloading: 0.0015 s/iter. Inference: 0.0627 s/iter. Eval: 0.0103 s/iter. Total: 0.0745 s/iter. ETA=0:04:41 +[03/31 22:15:58] detectron2.evaluation.evaluator INFO: Inference done 212/3925. Dataloading: 0.0015 s/iter. Inference: 0.0627 s/iter. Eval: 0.0111 s/iter. Total: 0.0753 s/iter. ETA=0:04:39 +[03/31 22:16:03] detectron2.evaluation.evaluator INFO: Inference done 278/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0114 s/iter. Total: 0.0756 s/iter. ETA=0:04:35 +[03/31 22:16:08] detectron2.evaluation.evaluator INFO: Inference done 344/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0115 s/iter. Total: 0.0756 s/iter. ETA=0:04:30 +[03/31 22:16:13] detectron2.evaluation.evaluator INFO: Inference done 410/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0116 s/iter. Total: 0.0758 s/iter. ETA=0:04:26 +[03/31 22:16:18] detectron2.evaluation.evaluator INFO: Inference done 475/3925. Dataloading: 0.0015 s/iter. Inference: 0.0627 s/iter. Eval: 0.0118 s/iter. Total: 0.0761 s/iter. ETA=0:04:22 +[03/31 22:16:23] detectron2.evaluation.evaluator INFO: Inference done 541/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0119 s/iter. Total: 0.0761 s/iter. ETA=0:04:17 +[03/31 22:16:28] detectron2.evaluation.evaluator INFO: Inference done 607/3925. Dataloading: 0.0015 s/iter. Inference: 0.0627 s/iter. Eval: 0.0119 s/iter. Total: 0.0761 s/iter. ETA=0:04:12 +[03/31 22:16:33] detectron2.evaluation.evaluator INFO: Inference done 674/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0119 s/iter. Total: 0.0761 s/iter. ETA=0:04:07 +[03/31 22:16:38] detectron2.evaluation.evaluator INFO: Inference done 740/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0119 s/iter. Total: 0.0761 s/iter. ETA=0:04:02 +[03/31 22:16:43] detectron2.evaluation.evaluator INFO: Inference done 807/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0118 s/iter. Total: 0.0760 s/iter. ETA=0:03:56 +[03/31 22:16:48] detectron2.evaluation.evaluator INFO: Inference done 874/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:03:51 +[03/31 22:16:53] detectron2.evaluation.evaluator INFO: Inference done 941/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:03:46 +[03/31 22:16:58] detectron2.evaluation.evaluator INFO: Inference done 1008/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:03:41 +[03/31 22:17:03] detectron2.evaluation.evaluator INFO: Inference done 1074/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:03:36 +[03/31 22:17:08] detectron2.evaluation.evaluator INFO: Inference done 1140/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:03:31 +[03/31 22:17:13] detectron2.evaluation.evaluator INFO: Inference done 1207/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:03:26 +[03/31 22:17:18] detectron2.evaluation.evaluator INFO: Inference done 1274/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0758 s/iter. ETA=0:03:21 +[03/31 22:17:23] detectron2.evaluation.evaluator INFO: Inference done 1340/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0758 s/iter. ETA=0:03:16 +[03/31 22:17:28] detectron2.evaluation.evaluator INFO: Inference done 1406/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0758 s/iter. ETA=0:03:11 +[03/31 22:17:33] detectron2.evaluation.evaluator INFO: Inference done 1473/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0758 s/iter. ETA=0:03:05 +[03/31 22:17:38] detectron2.evaluation.evaluator INFO: Inference done 1540/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0117 s/iter. Total: 0.0758 s/iter. ETA=0:03:00 +[03/31 22:17:43] detectron2.evaluation.evaluator INFO: Inference done 1602/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0119 s/iter. Total: 0.0760 s/iter. ETA=0:02:56 +[03/31 22:17:48] detectron2.evaluation.evaluator INFO: Inference done 1670/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:51 +[03/31 22:17:53] detectron2.evaluation.evaluator INFO: Inference done 1737/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:46 +[03/31 22:17:59] detectron2.evaluation.evaluator INFO: Inference done 1803/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:41 +[03/31 22:18:04] detectron2.evaluation.evaluator INFO: Inference done 1869/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:36 +[03/31 22:18:09] detectron2.evaluation.evaluator INFO: Inference done 1935/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0119 s/iter. Total: 0.0760 s/iter. ETA=0:02:31 +[03/31 22:18:14] detectron2.evaluation.evaluator INFO: Inference done 2002/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:25 +[03/31 22:18:19] detectron2.evaluation.evaluator INFO: Inference done 2068/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:20 +[03/31 22:18:24] detectron2.evaluation.evaluator INFO: Inference done 2134/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:15 +[03/31 22:18:29] detectron2.evaluation.evaluator INFO: Inference done 2200/3925. Dataloading: 0.0015 s/iter. Inference: 0.0625 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:10 +[03/31 22:18:34] detectron2.evaluation.evaluator INFO: Inference done 2268/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:05 +[03/31 22:18:39] detectron2.evaluation.evaluator INFO: Inference done 2334/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0118 s/iter. Total: 0.0759 s/iter. ETA=0:02:00 +[03/31 22:18:44] detectron2.evaluation.evaluator INFO: Inference done 2401/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0117 s/iter. Total: 0.0759 s/iter. ETA=0:01:55 +[03/31 22:18:49] detectron2.evaluation.evaluator INFO: Inference done 2468/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0117 s/iter. Total: 0.0758 s/iter. ETA=0:01:50 +[03/31 22:18:54] detectron2.evaluation.evaluator INFO: Inference done 2534/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0117 s/iter. Total: 0.0758 s/iter. ETA=0:01:45 +[03/31 22:18:59] detectron2.evaluation.evaluator INFO: Inference done 2600/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0117 s/iter. Total: 0.0759 s/iter. ETA=0:01:40 +[03/31 22:19:04] detectron2.evaluation.evaluator INFO: Inference done 2667/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0117 s/iter. Total: 0.0758 s/iter. 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ETA=0:00:18 +[03/31 22:20:24] detectron2.evaluation.evaluator INFO: Inference done 3744/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0114 s/iter. Total: 0.0755 s/iter. ETA=0:00:13 +[03/31 22:20:29] detectron2.evaluation.evaluator INFO: Inference done 3812/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0114 s/iter. Total: 0.0755 s/iter. ETA=0:00:08 +[03/31 22:20:35] detectron2.evaluation.evaluator INFO: Inference done 3880/3925. Dataloading: 0.0015 s/iter. Inference: 0.0626 s/iter. Eval: 0.0113 s/iter. Total: 0.0755 s/iter. ETA=0:00:03 +[03/31 22:20:39] detectron2.evaluation.evaluator INFO: Total inference time: 0:04:56.859338 (0.075729 s / iter per device, on 4 devices) +[03/31 22:20:39] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:04:05 (0.062571 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-iSAID/config.yaml b/OVRSIS/eval-iSAID/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..86798e5d6e64a99e40cb2c3ca91d2631d2ac250d --- /dev/null +++ b/OVRSIS/eval-iSAID/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - iSAID_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/iSAID.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-iSAID +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-iSAID/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-iSAID/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-iSAID/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-iSAID/inference/sem_seg_evaluation.pth b/OVRSIS/eval-iSAID/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..7f334c22506aac7e687edea5f6866b299ebea7fe --- /dev/null +++ b/OVRSIS/eval-iSAID/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:42dfcbe5737e9bb1194d19a22dbf0e034f6b8a488aabc1e0618f8daa789ba8a7 +size 5472 diff --git a/OVRSIS/eval-iSAID/inference/sem_seg_predictions.json b/OVRSIS/eval-iSAID/inference/sem_seg_predictions.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c079d3720a1759b1a3eb0cc2dc55d9e8f1a5b --- /dev/null +++ b/OVRSIS/eval-iSAID/inference/sem_seg_predictions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7cb9c8b1a06b59a5dab52ee8f3ceae19b11f3e93784a638e3821a8a998959156 +size 127289929 diff --git a/OVRSIS/eval-iSAID/log.txt b/OVRSIS/eval-iSAID/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..187515dffb382b2ac9408a059a38cf789ac4ec58 --- /dev/null +++ b/OVRSIS/eval-iSAID/log.txt @@ -0,0 +1,1077 @@ +[03/31 21:52:42] detectron2 INFO: Rank of current process: 0. World size: 4 +[03/31 21:52:44] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 21:52:44] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-iSAID', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/iSAID.json', 'DATASETS.TEST', '("iSAID_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 21:52:44] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 21:52:44] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - iSAID_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/iSAID.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-iSAID +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 21:52:44] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-iSAID/config.yaml +[03/31 21:52:44] d2.utils.env INFO: Using a generated random seed 48436666 +[03/31 21:52:47] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 21:52:47] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 21:52:47] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 21:52:48] d2.data.datasets.coco INFO: Loaded 24439 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/iSAID/imgs +[03/31 21:52:48] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 21:52:48] d2.data.common INFO: Serializing the dataset using: +[03/31 21:52:48] d2.data.common INFO: Serializing 24439 elements to byte tensors and concatenating them all ... +[03/31 21:52:48] d2.data.common INFO: Serialized dataset takes 6.50 MiB +[03/31 21:52:49] d2.data.datasets.coco INFO: Loaded 24439 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/iSAID/imgs +[03/31 21:52:49] d2.evaluation.evaluator INFO: Start inference on 6110 batches +[03/31 21:53:12] d2.evaluation.evaluator INFO: Inference done 11/6110. Dataloading: 0.0013 s/iter. Inference: 0.0650 s/iter. Eval: 0.0026 s/iter. Total: 0.0690 s/iter. ETA=0:07:00 +[03/31 21:53:17] d2.evaluation.evaluator INFO: Inference done 85/6110. Dataloading: 0.0013 s/iter. Inference: 0.0641 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:51 +[03/31 21:53:23] d2.evaluation.evaluator INFO: Inference done 159/6110. Dataloading: 0.0013 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:45 +[03/31 21:53:28] d2.evaluation.evaluator INFO: Inference done 233/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:40 +[03/31 21:53:33] d2.evaluation.evaluator INFO: Inference done 307/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0682 s/iter. ETA=0:06:35 +[03/31 21:53:38] d2.evaluation.evaluator INFO: Inference done 381/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:30 +[03/31 21:53:43] d2.evaluation.evaluator INFO: Inference done 455/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:25 +[03/31 21:53:48] d2.evaluation.evaluator INFO: Inference done 529/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:20 +[03/31 21:53:53] d2.evaluation.evaluator INFO: Inference done 603/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:15 +[03/31 21:53:58] d2.evaluation.evaluator INFO: Inference done 677/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:10 +[03/31 21:54:03] d2.evaluation.evaluator INFO: Inference done 751/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:05 +[03/31 21:54:08] d2.evaluation.evaluator INFO: Inference done 825/6110. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:05:59 +[03/31 21:54:13] d2.evaluation.evaluator INFO: Inference done 898/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:05:55 +[03/31 21:54:18] d2.evaluation.evaluator INFO: Inference done 971/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:05:50 +[03/31 21:54:23] d2.evaluation.evaluator INFO: Inference done 1043/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:45 +[03/31 21:54:28] d2.evaluation.evaluator INFO: Inference done 1117/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:40 +[03/31 21:54:33] d2.evaluation.evaluator INFO: Inference done 1191/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:35 +[03/31 21:54:38] d2.evaluation.evaluator INFO: Inference done 1265/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:30 +[03/31 21:54:43] d2.evaluation.evaluator INFO: Inference done 1338/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:25 +[03/31 21:54:48] d2.evaluation.evaluator INFO: Inference done 1412/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:20 +[03/31 21:54:53] d2.evaluation.evaluator INFO: Inference done 1485/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:15 +[03/31 21:54:58] d2.evaluation.evaluator INFO: Inference done 1559/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:10 +[03/31 21:55:03] d2.evaluation.evaluator INFO: Inference done 1633/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:05 +[03/31 21:55:08] d2.evaluation.evaluator INFO: Inference done 1706/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:00 +[03/31 21:55:13] d2.evaluation.evaluator INFO: Inference done 1779/6110. Dataloading: 0.0014 s/iter. Inference: 0.0640 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:04:55 +[03/31 21:55:18] d2.evaluation.evaluator INFO: Inference done 1853/6110. Dataloading: 0.0014 s/iter. Inference: 0.0640 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:04:50 +[03/31 21:55:23] d2.evaluation.evaluator INFO: Inference done 1927/6110. Dataloading: 0.0014 s/iter. Inference: 0.0640 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. 