How to run inference with test-time augmentation on Mask R-CNN X152 model?

[11/11 08:31:24] detectron2 INFO: Rank of current process: 0. World size: 4 [11/11 08:31:25] detectron2 INFO: Environment info:

sys.platform linux Python 3.6.8 (default, Oct 7 2019, 12:59:55) [GCC 8.3.0] Numpy 1.17.3 Detectron2 Compiler GCC 7.4 Detectron2 CUDA Compiler 10.1 DETECTRON2_ENV_MODULE PyTorch 1.3.0 PyTorch Debug Build False torchvision 0.5.0.dev20191018 CUDA available True GPU 0,1,2,3,4,5,6,7 Tesla V100-SXM2-32GB CUDA_HOME /usr/local/cuda NVCC Cuda compilation tools, release 10.1, V10.1.243 Pillow 6.2.0 cv2 4.1.2

PyTorch built with:

GCC 7.3
Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v0.20.5 (Git Hash 0125f28c61c1f822fd48570b4c1066f96fcb9b2e)
OpenMP 201511 (a.k.a. OpenMP 4.5)
NNPACK is enabled
CUDA Runtime 10.1
NVCC architecture flags: -gencode;arch=compute_35,code=sm_35;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_50,code=compute_50
CuDNN 7.6.3
Magma 2.5.1
Build settings: BLAS=MKL, BUILD_NAMEDTENSOR=OFF, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Wno-stringop-overflow, DISABLE_NUMA=1, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=True, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF,

