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youngwanLEE / centermask2

[CVPR 2020] CenterMask : Real-time Anchor-Free Instance Segmentation
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The accuracy is 0 #76

Open jiameixia1202 opened 3 years ago

jiameixia1202 commented 3 years ago

My server does not have 8 GPUs. When I use 4 GPUs for training (without any modification to the network), I just change num-gpus to 4. After training, the segmentation accuracy is always 0.00. Do you need to modify other parameters?

haderalim commented 3 years ago

Did you update of the registered coco dataset? And what parameters and its values, you used in config file?

jiameixia1202 commented 3 years ago

The following are the parameters we used in training without any modification. I don't quite understand what you mean by "the registered coco dataset"

/root/anaconda3/envs/detectron2/bin/python3.6 /jmx/centermask2/train_net.py --config-file configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml --num-gpus 4 Command Line Args: Namespace(config_file='configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml', dist_url='tcp://127.0.0.1:49152', eval_only=False, machine_rank=0, num_gpus=4, num_machines=1, opts=[], resume=False) Config 'configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml' has no VERSION. Assuming it to be compatible with latest v2. Config 'configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml' has no VERSION. Assuming it to be compatible with latest v2. Config 'configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml' has no VERSION. Assuming it to be compatible with latest v2. Config 'configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml' has no VERSION. Assuming it to be compatible with latest v2. [12/08 18:45:25 detectron2]: Rank of current process: 0. World size: 4 [12/08 18:45:27 detectron2]: Environment info:

sys.platform linux Python 3.6.12 Anaconda, Inc. (default, Sep 8 2020, 23:10:56) [GCC 7.3.0] numpy 1.19.4 detectron2 0.3 @/jmx/detectron2/detectron2 Compiler GCC 7.4 CUDA compiler CUDA 10.1 detectron2 arch flags 6.1 DETECTRON2_ENV_MODULE PyTorch 1.6.0+cu101 @/root/anaconda3/envs/detectron2/lib/python3.6/site-packages/torch PyTorch debug build False GPU available True GPU 0,1,2,3 GeForce GTX 1080 (arch=6.1) CUDA_HOME /usr/local/cuda Pillow 8.0.1 torchvision 0.7.0+cu101 @/root/anaconda3/envs/detectron2/lib/python3.6/site-packages/torchvision torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5 fvcore 0.1.2.post20201122 cv2 4.4.0

PyTorch built with:

[12/08 18:45:27 detectron2]: Command line arguments: Namespace(config_file='configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml', dist_url='tcp://127.0.0.1:49152', eval_only=False, machine_rank=0, num_gpus=4, num_machines=1, opts=[], resume=False) [12/08 18:45:27 detectron2]: Contents of args.config_file=configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml: BASE: "Base-CenterMask-Lite-VoVNet.yaml"

MODEL: WEIGHTS: "/jmx/centermask2/models/vovnet19_ese_detectron2.pth" VOVNET: CONV_BODY : "V-19-eSE" SOLVER: STEPS: (300000, 340000) MAX_ITER: 360000 OUTPUT_DIR: "output/centermask/CenterMask-Lite-V-19-ms-4x"