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ETA=0:00:03 +[03/31 22:00:10] d2.evaluation.evaluator INFO: Total inference time: 0:06:57.974854 (0.068464 s / iter per device, on 4 devices) +[03/31 22:00:10] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:06:30 (0.063925 s / iter per device, on 4 devices) +[03/31 22:00:12] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 47.53790185380044, 'fwIoU': 58.49954597386547, 'IoU-ship': 62.148909187518754, 'BoundaryIoU-ship': 77.79494168912656, 'min(IoU, B-Iou)-ship': 62.148909187518754, 'IoU-storage tank': 83.52132970417587, 'BoundaryIoU-storage tank': 1.9870871629657572, 'min(IoU, B-Iou)-storage tank': 1.9870871629657572, 'IoU-baseball diamond': 42.0331458785553, 'BoundaryIoU-baseball diamond': 1.4155682239365845, 'min(IoU, B-Iou)-baseball diamond': 1.4155682239365845, 'IoU-tennis court': 68.73554429422559, 'BoundaryIoU-tennis court': 11.923358326455835, 'min(IoU, B-Iou)-tennis court': 11.923358326455835, 'IoU-basketball court': 22.09329277162808, 'BoundaryIoU-basketball court': 2.141632845243529, 'min(IoU, B-Iou)-basketball court': 2.141632845243529, 'IoU-ground track field': 18.33606099944613, 'BoundaryIoU-ground track field': 5.41081298125558, 'min(IoU, B-Iou)-ground track field': 5.41081298125558, 'IoU-bridge': 34.45977528639605, 'BoundaryIoU-bridge': 0.9142724551554812, 'min(IoU, B-Iou)-bridge': 0.9142724551554812, 'IoU-large vehicle': 68.31265355105305, 'BoundaryIoU-large vehicle': 7.478426577990742, 'min(IoU, B-Iou)-large vehicle': 7.478426577990742, 'IoU-small vehicle': 62.954455997690026, 'BoundaryIoU-small vehicle': 4.041794460317103, 'min(IoU, B-Iou)-small vehicle': 4.041794460317103, 'IoU-helicopter': 3.106092054517499, 'BoundaryIoU-helicopter': 0.07289681738613552, 'min(IoU, B-Iou)-helicopter': 0.07289681738613552, 'IoU-swimming pool': 6.060230174323875, 'BoundaryIoU-swimming pool': 1.4027616611321625, 'min(IoU, B-Iou)-swimming pool': 1.4027616611321625, 'IoU-roundabout': 40.60092919819415, 'BoundaryIoU-roundabout': 7.5295327450477, 'min(IoU, B-Iou)-roundabout': 7.5295327450477, 'IoU-soccer ball field': 59.55122327356327, 'BoundaryIoU-soccer ball field': 20.90546222154093, 'min(IoU, B-Iou)-soccer ball field': 20.90546222154093, 'IoU-plane': 85.03116849306484, 'BoundaryIoU-plane': 32.180791366501325, 'min(IoU, B-Iou)-plane': 32.180791366501325, 'IoU-harbor': 56.1237169426541, 'BoundaryIoU-harbor': 37.79498864563613, 'min(IoU, B-Iou)-harbor': 37.79498864563613, 'mACC': 66.94795127240099, 'pACC': 69.86914498828509, 'ACC-ship': 67.44064924979581, 'ACC-storage tank': 90.0601240213524, 'ACC-baseball diamond': 59.2478911249139, 'ACC-tennis court': 81.82316408434974, 'ACC-basketball court': 53.617527116720595, 'ACC-ground track field': 20.115305988632755, 'ACC-bridge': 83.04009699959516, 'ACC-large vehicle': 89.66916156775348, 'ACC-small vehicle': 69.94478323136832, 'ACC-helicopter': 3.7548254700715726, 'ACC-swimming pool': 95.28880225795882, 'ACC-roundabout': 61.94518198970255, 'ACC-soccer ball field': 68.23953369472785, 'ACC-plane': 87.81439263651463, 'ACC-harbor': 72.21782965255741})]) +[03/31 22:00:12] d2.engine.defaults INFO: Evaluation results for iSAID_all_sem_seg in csv format: +[03/31 22:00:12] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:00:12] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:00:12] d2.evaluation.testing INFO: copypaste: 47.5379,58.4995,66.9480,69.8691 diff --git a/OVRSIS/eval-iSAID/log.txt.rank1 b/OVRSIS/eval-iSAID/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..60fc27fa3a97e234c06b8489c44b138eae5ef9e3 --- /dev/null +++ b/OVRSIS/eval-iSAID/log.txt.rank1 @@ -0,0 +1,646 @@ +[03/31 21:52:42] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 21:52:45] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 21:52:45] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-iSAID', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/iSAID.json', 'DATASETS.TEST', '("iSAID_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 21:52:45] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 21:52:45] detectron2.utils.env INFO: Using a generated random seed 49095227 +[03/31 21:52:47] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 21:52:47] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 21:52:47] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 21:52:48] detectron2.data.datasets.coco INFO: Loaded 24439 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/iSAID/imgs +[03/31 21:52:48] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 21:52:48] detectron2.data.common INFO: Serializing the dataset using: +[03/31 21:52:48] detectron2.data.common INFO: Serializing 24439 elements to byte tensors and concatenating them all ... +[03/31 21:52:48] detectron2.data.common INFO: Serialized dataset takes 6.50 MiB +[03/31 21:52:49] detectron2.data.datasets.coco INFO: Loaded 24439 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/iSAID/imgs +[03/31 21:52:49] detectron2.evaluation.evaluator INFO: Start inference on 6110 batches +[03/31 21:53:13] detectron2.evaluation.evaluator INFO: Inference done 11/6110. Dataloading: 0.0009 s/iter. Inference: 0.0638 s/iter. Eval: 0.0020 s/iter. Total: 0.0668 s/iter. ETA=0:06:47 +[03/31 21:53:18] detectron2.evaluation.evaluator INFO: Inference done 85/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0682 s/iter. ETA=0:06:50 +[03/31 21:53:23] detectron2.evaluation.evaluator INFO: Inference done 159/6110. Dataloading: 0.0014 s/iter. Inference: 0.0640 s/iter. Eval: 0.0027 s/iter. Total: 0.0681 s/iter. ETA=0:06:45 +[03/31 21:53:28] detectron2.evaluation.evaluator INFO: Inference done 233/6110. Dataloading: 0.0014 s/iter. Inference: 0.0640 s/iter. Eval: 0.0027 s/iter. Total: 0.0681 s/iter. ETA=0:06:40 +[03/31 21:53:33] detectron2.evaluation.evaluator INFO: Inference done 307/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:35 +[03/31 21:53:38] detectron2.evaluation.evaluator INFO: Inference done 381/6110. Dataloading: 0.0015 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:30 +[03/31 21:53:43] detectron2.evaluation.evaluator INFO: Inference done 455/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:25 +[03/31 21:53:48] detectron2.evaluation.evaluator INFO: Inference done 529/6110. Dataloading: 0.0015 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:20 +[03/31 21:53:53] detectron2.evaluation.evaluator INFO: Inference done 603/6110. Dataloading: 0.0015 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:06:15 +[03/31 21:53:58] detectron2.evaluation.evaluator INFO: Inference done 677/6110. Dataloading: 0.0015 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:09 +[03/31 21:54:03] detectron2.evaluation.evaluator INFO: Inference done 751/6110. Dataloading: 0.0015 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:05 +[03/31 21:54:08] detectron2.evaluation.evaluator INFO: Inference done 825/6110. Dataloading: 0.0015 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:05:59 +[03/31 21:54:13] detectron2.evaluation.evaluator INFO: Inference done 898/6110. Dataloading: 0.0015 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:05:55 +[03/31 21:54:18] detectron2.evaluation.evaluator INFO: Inference done 972/6110. Dataloading: 0.0015 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0682 s/iter. ETA=0:05:50 +[03/31 21:54:23] detectron2.evaluation.evaluator INFO: Inference done 1044/6110. Dataloading: 0.0015 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:45 +[03/31 21:54:28] detectron2.evaluation.evaluator INFO: Inference done 1117/6110. Dataloading: 0.0015 s/iter. Inference: 0.0640 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:41 +[03/31 21:54:33] detectron2.evaluation.evaluator INFO: Inference done 1191/6110. Dataloading: 0.0015 s/iter. Inference: 0.0640 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:36 +[03/31 21:54:38] detectron2.evaluation.evaluator INFO: Inference done 1265/6110. Dataloading: 0.0015 s/iter. Inference: 0.0640 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:30 +[03/31 21:54:43] detectron2.evaluation.evaluator INFO: Inference done 1339/6110. Dataloading: 0.0015 s/iter. Inference: 0.0640 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:25 +[03/31 21:54:48] detectron2.evaluation.evaluator INFO: Inference done 1413/6110. Dataloading: 0.0015 s/iter. Inference: 0.0640 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:20 +[03/31 21:54:53] detectron2.evaluation.evaluator INFO: Inference done 1487/6110. Dataloading: 0.0015 s/iter. Inference: 0.0640 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:15 +[03/31 21:54:58] detectron2.evaluation.evaluator INFO: Inference done 1561/6110. Dataloading: 0.0015 s/iter. Inference: 0.0640 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:10 +[03/31 21:55:04] detectron2.evaluation.evaluator INFO: Inference done 1635/6110. Dataloading: 0.0015 s/iter. Inference: 0.0640 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:05 +[03/31 21:55:09] detectron2.evaluation.evaluator INFO: Inference done 1708/6110. Dataloading: 0.0015 s/iter. Inference: 0.0640 s/iter. Eval: 0.0028 s/iter. Total: 0.0683 s/iter. ETA=0:05:00 +[03/31 21:55:14] detectron2.evaluation.evaluator INFO: Inference done 1781/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0028 s/iter. Total: 0.0684 s/iter. ETA=0:04:56 +[03/31 21:55:19] detectron2.evaluation.evaluator INFO: Inference done 1855/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0028 s/iter. Total: 0.0684 s/iter. ETA=0:04:50 +[03/31 21:55:24] detectron2.evaluation.evaluator INFO: Inference done 1928/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0028 s/iter. Total: 0.0684 s/iter. ETA=0:04:45 +[03/31 21:55:29] detectron2.evaluation.evaluator INFO: Inference done 2002/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0684 s/iter. ETA=0:04:40 +[03/31 21:55:34] detectron2.evaluation.evaluator INFO: Inference done 2075/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0684 s/iter. ETA=0:04:35 +[03/31 21:55:39] detectron2.evaluation.evaluator INFO: Inference done 2149/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0684 s/iter. ETA=0:04:30 +[03/31 21:55:44] detectron2.evaluation.evaluator INFO: Inference done 2223/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0684 s/iter. ETA=0:04:25 +[03/31 21:55:49] detectron2.evaluation.evaluator INFO: Inference done 2296/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0684 s/iter. ETA=0:04:20 +[03/31 21:55:54] detectron2.evaluation.evaluator INFO: Inference done 2370/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0684 s/iter. ETA=0:04:15 +[03/31 21:55:59] detectron2.evaluation.evaluator INFO: Inference done 2444/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0684 s/iter. ETA=0:04:10 +[03/31 21:56:04] detectron2.evaluation.evaluator INFO: Inference done 2518/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:04:05 +[03/31 21:56:09] detectron2.evaluation.evaluator INFO: Inference done 2592/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:04:00 +[03/31 21:56:14] detectron2.evaluation.evaluator INFO: Inference done 2666/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:55 +[03/31 21:56:19] detectron2.evaluation.evaluator INFO: Inference done 2740/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:50 +[03/31 21:56:24] detectron2.evaluation.evaluator INFO: Inference done 2814/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:45 +[03/31 21:56:29] detectron2.evaluation.evaluator INFO: Inference done 2888/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:40 +[03/31 21:56:34] detectron2.evaluation.evaluator INFO: Inference done 2962/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:35 +[03/31 21:56:39] detectron2.evaluation.evaluator INFO: Inference done 3035/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:30 +[03/31 21:56:44] detectron2.evaluation.evaluator INFO: Inference done 3109/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:25 +[03/31 21:56:49] detectron2.evaluation.evaluator INFO: Inference done 3182/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:20 +[03/31 21:56:54] detectron2.evaluation.evaluator INFO: Inference done 3256/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:15 +[03/31 21:56:59] detectron2.evaluation.evaluator INFO: Inference done 3330/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:09 +[03/31 21:57:04] detectron2.evaluation.evaluator INFO: Inference done 3403/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:05 +[03/31 21:57:09] detectron2.evaluation.evaluator INFO: Inference done 3477/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0684 s/iter. ETA=0:02:59 +[03/31 21:57:14] detectron2.evaluation.evaluator INFO: Inference done 3551/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:54 +[03/31 21:57:19] detectron2.evaluation.evaluator INFO: Inference done 