[11/11 08:31:25] detectron2 INFO: Command line arguments: Namespace(config_file='./configs/gongjing-detection/cascade_mask_rcnn_X_152_FPN_siz2.yaml', dist_url='tcp://127.0.0.1:49152', eval_only=True, machine_rank=0, num_gpus=4, num_machines=1, opts=['TEST.AUG.ENABLED', 'True'], resume=False) [11/11 08:31:25] detectron2 INFO: Contents of args.config_file=./configs/gongjing-detection/cascade_mask_rcnn_X_152_FPN_siz2.yaml: BASE: "../Base-RCNN-FPN.yaml" MODEL: MASK_ON: False WEIGHTS: "catalog://ImageNetPretrained/FAIR/X-152-32x8d-IN5k" RESNETS: STRIDE_IN_1X1: False # this is a C2 model NUM_GROUPS: 32 WIDTH_PER_GROUP: 8 DEPTH: 152 DEFORM_ON_PER_STAGE: [False, True, True, True] ROI_HEADS: NAME: "CascadeROIHeads" ROI_BOX_HEAD: NAME: "FastRCNNConvFCHead" NUM_CONV: 4 NUM_FC: 1 NORM: "GN" CLS_AGNOSTIC_BBOX_REG: True ROI_MASK_HEAD: NUM_CONV: 8 NORM: "GN" RPN: POST_NMS_TOPK_TRAIN: 2000 INPUT: MIN_SIZE_TRAIN: (800, 1024) MIN_SIZE_TRAIN_SAMPLING: "range" MAX_SIZE_TRAIN: 1440 MIN_SIZE_TEST: 1000 MAX_SIZE_TEST: 1440 CROP: ENABLED: True TEST: EVAL_PERIOD: 0 AUG: ENABLED: True MIN_SIZES: (800,900,1000 ) MAX_SIZE: 4000 FLIP: False DATASETS: TRAIN: ("my_dataset_train2",) TEST: ("my_dataset_val2",) SOLVER: STEPS: (13910, 18548) MAX_ITER: 23184 BASE_LR: 0.01 OUTPUT_DIR: "./output_cascade_X_153_FPN_siz2" [11/11 08:31:25] detectron2 INFO: Running with full config: CUDNN_BENCHMARK: False DATALOADER: ASPECT_RATIO_GROUPING: True NUM_WORKERS: 4 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: ('my_dataset_val2',) TRAIN: ('my_dataset_train2',) GLOBAL: HACK: 1.0 INPUT: CROP: ENABLED: True SIZE: [0.9, 0.9] TYPE: relative_range FORMAT: BGR MASK_FORMAT: polygon MAX_SIZE_TEST: 1440 MAX_SIZE_TRAIN: 1440 MIN_SIZE_TEST: 1000 MIN_SIZE_TRAIN: (800, 1024) MIN_SIZE_TRAIN_SAMPLING: range MODEL: ANCHOR_GENERATOR: ANGLES: [[-90, 0, 90]] ASPECT_RATIOS: [[0.5, 1.0, 2.0]] NAME: DefaultAnchorGenerator SIZES: [[32], [64], [128], [256], [512]] BACKBONE: FREEZE_AT: 2 NAME: build_resnet_fpn_backbone DEVICE: cuda FPN: FUSE_TYPE: sum IN_FEATURES: ['res2', 'res3', 'res4', 'res5'] NORM: OUT_CHANNELS: 256 KEYPOINT_ON: False LOAD_PROPOSALS: False MASK_ON: False META_ARCHITECTURE: GeneralizedRCNN PANOPTIC_FPN: COMBINE: ENABLED: True INSTANCES_CONFIDENCE_THRESH: 0.5 OVERLAP_THRESH: 0.5 STUFF_AREA_LIMIT: 4096 INSTANCE_LOSS_WEIGHT: 1.0 PIXEL_MEAN: [103.53, 116.28, 123.675] PIXEL_STD: [1.0, 1.0, 1.0] PROPOSAL_GENERATOR: MIN_SIZE: 0 NAME: RPN RESNETS: DEFORM_MODULATED: False DEFORM_NUM_GROUPS: 1 DEFORM_ON_PER_STAGE: [False, True, True, True] DEPTH: 152 NORM: FrozenBN NUM_GROUPS: 32 OUT_FEATURES: ['res2', 'res3', 'res4', 'res5'] RES2_OUT_CHANNELS: 256 RES5_DILATION: 1 STEM_OUT_CHANNELS: 64 STRIDE_IN_1X1: False WIDTH_PER_GROUP: 8 RETINANET: BBOX_REG_WEIGHTS: (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 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: ((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_WEIGHTS: (10.0, 10.0, 5.0, 5.0) CLS_AGNOSTIC_BBOX_REG: True CONV_DIM: 256 FC_DIM: 1024 NAME: FastRCNNConvFCHead NORM: GN NUM_CONV: 4 NUM_FC: 1 POOLER_RESOLUTION: 7 POOLER_SAMPLING_RATIO: 0 POOLER_TYPE: ROIAlignV2 SMOOTH_L1_BETA: 0.0 ROI_HEADS: BATCH_SIZE_PER_IMAGE: 512 IN_FEATURES: ['p2', 'p3', 'p4', 'p5'] IOU_LABELS: [0, 1] IOU_THRESHOLDS: [0.5] NAME: CascadeROIHeads NMS_THRESH_TEST: 0.5 NUM_CLASSES: 1 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: GN NUM_CONV: 8 POOLER_RESOLUTION: 14 POOLER_SAMPLING_RATIO: 0 POOLER_TYPE: ROIAlignV2 RPN: BATCH_SIZE_PER_IMAGE: 256 BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0) BOUNDARY_THRESH: -1 HEAD_NAME: StandardRPNHead IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6'] 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: 1000 PRE_NMS_TOPK_TRAIN: 2000 SMOOTH_L1_BETA: 0.0 SEM_SEG_HEAD: COMMON_STRIDE: 4 CONVS_DIM: 128 IGNORE_VALUE: 255 IN_FEATURES: ['p2', 'p3', 'p4', 'p5'] LOSS_WEIGHT: 1.0 NAME: SemSegFPNHead NORM: GN NUM_CLASSES: 54 WEIGHTS: catalog://ImageNetPretrained/FAIR/X-152-32x8d-IN5k OUTPUT_DIR: ./output_cascade_X_153_FPN_siz2 SEED: -1 SOLVER: BASE_LR: 0.01 BIAS_LR_FACTOR: 1.0 CHECKPOINT_PERIOD: 2000 GAMMA: 0.1 IMS_PER_BATCH: 16 LR_SCHEDULER_NAME: WarmupMultiStepLR MAX_ITER: 23184 MOMENTUM: 0.9 STEPS: (13910, 18548) WARMUP_FACTOR: 0.001 WARMUP_ITERS: 1000 WARMUP_METHOD: linear WEIGHT_DECAY: 0.0001 WEIGHT_DECAY_BIAS: 0.0001 WEIGHT_DECAY_NORM: 0.0 TEST: AUG: ENABLED: True FLIP: False MAX_SIZE: 4000 MIN_SIZES: (800, 900, 1000) DETECTIONS_PER_IMAGE: 100 EVAL_PERIOD: 0 EXPECTED_RESULTS: [] KEYPOINT_OKS_SIGMAS: [] PRECISE_BN: ENABLED: False NUM_ITER: 200 VERSION: 2 [11/11 08:31:25] detectron2 INFO: Full config saved to /home/baiguangdong/detectron2-master/output_cascade_X_153_FPN_siz2/config.yaml [11/11 08:31:25] d2.utils.env INFO: Using a generated random seed 25464151 [11/11 08:31:29] d2.engine.defaults INFO: Model: GeneralizedRCNN( (backbone): FPN( (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (top_block): LastLevelMaxPool() (bottom_up): ResNet( (stem): BasicStem( (conv1): Conv2d( 3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) ) (res2): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv1): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) ) (res3): Sequential( (0): DeformBottleneckBlock( (shortcut): Conv2d( 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv1): Conv2d( 256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 18, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (conv2): DeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (1): DeformBottleneckBlock( (conv1): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (2): DeformBottleneckBlock( (conv1): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (3): DeformBottleneckBlock( (conv1): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (4): DeformBottleneckBlock( (conv1): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (5): DeformBottleneckBlock( (conv1): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (6): DeformBottleneckBlock( (conv1): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (7): DeformBottleneckBlock( (conv1): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) ) (res4): Sequential( (0): DeformBottleneckBlock( (shortcut): Conv2d( 