[12/08 18:45:27 detectron2]: Running with full config: CUDNN_BENCHMARK: False DATALOADER: ASPECT_RATIO_GROUPING: True FILTER_EMPTY_ANNOTATIONS: True NUM_WORKERS: 0 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: ('coco_2017_val',) TRAIN: ('coco_2017_train',) GLOBAL: HACK: 1.0 INPUT: CROP: ENABLED: False SIZE: [0.9, 0.9] TYPE: relative_range FORMAT: BGR MASK_FORMAT: polygon MAX_SIZE_TEST: 1000 MAX_SIZE_TRAIN: 1000 MIN_SIZE_TEST: 600 MIN_SIZE_TRAIN: (580, 600) MIN_SIZE_TRAIN_SAMPLING: choice RANDOM_FLIP: horizontal 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: 0 NAME: build_fcos_vovnet_fpn_backbone DEVICE: cuda FCOS: CENTER_SAMPLE: True FPN_STRIDES: [8, 16, 32, 64, 128] INFERENCE_TH_TEST: 0.05 INFERENCE_TH_TRAIN: 0.05 IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7'] LOC_LOSS_TYPE: giou LOSS_ALPHA: 0.25 LOSS_GAMMA: 2.0 NMS_TH: 0.6 NORM: GN NUM_BOX_CONVS: 2 NUM_CLASSES: 80 NUM_CLS_CONVS: 2 NUM_SHARE_CONVS: 0 POST_NMS_TOPK_TEST: 50 POST_NMS_TOPK_TRAIN: 100 POS_RADIUS: 1.5 PRE_NMS_TOPK_TEST: 1000 PRE_NMS_TOPK_TRAIN: 1000 PRIOR_PROB: 0.01 SIZES_OF_INTEREST: [64, 128, 256, 512] THRESH_WITH_CTR: False TOP_LEVELS: 2 USE_DEFORMABLE: False USE_RELU: True USE_SCALE: True FPN: FUSE_TYPE: sum IN_FEATURES: ['stage3', 'stage4', 'stage5'] NORM: OUT_CHANNELS: 128 KEYPOINT_ON: False LOAD_PROPOSALS: False MASKIOU_LOSS_WEIGHT: 1.0 MASKIOU_ON: True MASK_ON: True META_ARCHITECTURE: GeneralizedRCNN MOBILENET: False 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: FCOS 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 RES5_DILATION: 1 STEM_OUT_CHANNELS: 64 STRIDE_IN_1X1: True WIDTH_PER_GROUP: 64 RETINANET: BBOX_REG_LOSS_TYPE: smooth_l1 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 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: ((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: (10.0, 10.0, 5.0, 5.0) CLS_AGNOSTIC_BBOX_REG: False CONV_DIM: 256 FC_DIM: 1024 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 ROI_HEADS: BATCH_SIZE_PER_IMAGE: 512 IN_FEATURES: ['p3', 'p4', 'p5'] IOU_LABELS: [0, 1] IOU_THRESHOLDS: [0.5] NAME: CenterROIHeads NMS_THRESH_TEST: 0.5 NUM_CLASSES: 80 POSITIVE_FRACTION: 0.25 PROPOSAL_APPEND_GT: True SCORE_THRESH_TEST: 0.05 ROI_KEYPOINT_HEAD: ASSIGN_CRITERION: ratio CONV_DIMS: (512, 512, 512, 512, 512, 512, 512, 512) IN_FEATURES: ['p2', 'p3', 'p4', 'p5'] 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_MASKIOU_HEAD: CONV_DIM: 128 NAME: MaskIoUHead NUM_CONV: 2 ROI_MASK_HEAD: ASSIGN_CRITERION: ratio CLS_AGNOSTIC_MASK: False CONV_DIM: 128 NAME: SpatialAttentionMaskHead NORM: NUM_CONV: 2 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: (1.0, 1.0, 1.0, 1.0) BOUNDARY_THRESH: -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: 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 VOVNET: BACKBONE_OUT_CHANNELS: 256 CONV_BODY: V-19-eSE DEFORMABLE_GROUPS: 1 NORM: FrozenBN OUT_CHANNELS: 256 OUT_FEATURES: ['stage3', 'stage4', 'stage5'] STAGE_WITH_DCN: (False, False, False, False) WITH_MODULATED_DCN: False WEIGHTS: /jmx/centermask2/models/vovnet19_ese_detectron2.pth OUTPUT_DIR: output/centermask/CenterMask-Lite-V-19-ms-4x SEED: -1 SOLVER: AMP: ENABLED: False BASE_LR: 0.01 BIAS_LR_FACTOR: 1.0 CHECKPOINT_PERIOD: 10000 CLIP_GRADIENTS: CLIP_TYPE: value CLIP_VALUE: 1.0 ENABLED: False NORM_TYPE: 2.0 GAMMA: 0.1 IMS_PER_BATCH: 16 LR_SCHEDULER_NAME: WarmupMultiStepLR MAX_ITER: 360000 MOMENTUM: 0.9 NESTEROV: False REFERENCE_WORLD_SIZE: 0 STEPS: (300000, 340000) 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: False FLIP: True MAX_SIZE: 4000 MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200) DETECTIONS_PER_IMAGE: 100 EVAL_PERIOD: 0 EXPECTED_RESULTS: [] KEYPOINT_OKS_SIGMAS: [] PRECISE_BN: ENABLED: False NUM_ITER: 200 VERSION: 2 VIS_PERIOD: 0 [12/08 18:45:27 detectron2]: Full config saved to output/centermask/CenterMask-Lite-V-19-ms-4x/config.yaml [12/08 18:45:27 d2.utils.env]: Using a generated random seed 27801612