3625/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:49 +[03/31 21:57:25] detectron2.evaluation.evaluator INFO: Inference done 3699/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:44 +[03/31 21:57:30] detectron2.evaluation.evaluator INFO: Inference done 3773/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:39 +[03/31 21:57:35] detectron2.evaluation.evaluator INFO: Inference done 3847/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:34 +[03/31 21:57:40] detectron2.evaluation.evaluator INFO: Inference done 3921/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:29 +[03/31 21:57:45] detectron2.evaluation.evaluator INFO: Inference done 3994/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:24 +[03/31 21:57:50] detectron2.evaluation.evaluator INFO: Inference done 4068/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:19 +[03/31 21:57:55] detectron2.evaluation.evaluator INFO: Inference done 4142/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:14 +[03/31 21:58:00] detectron2.evaluation.evaluator INFO: Inference done 4216/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:09 +[03/31 21:58:05] detectron2.evaluation.evaluator INFO: Inference done 4290/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:04 +[03/31 21:58:10] detectron2.evaluation.evaluator INFO: Inference done 4364/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:59 +[03/31 21:58:15] detectron2.evaluation.evaluator INFO: Inference done 4438/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:54 +[03/31 21:58:20] detectron2.evaluation.evaluator INFO: Inference done 4511/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:49 +[03/31 21:58:25] detectron2.evaluation.evaluator INFO: Inference done 4584/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:44 +[03/31 21:58:30] detectron2.evaluation.evaluator INFO: Inference done 4658/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:39 +[03/31 21:58:35] detectron2.evaluation.evaluator INFO: Inference done 4732/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:34 +[03/31 21:58:40] detectron2.evaluation.evaluator INFO: Inference done 4806/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:29 +[03/31 21:58:45] detectron2.evaluation.evaluator INFO: Inference done 4880/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:23 +[03/31 21:58:50] detectron2.evaluation.evaluator INFO: Inference done 4954/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:18 +[03/31 21:58:55] detectron2.evaluation.evaluator INFO: Inference done 5028/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:13 +[03/31 21:59:00] detectron2.evaluation.evaluator INFO: Inference done 5102/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:08 +[03/31 21:59:05] detectron2.evaluation.evaluator INFO: Inference done 5176/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:03 +[03/31 21:59:10] detectron2.evaluation.evaluator INFO: Inference done 5249/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:58 +[03/31 21:59:15] detectron2.evaluation.evaluator INFO: Inference done 5323/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:53 +[03/31 21:59:20] detectron2.evaluation.evaluator INFO: Inference done 5397/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:48 +[03/31 21:59:25] detectron2.evaluation.evaluator INFO: Inference done 5471/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:43 +[03/31 21:59:30] detectron2.evaluation.evaluator INFO: Inference done 5545/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:00:38 +[03/31 21:59:35] detectron2.evaluation.evaluator INFO: Inference done 5619/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:00:33 +[03/31 21:59:40] detectron2.evaluation.evaluator INFO: Inference done 5693/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:00:28 +[03/31 21:59:46] detectron2.evaluation.evaluator INFO: Inference done 5767/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:00:23 +[03/31 21:59:51] detectron2.evaluation.evaluator INFO: Inference done 5841/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:00:18 +[03/31 21:59:56] detectron2.evaluation.evaluator INFO: Inference done 5915/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:00:13 +[03/31 22:00:01] detectron2.evaluation.evaluator INFO: Inference done 5989/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:00:08 +[03/31 22:00:06] detectron2.evaluation.evaluator INFO: Inference done 6063/6110. Dataloading: 0.0015 s/iter. Inference: 0.0641 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:00:03 +[03/31 22:00:10] detectron2.evaluation.evaluator INFO: Total inference time: 0:06:57.790617 (0.068434 s / iter per device, on 4 devices) +[03/31 22:00:10] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:06:31 (0.064108 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-iSAID/log.txt.rank2 b/OVRSIS/eval-iSAID/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..b7ada81151da9ac2a8544d5291b0e137c0f6df77 --- /dev/null +++ b/OVRSIS/eval-iSAID/log.txt.rank2 @@ -0,0 +1,646 @@ +[03/31 21:52:42] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 21:52:44] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 21:52:44] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-iSAID', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/iSAID.json', 'DATASETS.TEST', '("iSAID_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 21:52:44] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 21:52:44] detectron2.utils.env INFO: Using a generated random seed 48807448 +[03/31 21:52:47] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 21:52:47] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 21:52:47] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 21:52:48] detectron2.data.datasets.coco INFO: Loaded 24439 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/iSAID/imgs +[03/31 21:52:48] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 21:52:48] detectron2.data.common INFO: Serializing the dataset using: +[03/31 21:52:48] detectron2.data.common INFO: Serializing 24439 elements to byte tensors and concatenating them all ... +[03/31 21:52:48] detectron2.data.common INFO: Serialized dataset takes 6.50 MiB +[03/31 21:52:49] detectron2.data.datasets.coco INFO: Loaded 24439 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/iSAID/imgs +[03/31 21:52:49] detectron2.evaluation.evaluator INFO: Start inference on 6110 batches +[03/31 21:53:13] detectron2.evaluation.evaluator INFO: Inference done 11/6110. Dataloading: 0.0011 s/iter. Inference: 0.0636 s/iter. Eval: 0.0031 s/iter. Total: 0.0678 s/iter. ETA=0:06:53 +[03/31 21:53:18] detectron2.evaluation.evaluator INFO: Inference done 85/6110. Dataloading: 0.0013 s/iter. Inference: 0.0640 s/iter. Eval: 0.0026 s/iter. Total: 0.0679 s/iter. ETA=0:06:49 +[03/31 21:53:23] detectron2.evaluation.evaluator INFO: Inference done 159/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0026 s/iter. Total: 0.0679 s/iter. ETA=0:06:44 +[03/31 21:53:28] detectron2.evaluation.evaluator INFO: Inference done 233/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0026 s/iter. Total: 0.0680 s/iter. ETA=0:06:39 +[03/31 21:53:33] detectron2.evaluation.evaluator INFO: Inference done 307/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0027 s/iter. Total: 0.0681 s/iter. ETA=0:06:34 +[03/31 21:53:38] detectron2.evaluation.evaluator INFO: Inference done 381/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:30 +[03/31 21:53:43] detectron2.evaluation.evaluator INFO: Inference done 455/6110. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:24 +[03/31 21:53:48] detectron2.evaluation.evaluator INFO: Inference done 529/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:19 +[03/31 21:53:53] detectron2.evaluation.evaluator INFO: Inference done 603/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0028 s/iter. Total: 0.0681 s/iter. ETA=0:06:14 +[03/31 21:53:58] detectron2.evaluation.evaluator INFO: Inference done 677/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0027 s/iter. Total: 0.0681 s/iter. ETA=0:06:09 +[03/31 21:54:03] detectron2.evaluation.evaluator INFO: Inference done 751/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0027 s/iter. Total: 0.0681 s/iter. ETA=0:06:04 +[03/31 21:54:08] detectron2.evaluation.evaluator INFO: Inference done 825/6110. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0027 s/iter. Total: 0.0681 s/iter. ETA=0:05:59 +[03/31 21:54:13] detectron2.evaluation.evaluator INFO: Inference done 898/6110. Dataloading: 0.0014 s/iter. Inference: 0.0640 s/iter. Eval: 0.0026 s/iter. Total: 0.0681 s/iter. ETA=0:05:54 +[03/31 21:54:18] detectron2.evaluation.evaluator INFO: Inference done 971/6110. Dataloading: 0.0014 s/iter. Inference: 0.0640 s/iter. Eval: 0.0027 s/iter. Total: 0.0681 s/iter. ETA=0:05:50 +[03/31 21:54:23] detectron2.evaluation.evaluator INFO: Inference done 1043/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0682 s/iter. ETA=0:05:45 +[03/31 21:54:28] detectron2.evaluation.evaluator INFO: Inference done 1116/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:05:41 +[03/31 21:54:33] detectron2.evaluation.evaluator INFO: Inference done 1190/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:05:35 +[03/31 21:54:38] detectron2.evaluation.evaluator INFO: Inference done 1264/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:05:30 +[03/31 21:54:43] detectron2.evaluation.evaluator INFO: Inference done 1338/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:05:25 +[03/31 21:54:48] detectron2.evaluation.evaluator INFO: Inference done 1412/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:05:20 +[03/31 21:54:53] detectron2.evaluation.evaluator INFO: Inference done 1485/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:05:15 +[03/31 21:54:58] detectron2.evaluation.evaluator INFO: Inference done 1559/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:05:10 +[03/31 21:55:03] detectron2.evaluation.evaluator INFO: Inference done 1632/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:05:05 +[03/31 21:55:08] detectron2.evaluation.evaluator INFO: Inference done 1705/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:05:00 +[03/31 21:55:13] detectron2.evaluation.evaluator INFO: Inference done 1778/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0684 s/iter. ETA=0:04:56 +[03/31 21:55:18] detectron2.evaluation.evaluator INFO: Inference done 1852/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0684 s/iter. ETA=0:04:51 +[03/31 21:55:23] detectron2.evaluation.evaluator INFO: Inference done 1925/6110. Dataloading: 0.0014 s/iter. Inference: 0.0643 s/iter. Eval: 0.0026 s/iter. Total: 0.0684 s/iter. ETA=0:04:46 +[03/31 21:55:28] detectron2.evaluation.evaluator INFO: Inference done 1999/6110. Dataloading: 0.0014 s/iter. Inference: 0.0643 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:04:40 +[03/31 21:55:33] detectron2.evaluation.evaluator INFO: Inference done 2072/6110. Dataloading: 0.0014 s/iter. Inference: 0.0643 s/iter. Eval: 0.0026 s/iter. Total: 0.0684 s/iter. ETA=0:04:36 +[03/31 21:55:38] detectron2.evaluation.evaluator INFO: Inference done 2146/6110. Dataloading: 0.0014 s/iter. Inference: 0.0643 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:04:30 +[03/31 21:55:43] detectron2.evaluation.evaluator INFO: Inference done 2220/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:04:25 +[03/31 21:55:48] detectron2.evaluation.evaluator INFO: Inference done 2293/6110. Dataloading: 0.0014 s/iter. Inference: 0.0643 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:04:20 +[03/31 21:55:54] detectron2.evaluation.evaluator INFO: Inference done 2367/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:04:15 +[03/31 21:55:59] detectron2.evaluation.evaluator INFO: Inference done 2440/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0684 s/iter. ETA=0:04:10 +[03/31 21:56:04] detectron2.evaluation.evaluator INFO: Inference done 2514/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:04:05 +[03/31 21:56:09] detectron2.evaluation.evaluator INFO: Inference done 2588/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0026 s/iter. Total: 0.0683 s/iter. ETA=0:04:00 +[03/31 21:56:14] detectron2.evaluation.evaluator INFO: Inference done 2662/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:55 +[03/31 21:56:19] detectron2.evaluation.evaluator INFO: Inference done 2736/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:50 +[03/31 21:56:24] detectron2.evaluation.evaluator INFO: Inference done 2809/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:45 +[03/31 21:56:29] detectron2.evaluation.evaluator INFO: Inference done 2883/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:40 +[03/31 21:56:34] detectron2.evaluation.evaluator INFO: Inference done 2957/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:35 +[03/31 21:56:39] detectron2.evaluation.evaluator INFO: Inference done 3030/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:30 +[03/31 21:56:44] detectron2.evaluation.evaluator INFO: Inference done 3104/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:25 +[03/31 21:56:49] detectron2.evaluation.evaluator INFO: Inference done 3177/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:20 +[03/31 21:56:54] detectron2.evaluation.evaluator INFO: Inference done 3251/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:15 +[03/31 21:56:59] detectron2.evaluation.evaluator