512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv1): Conv2d( 512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (1): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (2): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (3): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (4): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (5): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (6): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (7): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (8): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (9): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (10): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (11): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (12): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (13): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (14): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (15): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (16): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (17): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (18): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (19): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (20): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (21): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (22): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (23): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (24): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (25): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (26): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (27): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (28): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (29): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (30): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (31): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (32): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (33): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (34): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (35): DeformBottleneckBlock( (conv1): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv2_offset): Conv2d(1024, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=1024, out_channels=1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv3): Conv2d( 1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) ) (res5): Sequential( (0): DeformBottleneckBlock( (shortcut): Conv2d( 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv1): Conv2d( 1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv2_offset): Conv2d(2048, 18, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (conv2): DeformConv( in_channels=2048, out_channels=2048, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv3): Conv2d( 2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) (1): DeformBottleneckBlock( (conv1): Conv2d( 2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv2_offset): Conv2d(2048, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=2048, out_channels=2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv3): Conv2d( 2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) (2): DeformBottleneckBlock( (conv1): Conv2d( 2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv2_offset): Conv2d(2048, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): DeformConv( in_channels=2048, out_channels=2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv3): Conv2d( 2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) ) ) ) (proposal_generator): RPN( (anchor_generator): DefaultAnchorGenerator( (cell_anchors): BufferList() ) (rpn_head): StandardRPNHead( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1)) (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1)) ) ) (roi_heads): CascadeROIHeads( (box_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True) (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True) (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True) (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True) ) ) (box_head): ModuleList( (0): FastRCNNConvFCHead( (conv1): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (conv3): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (conv4): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (fc1): Linear(in_features=12544, out_features=1024, bias=True) ) (1): FastRCNNConvFCHead( (conv1): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (conv3): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (conv4): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (fc1): Linear(in_features=12544, out_features=1024, bias=True) ) (2): FastRCNNConvFCHead( (conv1): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (conv3): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (conv4): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 256, eps=1e-05, affine=True) ) (fc1): Linear(in_features=12544, out_features=1024, bias=True) ) ) (box_predictor): ModuleList( (0): FastRCNNOutputLayers( (cls_score): Linear(in_features=1024, out_features=2, bias=True) (bbox_pred): Linear(in_features=1024, out_features=4, bias=True) ) (1): FastRCNNOutputLayers( (cls_score): Linear(in_features=1024, out_features=2, bias=True) (bbox_pred): Linear(in_features=1024, out_features=4, bias=True) ) (2): FastRCNNOutputLayers( (cls_score): Linear(in_features=1024, out_features=2, bias=True) (bbox_pred): Linear(in_features=1024, out_features=4, bias=True) ) ) ) ) [11/11 08:31:29] fvcore.common.checkpoint INFO: Loading checkpoint from ./output_cascade_X_153_FPN_siz2/model_final.pth [11/11 08:31:30] d2.data.datasets.coco INFO: Loaded 1292 images in COCO format from ./datasets/gongjing2/val.json [11/11 08:31:30] d2.data.build INFO: Distribution of training instances among all 1 categories: [36m	category	#instances
abnormal	3140
		[0m