GeneralizedRCNN( (backbone): FPN( (fpn_lateral3): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) (fpn_output3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral4): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1)) (fpn_output4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral5): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1)) (fpn_output5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (top_block): LastLevelP6P7( (p6): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (p7): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) (bottom_up): VoVNet( (stem): Sequential( (stem_1/conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (stem_1/norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) (stem_1/relu): ReLU(inplace=True) (stem_2/conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (stem_2/norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) (stem_2/relu): ReLU(inplace=True) (stem_3/conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (stem_3/norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) (stem_3/relu): ReLU(inplace=True) ) (stage2): _OSA_stage( (OSA2_1): _OSA_module( (layers): ModuleList( (0): Sequential( (OSA2_1_0/conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA2_1_0/norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) (OSA2_1_0/relu): ReLU(inplace=True) ) (1): Sequential( (OSA2_1_1/conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA2_1_1/norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) (OSA2_1_1/relu): ReLU(inplace=True) ) (2): Sequential( (OSA2_1_2/conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA2_1_2/norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) (OSA2_1_2/relu): ReLU(inplace=True) ) ) (concat): Sequential( (OSA2_1_concat/conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (OSA2_1_concat/norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) (OSA2_1_concat/relu): ReLU(inplace=True) ) (ese): eSEModule( (avg_pool): AdaptiveAvgPool2d(output_size=1) (fc): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (hsigmoid): Hsigmoid() ) ) ) (stage3): _OSA_stage( (Pooling): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) (OSA3_1): _OSA_module( (layers): ModuleList( (0): Sequential( (OSA3_1_0/conv): Conv2d(256, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA3_1_0/norm): FrozenBatchNorm2d(num_features=160, eps=1e-05) (OSA3_1_0/relu): ReLU(inplace=True) ) (1): Sequential( (OSA3_1_1/conv): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA3_1_1/norm): FrozenBatchNorm2d(num_features=160, eps=1e-05) (OSA3_1_1/relu): ReLU(inplace=True) ) (2): Sequential( (OSA3_1_2/conv): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA3_1_2/norm): FrozenBatchNorm2d(num_features=160, eps=1e-05) (OSA3_1_2/relu): ReLU(inplace=True) ) ) (concat): Sequential( (OSA3_1_concat/conv): Conv2d(736, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (OSA3_1_concat/norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) (OSA3_1_concat/relu): ReLU(inplace=True) ) (ese): eSEModule( (avg_pool): AdaptiveAvgPool2d(output_size=1) (fc): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) (hsigmoid): Hsigmoid() ) ) ) (stage4): _OSA_stage( (Pooling): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) (OSA4_1): _OSA_module( (layers): ModuleList( (0): Sequential( (OSA4_1_0/conv): Conv2d(512, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA4_1_0/norm): FrozenBatchNorm2d(num_features=192, eps=1e-05) (OSA4_1_0/relu): ReLU(inplace=True) ) (1): Sequential( (OSA4_1_1/conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA4_1_1/norm): FrozenBatchNorm2d(num_features=192, eps=1e-05) (OSA4_1_1/relu): ReLU(inplace=True) ) (2): Sequential( (OSA4_1_2/conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA4_1_2/norm): FrozenBatchNorm2d(num_features=192, eps=1e-05) (OSA4_1_2/relu): ReLU(inplace=True) ) ) (concat): Sequential( (OSA4_1_concat/conv): Conv2d(1088, 768, kernel_size=(1, 1), stride=(1, 