INFO: Inference done 3325/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:10 +[03/31 21:57:04] detectron2.evaluation.evaluator INFO: Inference done 3398/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:05 +[03/31 21:57:09] detectron2.evaluation.evaluator INFO: Inference done 3471/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:03:00 +[03/31 21:57:14] detectron2.evaluation.evaluator INFO: Inference done 3545/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:55 +[03/31 21:57:19] detectron2.evaluation.evaluator INFO: Inference done 3619/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:50 +[03/31 21:57:24] detectron2.evaluation.evaluator INFO: Inference done 3693/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:45 +[03/31 21:57:29] detectron2.evaluation.evaluator INFO: Inference done 3767/6110. Dataloading: 0.0014 s/iter. Inference: 0.0642 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:40 +[03/31 21:57:34] detectron2.evaluation.evaluator INFO: Inference done 3842/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:34 +[03/31 21:57:39] detectron2.evaluation.evaluator INFO: Inference done 3916/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:29 +[03/31 21:57:44] detectron2.evaluation.evaluator INFO: Inference done 3989/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:24 +[03/31 21:57:49] detectron2.evaluation.evaluator INFO: Inference done 4063/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:19 +[03/31 21:57:54] detectron2.evaluation.evaluator INFO: Inference done 4137/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:14 +[03/31 21:57:59] detectron2.evaluation.evaluator INFO: Inference done 4211/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:09 +[03/31 21:58:04] detectron2.evaluation.evaluator INFO: Inference done 4285/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:02:04 +[03/31 21:58:09] detectron2.evaluation.evaluator INFO: Inference done 4359/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:59 +[03/31 21:58:14] detectron2.evaluation.evaluator INFO: Inference done 4433/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:54 +[03/31 21:58:19] detectron2.evaluation.evaluator INFO: Inference done 4507/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:49 +[03/31 21:58:25] detectron2.evaluation.evaluator INFO: Inference done 4580/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:44 +[03/31 21:58:30] detectron2.evaluation.evaluator INFO: Inference done 4654/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:39 +[03/31 21:58:35] detectron2.evaluation.evaluator INFO: Inference done 4728/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:34 +[03/31 21:58:40] detectron2.evaluation.evaluator INFO: Inference done 4802/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:29 +[03/31 21:58:45] detectron2.evaluation.evaluator INFO: Inference done 4875/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:24 +[03/31 21:58:50] detectron2.evaluation.evaluator INFO: Inference done 4949/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:19 +[03/31 21:58:55] detectron2.evaluation.evaluator INFO: Inference done 5023/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:14 +[03/31 21:59:00] detectron2.evaluation.evaluator INFO: Inference done 5096/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:09 +[03/31 21:59:05] detectron2.evaluation.evaluator INFO: Inference done 5170/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:01:04 +[03/31 21:59:10] detectron2.evaluation.evaluator INFO: Inference done 5244/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:59 +[03/31 21:59:15] detectron2.evaluation.evaluator INFO: Inference done 5318/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:54 +[03/31 21:59:20] detectron2.evaluation.evaluator INFO: Inference done 5392/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:49 +[03/31 21:59:25] detectron2.evaluation.evaluator INFO: Inference done 5465/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:44 +[03/31 21:59:30] detectron2.evaluation.evaluator INFO: Inference done 5539/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:38 +[03/31 21:59:35] detectron2.evaluation.evaluator INFO: Inference done 5613/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:33 +[03/31 21:59:40] detectron2.evaluation.evaluator INFO: Inference done 5687/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:28 +[03/31 21:59:45] detectron2.evaluation.evaluator INFO: Inference done 5761/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:23 +[03/31 21:59:50] detectron2.evaluation.evaluator INFO: Inference done 5835/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:18 +[03/31 21:59:55] detectron2.evaluation.evaluator INFO: Inference done 5910/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:13 +[03/31 22:00:00] detectron2.evaluation.evaluator INFO: Inference done 5984/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0683 s/iter. ETA=0:00:08 +[03/31 22:00:05] detectron2.evaluation.evaluator INFO: Inference done 6058/6110. Dataloading: 0.0014 s/iter. Inference: 0.0641 s/iter. Eval: 0.0027 s/iter. Total: 0.0682 s/iter. ETA=0:00:03 +[03/31 22:00:10] detectron2.evaluation.evaluator INFO: Total inference time: 0:06:57.802877 (0.068436 s / iter per device, on 4 devices) +[03/31 22:00:10] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:06:31 (0.064066 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-iSAID/log.txt.rank3 b/OVRSIS/eval-iSAID/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..0f64d8e032091ae149544a04cfa02030ccaf8e05 --- /dev/null +++ b/OVRSIS/eval-iSAID/log.txt.rank3 @@ -0,0 +1,646 @@ +[03/31 21:52:42] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 21:52:44] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 21:52:44] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-iSAID', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/iSAID.json', 'DATASETS.TEST', '("iSAID_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 21:52:44] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 21:52:44] detectron2.utils.env INFO: Using a generated random seed 48405557 +[03/31 21:52:47] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 21:52:47] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 21:52:47] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 21:52:48] detectron2.data.datasets.coco INFO: Loaded 24439 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/iSAID/imgs +[03/31 21:52:48] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 21:52:48] detectron2.data.common INFO: Serializing the dataset using: +[03/31 21:52:48] detectron2.data.common INFO: Serializing 24439 elements to byte tensors and concatenating them all ... +[03/31 21:52:48] detectron2.data.common INFO: Serialized dataset takes 6.50 MiB +[03/31 21:52:49] detectron2.data.datasets.coco INFO: Loaded 24439 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/iSAID/imgs +[03/31 21:52:49] detectron2.evaluation.evaluator INFO: Start inference on 6109 batches +[03/31 21:53:13] detectron2.evaluation.evaluator INFO: Inference done 11/6109. Dataloading: 0.0009 s/iter. Inference: 0.0637 s/iter. Eval: 0.0026 s/iter. Total: 0.0671 s/iter. ETA=0:06:49 +[03/31 21:53:18] detectron2.evaluation.evaluator INFO: Inference done 85/6109. Dataloading: 0.0013 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0679 s/iter. ETA=0:06:49 +[03/31 21:53:23] detectron2.evaluation.evaluator INFO: Inference done 159/6109. Dataloading: 0.0013 s/iter. Inference: 0.0638 s/iter. Eval: 0.0029 s/iter. Total: 0.0680 s/iter. ETA=0:06:44 +[03/31 21:53:28] detectron2.evaluation.evaluator INFO: Inference done 233/6109. Dataloading: 0.0013 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0680 s/iter. ETA=0:06:39 +[03/31 21:53:33] detectron2.evaluation.evaluator INFO: Inference done 307/6109. Dataloading: 0.0013 s/iter. Inference: 0.0638 s/iter. Eval: 0.0028 s/iter. Total: 0.0680 s/iter. ETA=0:06:34 +[03/31 21:53:38] detectron2.evaluation.evaluator INFO: Inference done 381/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0681 s/iter. ETA=0:06:30 +[03/31 21:53:43] detectron2.evaluation.evaluator INFO: Inference done 455/6109. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0029 s/iter. Total: 0.0681 s/iter. ETA=0:06:25 +[03/31 21:53:48] detectron2.evaluation.evaluator INFO: Inference done 529/6109. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0029 s/iter. Total: 0.0681 s/iter. ETA=0:06:20 +[03/31 21:53:53] detectron2.evaluation.evaluator INFO: Inference done 603/6109. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0029 s/iter. Total: 0.0681 s/iter. ETA=0:06:15 +[03/31 21:53:58] detectron2.evaluation.evaluator INFO: Inference done 677/6109. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0029 s/iter. Total: 0.0681 s/iter. ETA=0:06:09 +[03/31 21:54:03] detectron2.evaluation.evaluator INFO: Inference done 751/6109. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0029 s/iter. Total: 0.0681 s/iter. ETA=0:06:04 +[03/31 21:54:08] detectron2.evaluation.evaluator INFO: Inference done 825/6109. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0029 s/iter. Total: 0.0681 s/iter. ETA=0:05:59 +[03/31 21:54:13] detectron2.evaluation.evaluator INFO: Inference done 899/6109. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0029 s/iter. Total: 0.0681 s/iter. ETA=0:05:54 +[03/31 21:54:18] detectron2.evaluation.evaluator INFO: Inference done 972/6109. Dataloading: 0.0014 s/iter. Inference: 0.0638 s/iter. Eval: 0.0029 s/iter. Total: 0.0681 s/iter. ETA=0:05:49 +[03/31 21:54:23] detectron2.evaluation.evaluator INFO: Inference done 1045/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0682 s/iter. ETA=0:05:45 +[03/31 21:54:28] detectron2.evaluation.evaluator INFO: Inference done 1118/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:40 +[03/31 21:54:33] detectron2.evaluation.evaluator INFO: Inference done 1192/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:35 +[03/31 21:54:38] detectron2.evaluation.evaluator INFO: Inference done 1266/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:30 +[03/31 21:54:43] detectron2.evaluation.evaluator INFO: Inference done 1340/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:25 +[03/31 21:54:48] detectron2.evaluation.evaluator INFO: Inference done 1414/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:20 +[03/31 21:54:53] detectron2.evaluation.evaluator INFO: Inference done 1488/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0029 s/iter. Total: 0.0683 s/iter. ETA=0:05:15 +[03/31 21:54:58] detectron2.evaluation.evaluator INFO: Inference done 1562/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. ETA=0:05:10 +[03/31 21:55:03] detectron2.evaluation.evaluator INFO: Inference done 1635/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. ETA=0:05:05 +[03/31 21:55:08] detectron2.evaluation.evaluator INFO: Inference done 1708/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. ETA=0:05:00 +[03/31 21:55:13] detectron2.evaluation.evaluator INFO: Inference done 1781/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. ETA=0:04:55 +[03/31 21:55:19] detectron2.evaluation.evaluator INFO: Inference done 1855/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. ETA=0:04:50 +[03/31 21:55:24] detectron2.evaluation.evaluator INFO: Inference done 1929/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. ETA=0:04:45 +[03/31 21:55:29] detectron2.evaluation.evaluator INFO: Inference done 2003/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. ETA=0:04:40 +[03/31 21:55:34] detectron2.evaluation.evaluator INFO: Inference done 2076/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. ETA=0:04:35 +[03/31 21:55:39] detectron2.evaluation.evaluator INFO: Inference done 2150/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. ETA=0:04:30 +[03/31 21:55:44] detectron2.evaluation.evaluator INFO: Inference done 2224/6109. Dataloading: 0.0014 s/iter. Inference: 0.0639 s/iter. Eval: 0.0030 s/iter. Total: 0.0683 s/iter. 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ETA=0:00:03 +[03/31 22:00:10] detectron2.evaluation.evaluator INFO: Total inference time: 0:06:57.721165 (0.068434 s / iter per device, on 4 devices) +[03/31 22:00:10] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:06:30 (0.063919 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-openearthmap/config.yaml b/OVRSIS/eval-openearthmap/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9cb70b88d1af58d81689ac6d233fd669ea3b5a32 --- /dev/null +++ b/OVRSIS/eval-openearthmap/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - OpenEarthMap_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/openearthmap.