[11/11 08:31:30] d2.evaluation.evaluator INFO: Start inference on 323 images [11/11 08:33:07] d2.evaluation.evaluator INFO: Total inference time: 0:01:29 (0.279874 s / img per device, on 4 devices) [11/11 08:33:07] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:01:28 (0.276990 s / img per device, on 4 devices) [11/11 08:33:07] d2.evaluation.coco_evaluation INFO: Preparing results for COCO format ... [11/11 08:33:07] d2.evaluation.coco_evaluation INFO: Saving results to ./output_cascade_X_153_FPN_siz2/inference/coco_instances_results.json [11/11 08:33:07] d2.evaluation.coco_evaluation INFO: Evaluating predictions ... [11/11 08:33:08] d2.evaluation.coco_evaluation INFO: Evaluation results for bbox:	AP	AP50	AP75	APs	APm	APl
84.673	93.418	90.251	87.581	88.611	78.224

[11/11 08:33:08] d2.engine.defaults INFO: Evaluation results for my_dataset_val2 in csv format: [11/11 08:33:08] d2.evaluation.testing INFO: copypaste: Task: bbox [11/11 08:33:08] d2.evaluation.testing INFO: copypaste: AP,AP50,AP75,APs,APm,APl [11/11 08:33:08] d2.evaluation.testing INFO: copypaste: 84.6727,93.4183,90.2510,87.5808,88.6114,78.2240 [11/11 08:33:08] d2.trainer INFO: Running inference with test-time augmentation ... [11/11 08:33:08] d2.data.datasets.coco INFO: Loaded 1292 images in COCO format from ./datasets/gongjing2/val.json [11/11 08:33:08] d2.evaluation.evaluator INFO: Start inference on 323 images [11/11 08:37:21] d2.evaluation.evaluator INFO: Total inference time: 0:04:03 (0.764151 s / img per device, on 4 devices) [11/11 08:37:21] d2.evaluation.evaluator INFO: Total inference pure compute time: 0:04:01 (0.760205 s / img per device, on 4 devices) [11/11 08:37:25] d2.evaluation.coco_evaluation INFO: Preparing results for COCO format ... [11/11 08:37:25] d2.evaluation.coco_evaluation INFO: Saving results to ./output_cascade_X_153_FPN_siz2/inference_TTA/coco_instances_results.json [11/11 08:37:25] d2.evaluation.coco_evaluation INFO: Evaluating predictions ... [11/11 08:37:25] d2.evaluation.coco_evaluation WARNING: No predictions from the model! Set scores to -1 [11/11 08:37:25] d2.engine.defaults INFO: Evaluation results for my_dataset_val2 in csv format: [11/11 08:37:25] d2.evaluation.testing INFO: copypaste: Task: bbox [11/11 08:37:25] d2.evaluation.testing INFO: copypaste: AP,AP50,AP75,APs,APm,APl [11/11 08:37:25] d2.evaluation.testing INFO: copypaste: -1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000

facebookresearch / detectron2

How to run inference with test-time augmentation on Mask R-CNN X152 model? #296