1), bias=False) (OSA4_1_concat/norm): FrozenBatchNorm2d(num_features=768, eps=1e-05) (OSA4_1_concat/relu): ReLU(inplace=True) ) (ese): eSEModule( (avg_pool): AdaptiveAvgPool2d(output_size=1) (fc): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1)) (hsigmoid): Hsigmoid() ) ) ) (stage5): _OSA_stage( (Pooling): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) (OSA5_1): _OSA_module( (layers): ModuleList( (0): Sequential( (OSA5_1_0/conv): Conv2d(768, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA5_1_0/norm): FrozenBatchNorm2d(num_features=224, eps=1e-05) (OSA5_1_0/relu): ReLU(inplace=True) ) (1): Sequential( (OSA5_1_1/conv): Conv2d(224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA5_1_1/norm): FrozenBatchNorm2d(num_features=224, eps=1e-05) (OSA5_1_1/relu): ReLU(inplace=True) ) (2): Sequential( (OSA5_1_2/conv): Conv2d(224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA5_1_2/norm): FrozenBatchNorm2d(num_features=224, eps=1e-05) (OSA5_1_2/relu): ReLU(inplace=True) ) ) (concat): Sequential( (OSA5_1_concat/conv): Conv2d(1440, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (OSA5_1_concat/norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) (OSA5_1_concat/relu): ReLU(inplace=True) ) (ese): eSEModule( (avg_pool): AdaptiveAvgPool2d(output_size=1) (fc): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) (hsigmoid): Hsigmoid() ) ) ) ) ) (proposal_generator): FCOS( (iou_loss): IOULoss() (fcos_head): FCOSHead( (cls_tower): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): GroupNorm(32, 128, eps=1e-05, affine=True) (2): ReLU() (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): GroupNorm(32, 128, eps=1e-05, affine=True) (5): ReLU() ) (bbox_tower): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): GroupNorm(32, 128, eps=1e-05, affine=True) (2): ReLU() (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): GroupNorm(32, 128, eps=1e-05, affine=True) (5): ReLU() ) (share_tower): Sequential() (cls_logits): Conv2d(128, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (bbox_pred): Conv2d(128, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (ctrness): Conv2d(128, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (scales): ModuleList( (0): Scale() (1): Scale() (2): Scale() (3): Scale() (4): Scale() ) ) ) (roi_heads): CenterROIHeads( (mask_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True) (1): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True) (2): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True) ) ) (mask_head): SpatialAttentionMaskHead( (mask_fcn1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (mask_fcn2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (spatialAtt): SpatialAttention( (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (sigmoid): Sigmoid() ) (deconv): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2)) (predictor): Conv2d(128, 80, kernel_size=(1, 1), stride=(1, 1)) ) (maskiou_head): MaskIoUHead( (maskiou_fcn1): Conv2d(129, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (maskiou_fcn2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (maskiou_fc1): Linear(in_features=6272, out_features=1024, bias=True) (maskiou_fc2): Linear(in_features=1024, out_features=1024, bias=True) (maskiou): Linear(in_features=1024, out_features=80, bias=True) (pooling): MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=[0, 0], dilation=[1, 1], ceil_mode=False) ) ) ) [12/08 18:45:28 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(580, 600), max_size=1000, sample_style='choice'), RandomFlip()] [12/08 18:46:12 d2.data.datasets.coco]: Loading datasets/coco/annotations/instances_train2017.json takes 44.32 seconds. [12/08 18:46:14 d2.data.datasets.coco]: Loaded 118287 images in COCO format from datasets/coco/annotations/instances_train2017.json [12/08 18:46:38 d2.data.build]: Removed 1021 images with no usable annotations. 117266 images left. [12/08 18:46:52 d2.data.build]: Distribution of instances among all 80 categories: category #instances category #instances category #instances
person 257253 bicycle 7056 car 43533
motorcycle 8654 airplane 5129 bus 6061
train 4570 truck 9970 boat 10576
traffic light 12842 fire hydrant 1865 stop sign 1983
parking meter 1283 bench 9820 bird 10542
cat 4766 dog 5500 horse 6567
sheep 9223 cow 8014 elephant 5484
bear 1294 zebra 5269 giraffe 5128
backpack 8714 umbrella 11265 handbag 12342
tie 6448 suitcase 6112 frisbee 2681
skis 6623 snowboard 2681 sports ball 6299
kite 8802 baseball bat 3273 baseball gl.. 3747
skateboard 5536 surfboard 6095 tennis racket 4807
bottle 24070 wine glass 7839 cup 20574
fork 5474 knife 7760 spoon 6159
bowl 14323 banana 9195 apple 5776
sandwich 4356 orange 6302 broccoli 7261
carrot 7758 hot dog 2884 pizza 5807
donut 7005 cake 6296 chair 38073
couch 5779 potted plant 8631 bed 4192
dining table 15695 toilet 4149 tv 5803
laptop 4960 mouse 2261 remote 5700
keyboard 2854 cell phone 6422 microwave 1672
oven 3334 toaster 225 sink 5609
refrigerator 2634 book 24077 clock 6320
vase 6577 scissors 1464 teddy bear 4729
hair drier 198 toothbrush 1945
total 849949