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-openearthmap +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-openearthmap/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-openearthmap/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-openearthmap/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-openearthmap/inference/sem_seg_evaluation.pth b/OVRSIS/eval-openearthmap/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..ac4d1cc2837f8e32d4670a32404ada0ba832d316 --- /dev/null +++ b/OVRSIS/eval-openearthmap/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5a738f819fbd9f231c0706c6ba3ddbb6ee35273d056c4a6072d24cbeefcd675 +size 3680 diff --git a/OVRSIS/eval-openearthmap/inference/sem_seg_predictions.json b/OVRSIS/eval-openearthmap/inference/sem_seg_predictions.json new file mode 100644 index 0000000000000000000000000000000000000000..c5afcbdca1318fb2c9727d4bf9b7b16e525f9728 --- /dev/null +++ b/OVRSIS/eval-openearthmap/inference/sem_seg_predictions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:411c78b18c7027cb6d4dc7217b4e54957ee68744c9bc66bbead92b49c55f113b +size 138116874 diff --git a/OVRSIS/eval-openearthmap/log.txt b/OVRSIS/eval-openearthmap/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..24db9a748a371db2d83e4509d317027fbe2c97a2 --- /dev/null +++ b/OVRSIS/eval-openearthmap/log.txt @@ -0,0 +1,1009 @@ +[03/31 22:20:50] detectron2 INFO: Rank of current process: 0. World size: 4 +[03/31 22:20:51] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:20:51] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-openearthmap', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/openearthmap.json', 'DATASETS.TEST', '("OpenEarthMap_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:20:51] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:20:51] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - OpenEarthMap_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/openearthmap.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-openearthmap +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 22:20:51] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-openearthmap/config.yaml +[03/31 22:20:51] d2.utils.env INFO: Using a generated random seed 55314100 +[03/31 22:20:54] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:20:54] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:20:54] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:20:55] d2.data.datasets.coco INFO: Loaded 2687 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/OpenEarthMap/images +[03/31 22:20:55] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:20:55] d2.data.common INFO: Serializing the dataset using: +[03/31 22:20:55] d2.data.common INFO: Serializing 2687 elements to byte tensors and concatenating them all ... +[03/31 22:20:55] d2.data.common INFO: Serialized dataset takes 0.69 MiB +[03/31 22:20:55] d2.data.datasets.coco INFO: Loaded 2687 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/OpenEarthMap/images +[03/31 22:20:55] d2.evaluation.evaluator INFO: Start inference on 672 batches +[03/31 22:21:02] d2.evaluation.evaluator INFO: Inference done 11/672. Dataloading: 0.0012 s/iter. Inference: 0.0625 s/iter. Eval: 0.0498 s/iter. Total: 0.1135 s/iter. ETA=0:01:15 +[03/31 22:21:07] d2.evaluation.evaluator INFO: Inference done 57/672. Dataloading: 0.0015 s/iter. Inference: 0.0615 s/iter. Eval: 0.0469 s/iter. Total: 0.1099 s/iter. ETA=0:01:07 +[03/31 22:21:12] d2.evaluation.evaluator INFO: Inference done 102/672. Dataloading: 0.0015 s/iter. Inference: 0.0615 s/iter. Eval: 0.0477 s/iter. Total: 0.1108 s/iter. ETA=0:01:03 +[03/31 22:21:17] d2.evaluation.evaluator INFO: Inference done 148/672. Dataloading: 0.0015 s/iter. Inference: 0.0614 s/iter. Eval: 0.0475 s/iter. Total: 0.1105 s/iter. ETA=0:00:57 +[03/31 22:21:22] d2.evaluation.evaluator INFO: Inference done 194/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0471 s/iter. Total: 0.1102 s/iter. ETA=0:00:52 +[03/31 22:21:27] d2.evaluation.evaluator INFO: Inference done 241/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0466 s/iter. Total: 0.1097 s/iter. ETA=0:00:47 +[03/31 22:21:32] d2.evaluation.evaluator INFO: Inference done 287/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0465 s/iter. Total: 0.1096 s/iter. ETA=0:00:42 +[03/31 22:21:37] d2.evaluation.evaluator INFO: Inference done 334/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0461 s/iter. Total: 0.1092 s/iter. ETA=0:00:36 +[03/31 22:21:43] d2.evaluation.evaluator INFO: Inference done 382/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0457 s/iter. Total: 0.1088 s/iter. ETA=0:00:31 +[03/31 22:21:48] d2.evaluation.evaluator INFO: Inference done 431/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0450 s/iter. Total: 0.1082 s/iter. ETA=0:00:26 +[03/31 22:21:53] d2.evaluation.evaluator INFO: Inference done 475/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0455 s/iter. Total: 0.1087 s/iter. ETA=0:00:21 +[03/31 22:21:58] d2.evaluation.evaluator INFO: Inference done 522/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0454 s/iter. Total: 0.1086 s/iter. ETA=0:00:16 +[03/31 22:22:03] d2.evaluation.evaluator INFO: Inference done 570/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0452 s/iter. Total: 0.1084 s/iter. ETA=0:00:11 +[03/31 22:22:08] d2.evaluation.evaluator INFO: Inference done 616/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0454 s/iter. Total: 0.1086 s/iter. ETA=0:00:06 +[03/31 22:22:13] d2.evaluation.evaluator INFO: Inference done 662/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0455 s/iter. Total: 0.1087 s/iter. ETA=0:00:01 +[03/31 22:22:15] d2.evaluation.evaluator INFO: Total inference time: 0:01:13.257162 (0.109831 s / iter per device, on 4 devices) +[03/31 22:22:15] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:41 (0.061501 s / iter per device, on 4 devices) +[03/31 22:22:16] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 32.36911339981565, 'fwIoU': 33.0773928651039, 'IoU-Background': 1.6846310924586507, 'BoundaryIoU-Background': 45.54809610330852, 'min(IoU, B-Iou)-Background': 1.6846310924586507, 'IoU-Bareland, Barren, Dirt': 5.074382161817861, 'BoundaryIoU-Bareland, Barren, Dirt': 0.8054401582132871, 'min(IoU, B-Iou)-Bareland, Barren, Dirt': 0.8054401582132871, 'IoU-Pasture, Grass, Lawn': 15.578570523632756, 'BoundaryIoU-Pasture, Grass, Lawn': 4.976566140646193, 'min(IoU, B-Iou)-Pasture, Grass, Lawn': 4.976566140646193, 'IoU-Developed Space': 4.654517158247294, 'BoundaryIoU-Developed Space': 2.217121231979361, 'min(IoU, B-Iou)-Developed Space': 2.217121231979361, 'IoU-Roads': 42.618658948547065, 'BoundaryIoU-Roads': 5.213032008234624, 'min(IoU, B-Iou)-Roads': 5.213032008234624, 'IoU-Tree, Forest': 40.429123011928056, 'BoundaryIoU-Tree, Forest': 6.118493691344929, 'min(IoU, B-Iou)-Tree, Forest': 6.118493691344929, 'IoU-Water, River': 73.04737643809726, 'BoundaryIoU-Water, River': 3.685339086650416, 'min(IoU, B-Iou)-Water, River': 3.685339086650416, 'IoU-Agricultural Land, Cropland': 49.27267443677814, 'BoundaryIoU-Agricultural Land, Cropland': 20.796809779557965, 'min(IoU, B-Iou)-Agricultural Land, Cropland': 20.796809779557965, 'IoU-Buildings, Roof, House': 58.96208682683378, 'BoundaryIoU-Buildings, Roof, House': 8.917396429983073, 'min(IoU, B-Iou)-Buildings, Roof, House': 8.917396429983073, 'mACC': 51.931120595498925, 'pACC': 43.18123052339099, 'ACC-Background': 86.45573274311552, 'ACC-Bareland, Barren, Dirt': 5.148855962666731, 'ACC-Pasture, Grass, Lawn': 16.28917176884303, 'ACC-Developed Space': 4.741947019778587, 'ACC-Roads': 73.24165598898486, 'ACC-Tree, Forest': 44.07343474596575, 'ACC-Water, River': 82.58468466261023, 'ACC-Agricultural Land, Cropland': 85.92598333177601, 'ACC-Buildings, Roof, House': 68.91861913574962})]) +[03/31 22:22:16] d2.engine.defaults INFO: Evaluation results for OpenEarthMap_all_sem_seg in csv format: +[03/31 22:22:16] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:22:16] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:22:16] d2.evaluation.testing INFO: copypaste: 32.3691,33.0774,51.9311,43.1812 diff --git a/OVRSIS/eval-openearthmap/log.txt.rank1 b/OVRSIS/eval-openearthmap/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..22029692ad48c03fde2271e61800f46c8c0f7d87 --- /dev/null +++ b/OVRSIS/eval-openearthmap/log.txt.rank1 @@ -0,0 +1,578 @@ +[03/31 22:20:50] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 22:20:51] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:20:51] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-openearthmap', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/openearthmap.json', 'DATASETS.TEST', '("OpenEarthMap_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:20:51] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:20:51] detectron2.utils.env INFO: Using a generated random seed 55613351 +[03/31 22:20:54] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:20:54] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:20:54] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:20:55] detectron2.data.datasets.coco INFO: Loaded 2687 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/OpenEarthMap/images +[03/31 22:20:55] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:20:55] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:20:55] detectron2.data.common INFO: Serializing 2687 elements to byte tensors and concatenating them all ... +[03/31 22:20:55] detectron2.data.common INFO: Serialized dataset takes 0.69 MiB +[03/31 22:20:55] detectron2.data.datasets.coco INFO: Loaded 2687 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/OpenEarthMap/images +[03/31 22:20:55] detectron2.evaluation.evaluator INFO: Start inference on 672 batches +[03/31 22:21:02] detectron2.evaluation.evaluator INFO: Inference done 11/672. Dataloading: 0.0010 s/iter. Inference: 0.0625 s/iter. Eval: 0.0526 s/iter. Total: 0.1161 s/iter. ETA=0:01:16 +[03/31 22:21:07] detectron2.evaluation.evaluator INFO: Inference done 56/672. Dataloading: 0.0015 s/iter. Inference: 0.0615 s/iter. Eval: 0.0492 s/iter. Total: 0.1123 s/iter. ETA=0:01:09 +[03/31 22:21:12] detectron2.evaluation.evaluator INFO: Inference done 101/672. Dataloading: 0.0015 s/iter. Inference: 0.0614 s/iter. Eval: 0.0493 s/iter. Total: 0.1123 s/iter. ETA=0:01:04 +[03/31 22:21:17] detectron2.evaluation.evaluator INFO: Inference done 147/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0485 s/iter. Total: 0.1117 s/iter. ETA=0:00:58 +[03/31 22:21:22] detectron2.evaluation.evaluator INFO: Inference done 194/672. Dataloading: 0.0016 s/iter. Inference: 0.0616 s/iter. Eval: 0.0472 s/iter. Total: 0.1104 s/iter. ETA=0:00:52 +[03/31 22:21:27] detectron2.evaluation.evaluator INFO: Inference done 241/672. Dataloading: 0.0016 s/iter. Inference: 0.0616 s/iter. Eval: 0.0467 s/iter. Total: 0.1099 s/iter. ETA=0:00:47 +[03/31 22:21:32] detectron2.evaluation.evaluator INFO: Inference done 287/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0467 s/iter. Total: 0.1099 s/iter. ETA=0:00:42 +[03/31 22:21:37] detectron2.evaluation.evaluator INFO: Inference done 332/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0471 s/iter. Total: 0.1103 s/iter. ETA=0:00:37 +[03/31 22:21:42] detectron2.evaluation.evaluator INFO: Inference done 391/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0433 s/iter. Total: 0.1065 s/iter. ETA=0:00:29 +[03/31 22:21:47] detectron2.evaluation.evaluator INFO: Inference done 438/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0433 s/iter. Total: 0.1065 s/iter. ETA=0:00:24 +[03/31 22:21:53] detectron2.evaluation.evaluator INFO: Inference done 486/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0432 s/iter. Total: 0.1064 s/iter. ETA=0:00:19 +[03/31 22:21:58] detectron2.evaluation.evaluator INFO: Inference done 531/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0438 s/iter. Total: 0.1070 s/iter. ETA=0:00:15 +[03/31 22:22:03] detectron2.evaluation.evaluator INFO: Inference done 577/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0440 s/iter. Total: 0.1072 s/iter. ETA=0:00:10 +[03/31 22:22:08] detectron2.evaluation.evaluator INFO: Inference done 622/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0445 s/iter. Total: 0.1077 s/iter. ETA=0:00:05 +[03/31 22:22:13] detectron2.evaluation.evaluator INFO: Inference done 667/672. Dataloading: 0.0016 s/iter. Inference: 0.0615 s/iter. Eval: 0.0447 s/iter. Total: 0.1079 s/iter. ETA=0:00:00 +[03/31 22:22:14] detectron2.evaluation.evaluator INFO: Total inference time: 0:01:12.838344 (0.109203 s / iter per device, on 4 devices) +[03/31 22:22:14] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:41 (0.061524 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-openearthmap/log.txt.rank2 b/OVRSIS/eval-openearthmap/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..01847e99878de381151a91f0a7dcb1e5f5878b45 --- /dev/null +++ b/OVRSIS/eval-openearthmap/log.txt.rank2 @@ -0,0 +1,577 @@ +[03/31 22:20:50] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 22:20:52] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:20:52] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-openearthmap', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/openearthmap.json', 'DATASETS.TEST', '("OpenEarthMap_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:20:52] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:20:52] detectron2.utils.env INFO: Using a generated random seed 56317278 +[03/31 22:20:54] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:20:54] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:20:54] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:20:55] detectron2.data.datasets.coco INFO: Loaded 2687 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/OpenEarthMap/images +[03/31 22:20:55] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:20:55] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:20:55] detectron2.data.common INFO: Serializing 2687 elements to byte tensors and concatenating them all ... +[03/31 22:20:55] detectron2.data.common INFO: Serialized dataset takes 0.69 MiB +[03/31 22:20:55] detectron2.data.datasets.coco INFO: Loaded 2687 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/OpenEarthMap/images +[03/31 22:20:55] detectron2.evaluation.evaluator INFO: Start inference on 672 batches +[03/31 22:21:02] detectron2.evaluation.evaluator INFO: Inference done 11/672. Dataloading: 0.0010 s/iter. Inference: 0.0615 s/iter. Eval: 0.0515 s/iter. Total: 0.1140 s/iter. ETA=0:01:15 +[03/31 22:21:07] detectron2.evaluation.evaluator INFO: Inference done 57/672. Dataloading: 0.0014 s/iter. Inference: 0.0616 s/iter. Eval: 0.0468 s/iter. Total: 0.1098 s/iter. ETA=0:01:07 +[03/31 22:21:12] detectron2.evaluation.evaluator INFO: Inference done 102/672. Dataloading: 0.0015 s/iter. Inference: 0.0616 s/iter. Eval: 0.0474 s/iter. Total: 0.1105 s/iter. ETA=0:01:03 +[03/31 22:21:17] detectron2.evaluation.evaluator INFO: Inference done 151/672. Dataloading: 0.0014 s/iter. Inference: 0.0614 s/iter. Eval: 0.0451 s/iter. Total: 0.1081 s/iter. ETA=0:00:56 +[03/31 22:21:22] detectron2.evaluation.evaluator INFO: Inference done 195/672. Dataloading: 0.0015 s/iter. Inference: 0.0615 s/iter. Eval: 0.0465 s/iter. Total: 0.1095 s/iter. ETA=0:00:52 +[03/31 22:21:27] detectron2.evaluation.evaluator INFO: Inference done 240/672. Dataloading: 0.0015 s/iter. Inference: 0.0614 s/iter. Eval: 0.0473 s/iter. Total: 0.1103 s/iter. ETA=0:00:47 +[03/31 22:21:32] detectron2.evaluation.evaluator INFO: Inference done 288/672. Dataloading: 0.0015 s/iter. Inference: 0.0614 s/iter. Eval: 0.0463 s/iter. Total: 0.1093 s/iter. ETA=0:00:41 +[03/31 22:21:37] detectron2.evaluation.evaluator INFO: Inference done 339/672. Dataloading: 0.0015 s/iter. Inference: 0.0614 s/iter. Eval: 0.0448 s/iter. Total: 0.1078 s/iter. ETA=0:00:35 +[03/31 22:21:42] detectron2.evaluation.evaluator INFO: Inference done 396/672. Dataloading: 0.0015 s/iter. Inference: 0.0613 s/iter. Eval: 0.0422 s/iter. Total: 0.1051 s/iter. ETA=0:00:29 +[03/31 22:21:47] detectron2.evaluation.evaluator INFO: Inference done 443/672. Dataloading: 0.0015 s/iter. Inference: 0.0613 s/iter. Eval: 0.0425 s/iter. Total: 0.1054 s/iter. ETA=0:00:24 +[03/31 22:21:52] detectron2.evaluation.evaluator INFO: Inference done 488/672. Dataloading: 0.0015 s/iter. Inference: 0.0613 s/iter. Eval: 0.0431 s/iter. Total: 0.1060 s/iter. ETA=0:00:19 +[03/31 22:21:58] detectron2.evaluation.evaluator INFO: Inference done 551/672. Dataloading: 0.0015 s/iter. Inference: 0.0613 s/iter. Eval: 0.0402 s/iter. Total: 0.1030 s/iter. ETA=0:00:12 +[03/31 22:22:03] detectron2.evaluation.evaluator INFO: Inference done 601/672. Dataloading: 0.0015 s/iter. Inference: 0.0613 s/iter. Eval: 0.0400 s/iter. Total: 0.1028 s/iter. ETA=0:00:07 +[03/31 22:22:08] detectron2.evaluation.evaluator INFO: Inference done 648/672. Dataloading: 0.0015 s/iter. Inference: 0.0613 s/iter. Eval: 0.0403 s/iter. Total: 0.1032 s/iter. ETA=0:00:02 +[03/31 22:22:11] detectron2.evaluation.evaluator INFO: Total inference time: 0:01:09.749660 (0.104572 s / iter per device, on 4 devices) +[03/31 22:22:11] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:40 (0.061291 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-openearthmap/log.txt.rank3 b/OVRSIS/eval-openearthmap/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..983c01bfc02a0f05eb8b89be3118a56c9d069864 --- /dev/null +++ b/OVRSIS/eval-openearthmap/log.txt.rank3 @@ -0,0 +1,577 @@ +[03/31 22:20:50] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 22:20:51] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:20:51] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-openearthmap', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/openearthmap.json', 'DATASETS.TEST', '("OpenEarthMap_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:20:51] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:20:51] detectron2.utils.env INFO: Using a generated random seed 55939418 +[03/31 22:20:54] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:20:54] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:20:54] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:20:55] detectron2.data.datasets.coco INFO: Loaded 2687 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/OpenEarthMap/images +[03/31 22:20:55] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:20:55] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:20:55] detectron2.data.common INFO: Serializing 2687 elements to byte tensors and concatenating them all ... +[03/31 22:20:55] detectron2.data.common INFO: Serialized dataset takes 0.69 MiB +[03/31 22:20:55] detectron2.data.datasets.coco INFO: Loaded 2687 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/OpenEarthMap/images +[03/31 22:20:55] detectron2.evaluation.evaluator INFO: Start inference on 671 batches +[03/31 22:21:02] detectron2.evaluation.evaluator INFO: Inference done 11/671. Dataloading: 0.0009 s/iter. Inference: 0.0629 s/iter. Eval: 0.0548 s/iter. Total: 0.1186 s/iter. ETA=0:01:18 +[03/31 22:21:07] detectron2.evaluation.evaluator INFO: Inference done 57/671. Dataloading: 0.0015 s/iter. Inference: 0.0614 s/iter. Eval: 0.0483 s/iter. Total: 0.1113 s/iter. ETA=0:01:08 +[03/31 22:21:12] detectron2.evaluation.evaluator INFO: Inference done 113/671. Dataloading: 0.0015 s/iter. Inference: 0.0613 s/iter. Eval: 0.0382 s/iter. Total: 0.1011 s/iter. ETA=0:00:56 +[03/31 22:21:17] detectron2.evaluation.evaluator INFO: Inference done 159/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0411 s/iter. Total: 0.1040 s/iter. ETA=0:00:53 +[03/31 22:21:22] detectron2.evaluation.evaluator INFO: Inference done 204/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0430 s/iter. Total: 0.1059 s/iter. ETA=0:00:49 +[03/31 22:21:27] detectron2.evaluation.evaluator INFO: Inference done 251/671. Dataloading: 0.0016 s/iter. Inference: 0.0613 s/iter. Eval: 0.0432 s/iter. Total: 0.1061 s/iter. ETA=0:00:44 +[03/31 22:21:32] detectron2.evaluation.evaluator INFO: Inference done 297/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0437 s/iter. Total: 0.1067 s/iter. ETA=0:00:39 +[03/31 22:21:37] detectron2.evaluation.evaluator INFO: Inference done 344/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0437 s/iter. Total: 0.1067 s/iter. ETA=0:00:34 +[03/31 22:21:42] detectron2.evaluation.evaluator INFO: Inference done 390/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0439 s/iter. Total: 0.1070 s/iter. ETA=0:00:30 +[03/31 22:21:47] detectron2.evaluation.evaluator INFO: Inference done 447/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0416 s/iter. Total: 0.1047 s/iter. ETA=0:00:23 +[03/31 22:21:52] detectron2.evaluation.evaluator INFO: Inference done 494/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0418 s/iter. Total: 0.1048 s/iter. ETA=0:00:18 +[03/31 22:21:57] detectron2.evaluation.evaluator INFO: Inference done 539/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0424 s/iter. Total: 0.1055 s/iter. ETA=0:00:13 +[03/31 22:22:03] detectron2.evaluation.evaluator INFO: Inference done 585/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0428 s/iter. Total: 0.1059 s/iter. ETA=0:00:09 +[03/31 22:22:08] detectron2.evaluation.evaluator INFO: Inference done 631/671. Dataloading: 0.0016 s/iter. Inference: 0.0614 s/iter. Eval: 0.0431 s/iter. Total: 0.1062 s/iter. ETA=0:00:04 +[03/31 22:22:13] detectron2.evaluation.evaluator INFO: Total inference time: 0:01:11.873606 (0.107918 s / iter per device, on 4 devices) +[03/31 22:22:13] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:40 (0.061419 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-uavid/config.yaml b/OVRSIS/eval-uavid/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2f8e9b5c785f946e85f73385aa8fecfea6262f49 --- /dev/null +++ b/OVRSIS/eval-uavid/config.yaml @@ -0,0 +1,423 @@ +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - uavid_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/uavid.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-uavid +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 diff --git a/OVRSIS/eval-uavid/delta_corr_logs/delta_corr_step_log.csv b/OVRSIS/eval-uavid/delta_corr_logs/delta_corr_step_log.csv new file mode 100644 index 0000000000000000000000000000000000000000..721955b981767df167d0d6493b2e9418af657284 --- /dev/null +++ b/OVRSIS/eval-uavid/delta_corr_logs/delta_corr_step_log.csv @@ -0,0 +1 @@ +step,gt_in_mean,non_gt_mean,gap,align_ratio diff --git a/OVRSIS/eval-uavid/inference/sem_seg_evaluation.pth b/OVRSIS/eval-uavid/inference/sem_seg_evaluation.pth new file mode 100644 index 0000000000000000000000000000000000000000..51c20f6e9dd4341640294aea4e4eeb934c79d562 --- /dev/null +++ b/OVRSIS/eval-uavid/inference/sem_seg_evaluation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a09cb4a4db4743d113a92e36cdf0ea8f88d1b2601c402833142b643197a682fa +size 2912 diff --git a/OVRSIS/eval-uavid/inference/sem_seg_predictions.json b/OVRSIS/eval-uavid/inference/sem_seg_predictions.json new file mode 100644 index 0000000000000000000000000000000000000000..c4a9c26dfdebcf738b895d274fe9dfaf578f1d17 --- /dev/null +++ b/OVRSIS/eval-uavid/inference/sem_seg_predictions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05b5e60e0cf0f57db85d9620b2d8654679c39de5585873338fbc1dfc575e7b5c +size 45676277 diff --git a/OVRSIS/eval-uavid/log.txt b/OVRSIS/eval-uavid/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..5136a8142aa2fd32440b6daa0a7f5cb5dc3c67ff --- /dev/null +++ b/OVRSIS/eval-uavid/log.txt @@ -0,0 +1,1004 @@ +[03/31 22:11:56] detectron2 INFO: Rank of current process: 0. World size: 4 +[03/31 22:11:57] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:11:57] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-uavid', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/uavid.json', 'DATASETS.TEST', '("uavid_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:11:57] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:11:58] detectron2 INFO: Running with full config: +CUDNN_BENCHMARK: true +DATALOADER: + ASPECT_RATIO_GROUPING: true + FILTER_EMPTY_ANNOTATIONS: true + NUM_WORKERS: 8 + REPEAT_SQRT: true + REPEAT_THRESHOLD: 0.0 + SAMPLER_TRAIN: TrainingSampler +DATASETS: + PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 + PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 + PROPOSAL_FILES_TEST: [] + PROPOSAL_FILES_TRAIN: [] + TEST: + - uavid_all_sem_seg + TRAIN: + - OVRSIS95K_train_sem_seg + VAL_ALL: + - coco_2017_val_all_stuff_sem_seg +FLOAT32_PRECISION: '' +GLOBAL: + HACK: 1.0 +INPUT: + COLOR_AUG_SSD: true + CROP: + ENABLED: true + SINGLE_CATEGORY_MAX_AREA: 1.0 + SIZE: + - 384 + - 384 + TYPE: absolute + DATASET_MAPPER_NAME: mask_former_semantic + FORMAT: RGB + MASK_FORMAT: polygon + MAX_SIZE_TEST: 2560 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 640 + MIN_SIZE_TRAIN: + - 384 + MIN_SIZE_TRAIN_SAMPLING: choice + RANDOM_FLIP: horizontal + SIZE_DIVISIBILITY: 384 +MODEL: + ANCHOR_GENERATOR: + ANGLES: + - - -90 + - 0 + - 90 + ASPECT_RATIOS: + - - 0.5 + - 1.0 + - 2.0 + NAME: DefaultAnchorGenerator + OFFSET: 0.0 + SIZES: + - - 32 + - 64 + - 128 + - 256 + - 512 + BACKBONE: + FREEZE_AT: 2 + NAME: build_resnet_backbone + CLIP_PIXEL_MEAN: + - 122.7709383 + - 116.7460125 + - 104.09373615 + CLIP_PIXEL_STD: + - 68.5005327 + - 66.6321579 + - 70.323163 + DEVICE: cuda + FPN: + FUSE_TYPE: sum + IN_FEATURES: [] + NORM: '' + OUT_CHANNELS: 256 + KEYPOINT_ON: false + LOAD_PROPOSALS: false + MASK_FORMER: + SIZE_DIVISIBILITY: 32 + MASK_ON: false + META_ARCHITECTURE: CATSeg + PANOPTIC_FPN: + COMBINE: + ENABLED: true + INSTANCES_CONFIDENCE_THRESH: 0.5 + OVERLAP_THRESH: 0.5 + STUFF_AREA_LIMIT: 4096 + INSTANCE_LOSS_WEIGHT: 1.0 + PINI: + ENABLED: true + IMAGE_NOISE_TYPE: gaussian + IMAGE_STUDENT_T_DF: 3.0 + IMAGE_VPN_ENABLED: true + IS_VIS: false + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + TEXT_NOISE_TYPE: gaussian + TEXT_STUDENT_T_DF: 3.0 + TEXT_VPN_ENABLED: true + PIXEL_MEAN: + - 123.675 + - 116.28 + - 103.53 + PIXEL_STD: + - 58.395 + - 57.12 + - 57.375 + PROMPT_ENSEMBLE: false + PROMPT_ENSEMBLE_TYPE: single + PROPOSAL_GENERATOR: + MIN_SIZE: 0 + NAME: RPN + RESNETS: + DEFORM_MODULATED: false + DEFORM_NUM_GROUPS: 1 + DEFORM_ON_PER_STAGE: + - false + - false + - false + - false + DEPTH: 50 + NORM: FrozenBN + NUM_GROUPS: 1 + OUT_FEATURES: + - res4 + RES2_OUT_CHANNELS: 256 + RES4_DILATION: 1 + RES5_DILATION: 1 + RES5_MULTI_GRID: + - 1 + - 2 + - 4 + STEM_OUT_CHANNELS: 64 + STEM_TYPE: deeplab + STRIDE_IN_1X1: true + WIDTH_PER_GROUP: 64 + RETINANET: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_WEIGHTS: &id002 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + FOCAL_LOSS_ALPHA: 0.25 + FOCAL_LOSS_GAMMA: 2.0 + IN_FEATURES: + - p3 + - p4 + - p5 + - p6 + - p7 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.4 + - 0.5 + NMS_THRESH_TEST: 0.5 + NORM: '' + NUM_CLASSES: 80 + NUM_CONVS: 4 + PRIOR_PROB: 0.01 + SCORE_THRESH_TEST: 0.05 + SMOOTH_L1_LOSS_BETA: 0.1 + TOPK_CANDIDATES_TEST: 1000 + ROI_BOX_CASCADE_HEAD: + BBOX_REG_WEIGHTS: + - &id001 + - 10.0 + - 10.0 + - 5.0 + - 5.0 + - - 20.0 + - 20.0 + - 10.0 + - 10.0 + - - 30.0 + - 30.0 + - 15.0 + - 15.0 + IOUS: + - 0.5 + - 0.6 + - 0.7 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id001 + CLS_AGNOSTIC_BBOX_REG: false + CONV_DIM: 256 + FC_DIM: 1024 + FED_LOSS_FREQ_WEIGHT_POWER: 0.5 + FED_LOSS_NUM_CLASSES: 50 + NAME: '' + NORM: '' + NUM_CONV: 0 + NUM_FC: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + SMOOTH_L1_BETA: 0.0 + TRAIN_ON_PRED_BOXES: false + USE_FED_LOSS: false + USE_SIGMOID_CE: false + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 512 + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - 1 + IOU_THRESHOLDS: + - 0.5 + NAME: Res5ROIHeads + NMS_THRESH_TEST: 0.5 + NUM_CLASSES: 80 + POSITIVE_FRACTION: 0.25 + PROPOSAL_APPEND_GT: true + SCORE_THRESH_TEST: 0.05 + ROI_KEYPOINT_HEAD: + CONV_DIMS: + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + - 512 + LOSS_WEIGHT: 1.0 + MIN_KEYPOINTS_PER_IMAGE: 1 + NAME: KRCNNConvDeconvUpsampleHead + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true + NUM_KEYPOINTS: 17 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: false + CONV_DIM: 256 + NAME: MaskRCNNConvUpsampleHead + NORM: '' + NUM_CONV: 0 + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 0 + POOLER_TYPE: ROIAlignV2 + RPN: + BATCH_SIZE_PER_IMAGE: 256 + BBOX_REG_LOSS_TYPE: smooth_l1 + BBOX_REG_LOSS_WEIGHT: 1.0 + BBOX_REG_WEIGHTS: *id002 + BOUNDARY_THRESH: -1 + CONV_DIMS: + - -1 + HEAD_NAME: StandardRPNHead + IN_FEATURES: + - res4 + IOU_LABELS: + - 0 + - -1 + - 1 + IOU_THRESHOLDS: + - 0.3 + - 0.7 + LOSS_WEIGHT: 1.0 + NMS_THRESH: 0.7 + POSITIVE_FRACTION: 0.5 + POST_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 6000 + PRE_NMS_TOPK_TRAIN: 12000 + SMOOTH_L1_BETA: 0.0 + SEM_SEG_HEAD: + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + ASPP_CHANNELS: 256 + ASPP_DILATIONS: + - 6 + - 12 + - 18 + ASPP_DROPOUT: 0.1 + ATTENTION_TYPE: linear + CLIP_FINETUNE: attention + CLIP_PRETRAINED: ViT-B/16 + CLIP_PRETRAINED_DATASET: ViT-B/16 + COMMON_STRIDE: 4 + CONVS_DIM: 128 + DECODER_DIMS: + - 64 + - 32 + DECODER_GUIDANCE_DIMS: + - 256 + - 128 + DECODER_GUIDANCE_PROJ_DIMS: + - 32 + - 16 + FEATURE_RESOLUTION: + - 24 + - 24 + HIDDEN_DIMS: 128 + IGNORE_VALUE: 255 + IN_FEATURES: + - res2 + - res3 + - res4 + LOSS_TYPE: hard_pixel_mining + LOSS_WEIGHT: 1.0 + NAME: CATSegHead + NORM: GN + NUM_CLASSES: 39 + NUM_HEADS: 4 + NUM_LAYERS: 2 + POOLING_SIZES: + - 1 + - 1 + PROJECT_CHANNELS: + - 48 + PROJECT_FEATURES: + - res2 + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEST_CLASS_INDEXES: datasets/coco/coco_stuff/split/unseen_indexes.json + TEST_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/uavid.json + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + TRAIN_CLASS_INDEXES: datasets/coco/coco_stuff/split/seen_indexes.json + TRAIN_CLASS_JSON: /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json + USE_DEPTHWISE_SEPARABLE_CONV: false + WINDOW_SIZES: 12 + SWIN: + APE: false + ATTN_DROP_RATE: 0.0 + DEPTHS: + - 2 + - 2 + - 6 + - 2 + DROP_PATH_RATE: 0.3 + DROP_RATE: 0.0 + EMBED_DIM: 96 + MLP_RATIO: 4.0 + NUM_HEADS: + - 3 + - 6 + - 12 + - 24 + OUT_FEATURES: + - res2 + - res3 + - res4 + - res5 + PATCH_NORM: true + PATCH_SIZE: 4 + PRETRAIN_IMG_SIZE: 224 + QKV_BIAS: true + QK_SCALE: null + WINDOW_SIZE: 7 + WEIGHTS: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth +OUTPUT_DIR: output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-uavid +SEED: -1 +SOLVER: + AMP: + ENABLED: false + BACKBONE_MULTIPLIER: 0.0 + BASE_LR: 0.0002 + BASE_LR_END: 0.0 + BIAS_LR_FACTOR: 1.0 + CHECKPOINT_PERIOD: 5000 + CLIP_GRADIENTS: + CLIP_TYPE: full_model + CLIP_VALUE: 0.01 + ENABLED: true + NORM_TYPE: 2.0 + CLIP_MULTIPLIER: 0.01 + GAMMA: 0.1 + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + MAX_ITER: 40000 + MOMENTUM: 0.9 + NESTEROV: false + NUM_DECAYS: 3 + OPTIMIZER: ADAMW + POLY_LR_CONSTANT_ENDING: 0.0 + POLY_LR_POWER: 0.9 + REFERENCE_WORLD_SIZE: 0 + RESCALE_INTERVAL: false + STEPS: + - 30000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WARMUP_METHOD: linear + WEIGHT_DECAY: 0.0001 + WEIGHT_DECAY_BIAS: null + WEIGHT_DECAY_EMBED: 0.0 + WEIGHT_DECAY_NORM: 0.0 +TEST: + AUG: + ENABLED: false + FLIP: true + MAX_SIZE: 4000 + MIN_SIZES: + - 400 + - 500 + - 600 + - 700 + - 800 + - 900 + - 1000 + - 1100 + - 1200 + DETECTIONS_PER_IMAGE: 100 + EVAL_PERIOD: 5000 + EXPECTED_RESULTS: [] + KEYPOINT_OKS_SIGMAS: [] + PRECISE_BN: + ENABLED: false + NUM_ITER: 200 + SLIDING_WINDOW: true +VERSION: 2 +VIS_PERIOD: 0 + +[03/31 22:11:58] detectron2 INFO: Full config saved to output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-uavid/config.yaml +[03/31 22:11:58] d2.utils.env INFO: Using a generated random seed 62067475 +[03/31 22:12:01] d2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:12:01] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:12:01] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:12:02] d2.data.datasets.coco INFO: Loaded 270 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/uavid/Images +[03/31 22:12:02] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:12:02] d2.data.common INFO: Serializing the dataset using: +[03/31 22:12:02] d2.data.common INFO: Serializing 270 elements to byte tensors and concatenating them all ... +[03/31 22:12:02] d2.data.common INFO: Serialized dataset takes 0.07 MiB +[03/31 22:12:02] d2.data.datasets.coco INFO: Loaded 270 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/uavid/Images +[03/31 22:12:02] d2.evaluation.evaluator INFO: Start inference on 68 batches +[03/31 22:12:14] d2.evaluation.evaluator INFO: Inference done 11/68. Dataloading: 0.0018 s/iter. Inference: 0.0610 s/iter. Eval: 0.5115 s/iter. Total: 0.5742 s/iter. ETA=0:00:32 +[03/31 22:12:19] d2.evaluation.evaluator INFO: Inference done 17/68. Dataloading: 0.0015 s/iter. Inference: 0.0612 s/iter. Eval: 0.6795 s/iter. Total: 0.7425 s/iter. ETA=0:00:37 +[03/31 22:12:25] d2.evaluation.evaluator INFO: Inference done 23/68. Dataloading: 0.0018 s/iter. Inference: 0.0613 s/iter. Eval: 0.7325 s/iter. Total: 0.7957 s/iter. ETA=0:00:35 +[03/31 22:12:30] d2.evaluation.evaluator INFO: Inference done 29/68. Dataloading: 0.0019 s/iter. Inference: 0.0615 s/iter. Eval: 0.7631 s/iter. Total: 0.8268 s/iter. ETA=0:00:32 +[03/31 22:12:36] d2.evaluation.evaluator INFO: Inference done 35/68. Dataloading: 0.0020 s/iter. Inference: 0.0614 s/iter. Eval: 0.7824 s/iter. Total: 0.8459 s/iter. ETA=0:00:27 +[03/31 22:12:41] d2.evaluation.evaluator INFO: Inference done 40/68. Dataloading: 0.0020 s/iter. Inference: 0.0614 s/iter. Eval: 0.8090 s/iter. Total: 0.8726 s/iter. ETA=0:00:24 +[03/31 22:12:46] d2.evaluation.evaluator INFO: Inference done 46/68. Dataloading: 0.0020 s/iter. Inference: 0.0613 s/iter. Eval: 0.8087 s/iter. Total: 0.8724 s/iter. ETA=0:00:19 +[03/31 22:12:52] d2.evaluation.evaluator INFO: Inference done 52/68. Dataloading: 0.0021 s/iter. Inference: 0.0613 s/iter. Eval: 0.8172 s/iter. Total: 0.8808 s/iter. ETA=0:00:14 +[03/31 22:12:58] d2.evaluation.evaluator INFO: Inference done 59/68. Dataloading: 0.0020 s/iter. Inference: 0.0613 s/iter. Eval: 0.8115 s/iter. Total: 0.8752 s/iter. ETA=0:00:07 +[03/31 22:13:03] d2.evaluation.evaluator INFO: Inference done 68/68. Dataloading: 0.0020 s/iter. Inference: 0.0612 s/iter. Eval: 0.7688 s/iter. Total: 0.8324 s/iter. ETA=0:00:00 +[03/31 22:13:03] d2.evaluation.evaluator INFO: Total inference time: 0:00:53.134380 (0.843403 s / iter per device, on 4 devices) +[03/31 22:13:03] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:03 (0.061244 s / iter per device, on 4 devices) +[03/31 22:13:04] d2.evaluation.sem_seg_evaluation INFO: OrderedDict([('sem_seg', {'mIoU': 40.22302131460728, 'fwIoU': 53.978673190239334, 'IoU-Background/Clutter': 7.3597719499675405, 'BoundaryIoU-Background/Clutter': 51.658754504228355, 'min(IoU, B-Iou)-Background/Clutter': 7.3597719499675405, 'IoU-Building': 83.58894423559187, 'BoundaryIoU-Building': 30.201336120824585, 'min(IoU, B-Iou)-Building': 30.201336120824585, 'IoU-Road': 61.23070216895522, 'BoundaryIoU-Road': 19.93817178948046, 'min(IoU, B-Iou)-Road': 19.93817178948046, 'IoU-Tree': 54.42265014396974, 'BoundaryIoU-Tree': 22.184537516019986, 'min(IoU, B-Iou)-Tree': 22.184537516019986, 'IoU-Low vegetation': 47.402885049148516, 'BoundaryIoU-Low vegetation': 19.336424522313102, 'min(IoU, B-Iou)-Low vegetation': 19.336424522313102, 'IoU-Car': 26.463499331728933, 'BoundaryIoU-Car': 1.8569040753367894, 'min(IoU, B-Iou)-Car': 1.8569040753367894, 'IoU-Human': 1.092696322889113, 'BoundaryIoU-Human': 0.8769443752652711, 'min(IoU, B-Iou)-Human': 0.8769443752652711, 'mACC': 57.76383683431533, 'pACC': 63.68502926890345, 'ACC-Background/Clutter': 9.064188526650668, 'ACC-Building': 89.05600922722185, 'ACC-Road': 84.22471061039126, 'ACC-Tree': 59.569370264435804, 'ACC-Low vegetation': 69.92643951697292, 'ACC-Car': 76.49575324997548, 'ACC-Human': 16.010386444559284})]) +[03/31 22:13:04] d2.engine.defaults INFO: Evaluation results for uavid_all_sem_seg in csv format: +[03/31 22:13:04] d2.evaluation.testing INFO: copypaste: Task: sem_seg +[03/31 22:13:04] d2.evaluation.testing INFO: copypaste: mIoU,fwIoU,mACC,pACC +[03/31 22:13:04] d2.evaluation.testing INFO: copypaste: 40.2230,53.9787,57.7638,63.6850 diff --git a/OVRSIS/eval-uavid/log.txt.rank1 b/OVRSIS/eval-uavid/log.txt.rank1 new file mode 100644 index 0000000000000000000000000000000000000000..1f6cf586f0b19148a03233dd38381c61c59550dc --- /dev/null +++ b/OVRSIS/eval-uavid/log.txt.rank1 @@ -0,0 +1,571 @@ +[03/31 22:11:56] detectron2 INFO: Rank of current process: 1. World size: 4 +[03/31 22:11:59] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:11:59] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-uavid', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/uavid.json', 'DATASETS.TEST', '("uavid_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:11:59] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:11:59] detectron2.utils.env INFO: Using a generated random seed 63103037 +[03/31 22:12:01] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:12:01] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:12:01] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:12:02] detectron2.data.datasets.coco INFO: Loaded 270 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/uavid/Images +[03/31 22:12:02] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:12:02] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:12:02] detectron2.data.common INFO: Serializing 270 elements to byte tensors and concatenating them all ... +[03/31 22:12:02] detectron2.data.common INFO: Serialized dataset takes 0.07 MiB +[03/31 22:12:02] detectron2.data.datasets.coco INFO: Loaded 270 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/uavid/Images +[03/31 22:12:02] detectron2.evaluation.evaluator INFO: Start inference on 68 batches +[03/31 22:12:14] detectron2.evaluation.evaluator INFO: Inference done 11/68. Dataloading: 0.0018 s/iter. Inference: 0.0633 s/iter. Eval: 0.5037 s/iter. Total: 0.5688 s/iter. ETA=0:00:32 +[03/31 22:12:19] detectron2.evaluation.evaluator INFO: Inference done 20/68. Dataloading: 0.0018 s/iter. Inference: 0.0636 s/iter. Eval: 0.5075 s/iter. Total: 0.5730 s/iter. ETA=0:00:27 +[03/31 22:12:25] detectron2.evaluation.evaluator INFO: Inference done 27/68. Dataloading: 0.0018 s/iter. Inference: 0.0631 s/iter. Eval: 0.5702 s/iter. Total: 0.6352 s/iter. ETA=0:00:26 +[03/31 22:12:30] detectron2.evaluation.evaluator INFO: Inference done 34/68. Dataloading: 0.0018 s/iter. Inference: 0.0627 s/iter. Eval: 0.6062 s/iter. Total: 0.6708 s/iter. ETA=0:00:22 +[03/31 22:12:36] detectron2.evaluation.evaluator INFO: Inference done 43/68. Dataloading: 0.0019 s/iter. Inference: 0.0624 s/iter. Eval: 0.5925 s/iter. Total: 0.6568 s/iter. ETA=0:00:16 +[03/31 22:12:41] detectron2.evaluation.evaluator INFO: Inference done 49/68. Dataloading: 0.0019 s/iter. Inference: 0.0623 s/iter. Eval: 0.6298 s/iter. Total: 0.6942 s/iter. ETA=0:00:13 +[03/31 22:12:47] detectron2.evaluation.evaluator INFO: Inference done 56/68. Dataloading: 0.0019 s/iter. Inference: 0.0622 s/iter. Eval: 0.6379 s/iter. Total: 0.7021 s/iter. ETA=0:00:08 +[03/31 22:12:52] detectron2.evaluation.evaluator INFO: Inference done 64/68. Dataloading: 0.0019 s/iter. Inference: 0.0621 s/iter. Eval: 0.6330 s/iter. Total: 0.6971 s/iter. ETA=0:00:02 +[03/31 22:12:56] detectron2.evaluation.evaluator INFO: Total inference time: 0:00:45.591320 (0.723672 s / iter per device, on 4 devices) +[03/31 22:12:56] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:03 (0.062074 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-uavid/log.txt.rank2 b/OVRSIS/eval-uavid/log.txt.rank2 new file mode 100644 index 0000000000000000000000000000000000000000..cd456be159e34cf9443f3f9f79dae6af84d1b13f --- /dev/null +++ b/OVRSIS/eval-uavid/log.txt.rank2 @@ -0,0 +1,571 @@ +[03/31 22:11:56] detectron2 INFO: Rank of current process: 2. World size: 4 +[03/31 22:11:58] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:11:58] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-uavid', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/uavid.json', 'DATASETS.TEST', '("uavid_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:11:58] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:11:58] detectron2.utils.env INFO: Using a generated random seed 62363543 +[03/31 22:12:01] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:12:01] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:12:01] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:12:02] detectron2.data.datasets.coco INFO: Loaded 270 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/uavid/Images +[03/31 22:12:02] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:12:02] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:12:02] detectron2.data.common