[12/08 18:46:52 d2.data.build]: Using training sampler TrainingSampler [12/08 18:46:53 d2.data.common]: Serializing 117266 elements to byte tensors and concatenating them all ... [12/08 18:47:04 d2.data.common]: Serialized dataset takes 451.21 MiB

GeneralizedRCNN( (backbone): FPN( (fpn_lateral3): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) (fpn_output3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral4): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1)) (fpn_output4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral5): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1)) (fpn_output5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (top_block): LastLevelP6P7( (p6): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (p7): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) (bottom_up): VoVNet( (stem): Sequential( (stem_1/conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (stem_1/norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) (stem_1/relu): ReLU(inplace=True) (stem_2/conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (stem_2/norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) (stem_2/relu): ReLU(inplace=True) (stem_3/conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (stem_3/norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) (stem_3/relu): ReLU(inplace=True) ) (stage2): _OSA_stage( (OSA2_1): _OSA_module( (layers): ModuleList( (0): Sequential( (OSA2_1_0/conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA2_1_0/norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) (OSA2_1_0/relu): ReLU(inplace=True) ) (1): Sequential( (OSA2_1_1/conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA2_1_1/norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) (OSA2_1_1/relu): ReLU(inplace=True) ) (2): Sequential( (OSA2_1_2/conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA2_1_2/norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) (OSA2_1_2/relu): ReLU(inplace=True) ) ) (concat): Sequential( (OSA2_1_concat/conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (OSA2_1_concat/norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) (OSA2_1_concat/relu): ReLU(inplace=True) ) (ese): eSEModule( (avg_pool): AdaptiveAvgPool2d(output_size=1) (fc): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (hsigmoid): Hsigmoid() ) ) ) (stage3): _OSA_stage( (Pooling): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) (OSA3_1): _OSA_module( (layers): ModuleList( (0): Sequential( (OSA3_1_0/conv): Conv2d(256, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA3_1_0/norm): FrozenBatchNorm2d(num_features=160, eps=1e-05) (OSA3_1_0/relu): ReLU(inplace=True) ) (1): Sequential( (OSA3_1_1/conv): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA3_1_1/norm): FrozenBatchNorm2d(num_features=160, eps=1e-05) (OSA3_1_1/relu): ReLU(inplace=True) ) (2): Sequential( (OSA3_1_2/conv): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA3_1_2/norm): FrozenBatchNorm2d(num_features=160, eps=1e-05) (OSA3_1_2/relu): ReLU(inplace=True) ) ) (concat): Sequential( (OSA3_1_concat/conv): Conv2d(736, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (OSA3_1_concat/norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) (OSA3_1_concat/relu): ReLU(inplace=True) ) (ese): eSEModule( (avg_pool): AdaptiveAvgPool2d(output_size=1) (fc): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) (hsigmoid): Hsigmoid() ) ) ) (stage4): _OSA_stage( (Pooling): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) (OSA4_1): _OSA_module( (layers): ModuleList( (0): Sequential( (OSA4_1_0/conv): Conv2d(512, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA4_1_0/norm): FrozenBatchNorm2d(num_features=192, eps=1e-05) (OSA4_1_0/relu): ReLU(inplace=True) ) (1): Sequential( (OSA4_1_1/conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA4_1_1/norm): FrozenBatchNorm2d(num_features=192, eps=1e-05) (OSA4_1_1/relu): ReLU(inplace=True) ) (2): Sequential( (OSA4_1_2/conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA4_1_2/norm): FrozenBatchNorm2d(num_features=192, eps=1e-05) (OSA4_1_2/relu): ReLU(inplace=True) ) ) (concat): Sequential( (OSA4_1_concat/conv): Conv2d(1088, 768, kernel_size=(1, 1), stride=(1, 1), bias=False) (OSA4_1_concat/norm): FrozenBatchNorm2d(num_features=768, eps=1e-05) (OSA4_1_concat/relu): ReLU(inplace=True) ) (ese): eSEModule( (avg_pool): AdaptiveAvgPool2d(output_size=1) (fc): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1)) (hsigmoid): Hsigmoid() ) ) ) (stage5): _OSA_stage( (Pooling): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) (OSA5_1): _OSA_module( (layers): ModuleList( (0): Sequential( (OSA5_1_0/conv): Conv2d(768, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA5_1_0/norm): FrozenBatchNorm2d(num_features=224, eps=1e-05) (OSA5_1_0/relu): ReLU(inplace=True) ) (1): Sequential( (OSA5_1_1/conv): Conv2d(224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA5_1_1/norm): FrozenBatchNorm2d(num_features=224, eps=1e-05) (OSA5_1_1/relu): ReLU(inplace=True) ) (2): Sequential( (OSA5_1_2/conv): Conv2d(224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (OSA5_1_2/norm): FrozenBatchNorm2d(num_features=224, eps=1e-05) (OSA5_1_2/relu): ReLU(inplace=True) ) ) (concat): Sequential( (OSA5_1_concat/conv): Conv2d(1440, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (OSA5_1_concat/norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) (OSA5_1_concat/relu): ReLU(inplace=True) ) (ese): eSEModule( (avg_pool): AdaptiveAvgPool2d(output_size=1) (fc): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) (hsigmoid): Hsigmoid() ) ) ) ) ) (proposal_generator): FCOS( (iou_loss): IOULoss() (fcos_head): FCOSHead( (cls_tower): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): GroupNorm(32, 128, eps=1e-05, affine=True) (2): ReLU() (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): GroupNorm(32, 128, eps=1e-05, affine=True) (5): ReLU() ) (bbox_tower): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): GroupNorm(32, 128, eps=1e-05, affine=True) (2): ReLU() (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): GroupNorm(32, 128, eps=1e-05, affine=True) (5): ReLU() ) (share_tower): Sequential() (cls_logits): Conv2d(128, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (bbox_pred): Conv2d(128, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (ctrness): Conv2d(128, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (scales): ModuleList( (0): Scale() (1): Scale() (2): Scale() (3): Scale() (4): Scale() ) ) ) (roi_heads): CenterROIHeads( (mask_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True) (1): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True) (2): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True) ) ) (mask_head): SpatialAttentionMaskHead( (mask_fcn1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (mask_fcn2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (spatialAtt): SpatialAttention( (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (sigmoid): Sigmoid() ) (deconv): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2)) (predictor): Conv2d(128, 80, kernel_size=(1, 1), stride=(1, 1)) ) (maskiou_head): MaskIoUHead( (maskiou_fcn1): Conv2d(129, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (maskiou_fcn2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (maskiou_fc1): Linear(in_features=6272, out_features=1024, bias=True) (maskiou_fc2): Linear(in_features=1024, out_features=1024, bias=True) (maskiou): Linear(in_features=1024, out_features=80, bias=True) (pooling): MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=[0, 0], dilation=[1, 1], ceil_mode=False) ) ) ) [12/08 18:47:13 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(580, 600), max_size=1000, sample_style='choice'), RandomFlip()] [12/08 18:47:55 d2.data.datasets.coco]: Loading datasets/coco/annotations/instances_train2017.json takes 41.12 seconds. [12/08 18:47:56 d2.data.datasets.coco]: Loaded 118287 images in COCO format from datasets/coco/annotations/instances_train2017.json [12/08 18:48:20 d2.data.build]: Removed 1021 images with no usable annotations. 117266 images left. [12/08 18:48:33 d2.data.build]: Using training sampler TrainingSampler [12/08 18:48:34 d2.data.common]: Serializing 117266 elements to byte tensors and concatenating them all ... [12/08 18:48:45 d2.data.common]: Serialized dataset takes 451.21 MiB /jmx/detectron2/detectron2/structures/masks.py:345: UserWarning: This overload of nonzero is deprecated: nonzero() Consider using one of the following signatures instead: nonzero(, bool as_tuple) (Triggered internally at /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.) item = item.nonzero().squeeze(1).cpu().numpy().tolist() [12/08 18:48:54 fvcore.common.checkpoint]: Loading checkpoint from /jmx/centermask2/models/vovnet19_ese_detectron2.pth /jmx/detectron2/detectron2/structures/masks.py:345: UserWarning: This overload of nonzero is deprecated: nonzero() Consider using one of the following signatures instead: nonzero(, bool as_tuple) (Triggered internally at /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.) item = item.nonzero().squeeze(1).cpu().numpy().tolist() /jmx/detectron2/detectron2/structures/masks.py:345: UserWarning: This overload of nonzero is deprecated: nonzero() Consider using one of the following signatures instead: nonzero(, bool as_tuple) (Triggered internally at /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.) item = item.nonzero().squeeze(1).cpu().numpy().tolist() [12/08 18:48:54 fvcore.common.checkpoint]: Some model parameters or buffers are not found in the checkpoint: roi_heads.mask_head.mask_fcn2.{bias, weight} proposal_generator.fcos_head.cls_tower.1.{weight, bias} roi_heads.maskiou_head.maskiou.{weight, bias} proposal_generator.fcos_head.ctrness.{weight, bias} roi_heads.maskiou_head.maskiou_fc1.