INFO: Serializing 270 elements to byte tensors and concatenating them all ... +[03/31 22:12:02] detectron2.data.common INFO: Serialized dataset takes 0.07 MiB +[03/31 22:12:02] detectron2.data.datasets.coco INFO: Loaded 270 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/uavid/Images +[03/31 22:12:02] detectron2.evaluation.evaluator INFO: Start inference on 67 batches +[03/31 22:12:15] detectron2.evaluation.evaluator INFO: Inference done 11/67. Dataloading: 0.0012 s/iter. Inference: 0.0610 s/iter. Eval: 0.4895 s/iter. Total: 0.5517 s/iter. ETA=0:00:30 +[03/31 22:12:21] detectron2.evaluation.evaluator INFO: Inference done 21/67. Dataloading: 0.0014 s/iter. Inference: 0.0613 s/iter. Eval: 0.4864 s/iter. Total: 0.5491 s/iter. ETA=0:00:25 +[03/31 22:12:26] detectron2.evaluation.evaluator INFO: Inference done 30/67. Dataloading: 0.0015 s/iter. Inference: 0.0612 s/iter. Eval: 0.5039 s/iter. Total: 0.5667 s/iter. ETA=0:00:20 +[03/31 22:12:31] detectron2.evaluation.evaluator INFO: Inference done 37/67. Dataloading: 0.0016 s/iter. Inference: 0.0612 s/iter. Eval: 0.5436 s/iter. Total: 0.6064 s/iter. ETA=0:00:18 +[03/31 22:12:37] detectron2.evaluation.evaluator INFO: Inference done 43/67. Dataloading: 0.0017 s/iter. Inference: 0.0612 s/iter. Eval: 0.5924 s/iter. Total: 0.6555 s/iter. ETA=0:00:15 +[03/31 22:12:43] detectron2.evaluation.evaluator INFO: Inference done 49/67. Dataloading: 0.0017 s/iter. Inference: 0.0612 s/iter. Eval: 0.6407 s/iter. Total: 0.7038 s/iter. ETA=0:00:12 +[03/31 22:12:48] detectron2.evaluation.evaluator INFO: Inference done 55/67. Dataloading: 0.0017 s/iter. Inference: 0.0612 s/iter. Eval: 0.6564 s/iter. Total: 0.7195 s/iter. ETA=0:00:08 +[03/31 22:12:54] detectron2.evaluation.evaluator INFO: Inference done 65/67. Dataloading: 0.0017 s/iter. Inference: 0.0611 s/iter. Eval: 0.6311 s/iter. Total: 0.6942 s/iter. ETA=0:00:01 +[03/31 22:12:56] detectron2.evaluation.evaluator INFO: Total inference time: 0:00:43.735057 (0.705404 s / iter per device, on 4 devices) +[03/31 22:12:56] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:03 (0.061140 s / iter per device, on 4 devices) diff --git a/OVRSIS/eval-uavid/log.txt.rank3 b/OVRSIS/eval-uavid/log.txt.rank3 new file mode 100644 index 0000000000000000000000000000000000000000..5eb22bee0eb4fce5294ca33a84380c46cb2720a0 --- /dev/null +++ b/OVRSIS/eval-uavid/log.txt.rank3 @@ -0,0 +1,570 @@ +[03/31 22:11:56] detectron2 INFO: Rank of current process: 3. World size: 4 +[03/31 22:11:58] detectron2 INFO: Environment info: +------------------------------- ------------------------------------------------------------------------------------ +sys.platform linux +Python 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] +numpy 1.26.4 +detectron2 0.6 @/gemini/space/zhaozy/huotao/000OVS/CAT-Seg-cass/detectron2-main/detectron2 +Compiler GCC 11.4 +CUDA compiler CUDA 12.1 +detectron2 arch flags 9.0 +DETECTRON2_ENV_MODULE +PyTorch 2.4.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torch +PyTorch debug build False +torch._C._GLIBCXX_USE_CXX11_ABI False +GPU available Yes +GPU 0,1,2,3 NVIDIA H100 80GB HBM3 (arch=9.0) +Driver version 580.82.07 +CUDA_HOME /usr/local/cuda +Pillow 11.3.0 +torchvision 0.19.0+cu121 @/root/miniconda3/envs/S2_Corr/lib/python3.10/site-packages/torchvision +torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 +fvcore 0.1.5.post20221221 +iopath 0.1.9 +cv2 4.10.0 +------------------------------- ------------------------------------------------------------------------------------ +PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 12.1 + - NVCC architecture flags: -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;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 + - CuDNN 90.1 (built against CUDA 12.4) + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, + +[03/31 22:11:58] detectron2 INFO: Command line arguments: Namespace(config_file='configs/vitb_384_OVRSIS95K.yaml', resume=False, eval_only=True, num_gpus=4, num_machines=1, machine_rank=0, dist_url='auto', opts=['OUTPUT_DIR', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/OVRSIS/eval-uavid', 'MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON', '/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/uavid.json', 'DATASETS.TEST', '("uavid_all_sem_seg",)', 'TEST.SLIDING_WINDOW', 'True', 'MODEL.SEM_SEG_HEAD.POOLING_SIZES', '[1,1]', 'MODEL.WEIGHTS', 'output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth']) +[03/31 22:11:58] detectron2 INFO: Contents of args.config_file=configs/vitb_384_OVRSIS95K.yaml: +_BASE_: config.yaml +MODEL: + META_ARCHITECTURE: "CATSeg" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + SEM_SEG_HEAD: + NAME: "CATSegHead" + IN_FEATURES: ["res2", "res3", "res4"] + IGNORE_VALUE: 255 + NUM_CLASSES: 39 + TRAIN_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + TEST_CLASS_JSON: "/gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS95K.json" + CLIP_PRETRAINED: "ViT-B/16" + PROMPT_DEPTH: 0 + PROMPT_LENGTH: 0 + TEXT_GUIDANCE_DIM: 512 + TEXT_GUIDANCE_PROJ_DIM: 128 + APPEARANCE_GUIDANCE_DIM: 512 + APPEARANCE_GUIDANCE_PROJ_DIM: 128 + DECODER_DIMS: [64, 32] + DECODER_GUIDANCE_DIMS: [256, 128] + DECODER_GUIDANCE_PROJ_DIMS: [32, 16] + NUM_LAYERS: 2 + NUM_HEADS: 4 + HIDDEN_DIMS: 128 + POOLING_SIZES: [2, 2] + FEATURE_RESOLUTION: [24, 24] + WINDOW_SIZES: 12 + ATTENTION_TYPE: "linear" + CLIP_FINETUNE: "attention" + PINI: + ENABLED: True + IMAGE_VPN_ENABLED: True + TEXT_VPN_ENABLED: True + REDUCTION: 1 + TEXT_NOISE_STD: 0.02 + IS_VIS: False + # text noise + IMAGE_NOISE_TYPE: "gaussian" + IMAGE_STUDENT_T_DF: 3.0 + # text noise + TEXT_NOISE_TYPE: "gaussian" + TEXT_STUDENT_T_DF: 3.0 + + PROMPT_ENSEMBLE_TYPE: "single" +DATASETS: + TRAIN: ("OVRSIS95K_train_sem_seg",) + TEST: ("OVRSIS95K_val_sem_seg",) +INPUT: + MIN_SIZE_TRAIN: (384, ) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (384, 384) + SIZE_DIVISIBILITY: 384 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +SOLVER: + IMS_PER_BATCH: 8 + LR_SCHEDULER_NAME: WarmupCosineLR + BASE_LR: 0.0002 + MAX_ITER: 40000 + BACKBONE_MULTIPLIER: 0.0 + CLIP_MULTIPLIER: 0.01 +TEST: + EVAL_PERIOD: 5000 + +[03/31 22:11:58] detectron2.utils.env INFO: Using a generated random seed 62773964 +[03/31 22:12:01] detectron2.engine.defaults INFO: Model: +CATSeg( + (sem_seg_head): CATSegHead( + (predictor): CATSegPredictor( + (clip_model): CLIP( + (visual): VisualTransformer( + (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False) + (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): Sequential( + (0): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (1): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (2): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (3): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (4): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (5): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (6): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (7): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (8): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (9): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (10): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (11): ResidualAttentionBlock( + (attn): Attention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) + ) + (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=512, out_features=2048, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=2048, out_features=512, bias=True) + ) + (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (token_embedding): Embedding(49408, 512) + (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Aggregator( + (layers): ModuleList( + (0-1): 2 x AggregatorLayer( + (sptial_agg): SwinTransformerBlockWrapper( + (block_1): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (block_2): SwinTransformerBlock( + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (attn): WindowAttention( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=128, out_features=128, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (softmax): Softmax(dim=-1) + ) + (drop_path): Identity() + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (mlp): Mlp( + (fc1): Linear(in_features=128, out_features=512, bias=True) + (act): GELU(approximate='none') + (drop1): Dropout(p=0.0, inplace=False) + (norm): Identity() + (fc2): Linear(in_features=512, out_features=128, bias=True) + (drop2): Dropout(p=0.0, inplace=False) + ) + ) + (guidance_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + (class_agg): ClassTransformerLayer( + (pool): AvgPool2d(kernel_size=[1, 1], stride=[1, 1], padding=0) + (attention): AttentionLayer( + (q): Linear(in_features=256, out_features=128, bias=True) + (k): Linear(in_features=256, out_features=128, bias=True) + (v): Linear(in_features=128, out_features=128, bias=True) + (attention): LinearAttention() + ) + (MLP): Sequential( + (0): Linear(in_features=128, out_features=512, bias=True) + (1): ReLU() + (2): Linear(in_features=512, out_features=128, bias=True) + ) + (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (conv1): Conv2d(1, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) + (guidance_projection): Sequential( + (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (text_guidance_projection): Sequential( + (0): Linear(in_features=512, out_features=128, bias=True) + (1): ReLU() + ) + (decoder_guidance_projection): ModuleList( + (0): Sequential( + (0): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + (1): Sequential( + (0): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU() + ) + ) + (decoder1): Up( + (up): ConvTranspose2d(128, 96, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(4, 64, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(4, 64, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (decoder2): Up( + (up): ConvTranspose2d(64, 48, kernel_size=(2, 2), stride=(2, 2)) + (conv): DoubleConv( + (double_conv): Sequential( + (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(2, 32, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(2, 32, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + ) + ) + (head): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (image_vpn): ImageVpnGenerator( + (cross_attn): CrossAttention( + (fc_q): Linear(in_features=512, out_features=512, bias=True) + (fc_k): Linear(in_features=512, out_features=512, bias=True) + (fc_v): Linear(in_features=512, out_features=512, bias=True) + (softmax): Softmax(dim=-1) + ) + (fc_mean): Linear(in_features=512, out_features=512, bias=True) + (fc_variance): Linear(in_features=512, out_features=512, bias=True) + ) + (text_vpn): TextVpnGenerator() + ) + ) + (upsample1): ConvTranspose2d(768, 256, kernel_size=(2, 2), stride=(2, 2)) + (upsample2): ConvTranspose2d(768, 128, kernel_size=(4, 4), stride=(4, 4)) +) +[03/31 22:12:01] detectron2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:12:01] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/ablation_pini_agg/noise_img_gaussian_txt_gaussian/model_final.pth ... +[03/31 22:12:02] detectron2.data.datasets.coco INFO: Loaded 270 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/uavid/Images +[03/31 22:12:02] detectron2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(640, 640), max_size=2560, sample_style='choice')] +[03/31 22:12:02] detectron2.data.common INFO: Serializing the dataset using: +[03/31 22:12:02] detectron2.data.common INFO: Serializing 270 elements to byte tensors and concatenating them all ... +[03/31 22:12:02] detectron2.data.common INFO: Serialized dataset takes 0.07 MiB +[03/31 22:12:02] detectron2.data.datasets.coco INFO: Loaded 270 images with semantic segmentation from /gemini/space/zhaozy/libingyu/00_OVRSIS_v2/OVRSISBenchV2/datasets/OVRSIS/uavid/Images +[03/31 22:12:02] detectron2.evaluation.evaluator INFO: Start inference on 67 batches +[03/31 22:12:14] detectron2.evaluation.evaluator INFO: Inference done 11/67. Dataloading: 0.0017 s/iter. Inference: 0.0612 s/iter. Eval: 0.4634 s/iter. Total: 0.5263 s/iter. ETA=0:00:29 +[03/31 22:12:19] detectron2.evaluation.evaluator INFO: Inference done 20/67. Dataloading: 0.0018 s/iter. Inference: 0.0612 s/iter. Eval: 0.4818 s/iter. Total: 0.5449 s/iter. ETA=0:00:25 +[03/31 22:12:24] detectron2.evaluation.evaluator INFO: Inference done 29/67. Dataloading: 0.0020 s/iter. Inference: 0.0612 s/iter. Eval: 0.4915 s/iter. Total: 0.5548 s/iter. ETA=0:00:21 +[03/31 22:12:29] detectron2.evaluation.evaluator INFO: Inference done 38/67. Dataloading: 0.0021 s/iter. Inference: 0.0613 s/iter. Eval: 0.4983 s/iter. Total: 0.5618 s/iter. ETA=0:00:16 +[03/31 22:12:35] detectron2.evaluation.evaluator INFO: Inference done 47/67. Dataloading: 0.0022 s/iter. Inference: 0.0614 s/iter. Eval: 0.5125 s/iter. Total: 0.5763 s/iter. ETA=0:00:11 +[03/31 22:12:40] detectron2.evaluation.evaluator INFO: Inference done 55/67. Dataloading: 0.0022 s/iter. Inference: 0.0614 s/iter. Eval: 0.5246 s/iter. Total: 0.5884 s/iter. ETA=0:00:07 +[03/31 22:12:45] detectron2.evaluation.evaluator INFO: Inference done 65/67. Dataloading: 0.0022 s/iter. Inference: 0.0613 s/iter. Eval: 0.5173 s/iter. Total: 0.5810 s/iter. ETA=0:00:01 +[03/31 22:12:47] detectron2.evaluation.evaluator INFO: Total inference time: 0:00:36.758980 (0.592887 s / iter per device, on 4 devices) +[03/31 22:12:47] detectron2.evaluation.evaluator INFO: Total inference pure compute time: 0:00:03 (0.061337 s / iter per device, on 4 devices)