{weight, bias} backbone.fpn_lateral3.{weight, bias} roi_heads.mask_head.spatialAtt.conv.weight backbone.top_block.p6.{bias, weight} roi_heads.maskiou_head.maskiou_fcn1.{weight, bias} proposal_generator.fcos_head.cls_logits.{weight, bias} backbone.top_block.p7.{bias, weight} proposal_generator.fcos_head.bbox_tower.1.{weight, bias} proposal_generator.fcos_head.scales.1.scale proposal_generator.fcos_head.bbox_tower.4.{bias, weight} proposal_generator.fcos_head.bbox_pred.{bias, weight} proposal_generator.fcos_head.scales.2.scale backbone.fpn_lateral5.{bias, weight} roi_heads.maskiou_head.maskiou_fcn2.{bias, weight} backbone.fpn_lateral4.{bias, weight} roi_heads.mask_head.deconv.{weight, bias} roi_heads.mask_head.mask_fcn1.{bias, weight} proposal_generator.fcos_head.scales.3.scale roi_heads.mask_head.predictor.{weight, bias} backbone.fpn_output3.{weight, bias} proposal_generator.fcos_head.cls_tower.3.{bias, weight} proposal_generator.fcos_head.scales.0.scale proposal_generator.fcos_head.bbox_tower.0.{weight, bias} backbone.fpn_output5.{bias, weight} proposal_generator.fcos_head.cls_tower.0.{weight, bias} proposal_generator.fcos_head.cls_tower.4.{bias, weight} roi_heads.maskiou_head.maskiou_fc2.{bias, weight} backbone.fpn_output4.{bias, weight} proposal_generator.fcos_head.bbox_tower.3.{bias, weight} proposal_generator.fcos_head.scales.4.scale [12/08 18:48:54 fvcore.common.checkpoint]: The checkpoint state_dict contains keys that are not used by the model: backbone.bottom_up.stem.stem_1/norm.num_batches_tracked backbone.bottom_up.stem.stem_2/norm.num_batches_tracked backbone.bottom_up.stem.stem_3/norm.num_batches_tracked backbone.bottom_up.stage2.OSA2_1.layers.0.OSA2_1_0/norm.num_batches_tracked backbone.bottom_up.stage2.OSA2_1.layers.1.OSA2_1_1/norm.num_batches_tracked backbone.bottom_up.stage2.OSA2_1.layers.2.OSA2_1_2/norm.num_batches_tracked backbone.bottom_up.stage2.OSA2_1.concat.OSA2_1_concat/norm.num_batches_tracked backbone.bottom_up.stage3.OSA3_1.layers.0.OSA3_1_0/norm.num_batches_tracked backbone.bottom_up.stage3.OSA3_1.layers.1.OSA3_1_1/norm.num_batches_tracked backbone.bottom_up.stage3.OSA3_1.layers.2.OSA3_1_2/norm.num_batches_tracked backbone.bottom_up.stage3.OSA3_1.concat.OSA3_1_concat/norm.num_batches_tracked backbone.bottom_up.stage4.OSA4_1.layers.0.OSA4_1_0/norm.num_batches_tracked backbone.bottom_up.stage4.OSA4_1.layers.1.OSA4_1_1/norm.num_batches_tracked backbone.bottom_up.stage4.OSA4_1.layers.2.OSA4_1_2/norm.num_batches_tracked backbone.bottom_up.stage4.OSA4_1.concat.OSA4_1_concat/norm.num_batches_tracked backbone.bottom_up.stage5.OSA5_1.layers.0.OSA5_1_0/norm.num_batches_tracked backbone.bottom_up.stage5.OSA5_1.layers.1.OSA5_1_1/norm.num_batches_tracked backbone.bottom_up.stage5.OSA5_1.layers.2.OSA5_1_2/norm.num_batches_tracked backbone.bottom_up.stage5.OSA5_1.concat.OSA5_1_concat/norm.num_batches_tracked /jmx/detectron2/detectron2/structures/masks.py:345: UserWarning: This overload of nonzero is deprecated: nonzero() Consider using one of the following signatures instead: nonzero(, bool as_tuple) (Triggered internally at /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.) item = item.nonzero().squeeze(1).cpu().numpy().tolist() [W TensorIterator.cpp:918] Warning: Mixed memory format inputs detected while calling the operator. The operator will output contiguous tensor even if some of the inputs are in channels_last format. (function operator()) [W TensorIterator.cpp:918] Warning: Mixed memory format inputs detected while calling the operator. The operator will output contiguous tensor even if some of the inputs are in channels_last format. (function operator()) [W TensorIterator.cpp:918] Warning: Mixed memory format inputs detected while calling the operator. The operator will output contiguous tensor even if some of the inputs are in channels_last format. (function operator()) [W TensorIterator.cpp:918] Warning: Mixed memory format inputs detected while calling the operator. The operator will output contiguous tensor even if some of the inputs are in channels_last format. (function operator()) /root/anaconda3/envs/detectron2/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) /root/anaconda3/envs/detectron2/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) /root/anaconda3/envs/detectron2/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) /root/anaconda3/envs/detectron2/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) [12/08 18:49:08 d2.utils.events]: eta: 2 days, 12:45:56 iter: 19 total_loss: 3.564 loss_mask: 0.6932 loss_maskiou: 0.06747 loss_fcos_cls: 1.14 loss_fcos_loc: 0.9648 loss_fcos_ctr: 0.6922 time: 0.6086 data_time: 0.2780 lr: 1e-05 max_mem: 2960M [12/08 18:49:08 d2.utils.events]: eta: 2 days, 13:25:44 iter: 19 total_loss: 3.564 loss_mask: 0.6932 loss_maskiou: 0.06747 loss_fcos_cls: 1.14 loss_fcos_loc: 0.9648 loss_fcos_ctr: 0.6922 time: 0.6118 data_time: 0.2780 lr: 1e-05 max_mem: 2960M

haderalim commented 3 years ago

@jiameixia1202 I mean by "the registered coco dataset" you use coco dataset not custom dataset.

You can debug and check the pre_nms_top_n value in ''centermask/modeling/fcos/fcos_outputs/'

lines are:- candidate_inds = box_cls > self.pre_nms_thresh pre_nms_top_n = candidate_inds.view(N, -1).sum(1)

When cfg.SCORE_THRESH_TEST = 0.05 then self.pre_nms_thresh = 0.05. This value may cause of make pre_nms_top_n empty and make zero accuracy.

So, check if pre_nms_top_n is tensor([0, 0] or not.

jiameixia1202 commented 3 years ago

I use the registered coco dataset, and I check the pre nms top_ n is tensor([0, 0] .

It is normal for me to test with the weight provided by you, but when I train myself, the accuracy is 0 (without modifying the network, data and parameters)

jiameixia1202 commented 3 years ago

cfg.SCORE_THRESH_TEST = 0.05 then self.pre_nms_thresh = 0.05 How do I modify these two parameters？

haderalim commented 3 years ago

You can modify it through config file ''configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml' under MODEL and FCOS. If FCOS not exist, add it and modify the value 0.05 of INFERENCE_TH_TRAIN and INFERENCE_TH_TEST.

Like this: MODEL: FCOS: INFERENCE_TH_TRAIN: 0.05 INFERENCE_TH_TEST: 0.05

jiameixia1202 commented 3 years ago

I'm sorry, there may be something wrong with what I just said, My current settings cfg.SCORE THRESH TEST = 0.05 then self.pre nms thresh = 0.05 You said this value may cause of make pre nms top_ n empty and make zero accuracy. I wonder why I can avoid this problem by modifying these two values

Paragjain10 commented 3 years ago

You can modify it through config file ''configs/centermask/centermask_lite_V_19_eSE_FPN_ms_4x.yaml' under MODEL and FCOS. If FCOS not exist, add it and modify the value 0.05 of INFERENCE_TH_TRAIN and INFERENCE_TH_TEST.

Like this: MODEL: FCOS: INFERENCE_TH_TRAIN: 0.05 INFERENCE_TH_TEST: 0.05

can we change the optimizer to Adam?