ISTR
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hujiecpp
RuntimeError: CUDA out of memory. Tried to allocate 672.00 MiB (GPU 0; 15.78 GiB total capacity; 13.42 GiB already allocated; 50.75 MiB free; 14.41 GiB reserved in total by PyTorch)
dist_url='tcp://127.0.0.1:49152', eval_only=False, machine_rank=0, num_gpus=1, num_machines=1, opts=[], resume=False) [08/25 11:15:41 detectron2]: Contents of args.config_file=projects/ISTR/configs/ISTR-AE-R50-3x.yaml: BASE: "Base-ISTR.yaml" MODEL: WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" RESNETS: DEPTH: 50 STRIDE_IN_1X1: False ISTR: NUM_PROPOSALS: 300 NUM_CLASSES: 5 MASK_ENCODING_METHOD: "AE" PATH_COMPONENTS: "/content/drive/MyDrive/imenselmi/ISTR_TRAIN/ISTR/projects/AE/checkpoints/AE_112_256.t7" DATASETS: TRAIN: ("train",) TEST: ("val",) SOLVER: STEPS: (210000, 250000) MAX_ITER: 270000 INPUT: FORMAT: "RGB"
[08/25 11:15:41 detectron2]: Running with full config: CUDNN_BENCHMARK: true DATALOADER: ASPECT_RATIO_GROUPING: true FILTER_EMPTY_ANNOTATIONS: 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:
- val TRAIN:
- train
GLOBAL:
HACK: 1.0
INPUT:
CROP:
ENABLED: true
SIZE:
- 384
- 600 TYPE: absolute_range FORMAT: RGB MASK_FORMAT: polygon MAX_SIZE_TEST: 1333 MAX_SIZE_TRAIN: 1333 MIN_SIZE_TEST: 800 MIN_SIZE_TRAIN:
- 416
- 448
- 480
- 512
- 544
- 576
- 608
- 640
- 672
- 704
- 736
- 768
- 800
- 832
- 864
- 896
- 928
- 960
- 992
- 1024
- 1056
- 1088
MIN_SIZE_TRAIN_SAMPLING: choice
RANDOM_FLIP: horizontal
LSJ_AUG: false
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: -1 NAME: build_resnet_fpn_backbone DEVICE: cuda FPN: FUSE_TYPE: sum IN_FEATURES:
- res2
- res3
- res4
- res5 NORM: '' OUT_CHANNELS: 256 ISTR: ALPHA: 0.25 CLASS_WEIGHT: 2.0 DEEP_SUPERVISION: true DIM_DYNAMIC: 64 DIM_FEEDFORWARD: 2048 DROPOUT: 0.0 FEAT_WEIGHT: 1.0 GAMMA: 2.0 GIOU_WEIGHT: 2.0 HIDDEN_DIM: 256 IOU_LABELS:
- 0
- 1 IOU_THRESHOLDS:
- 0.5 L1_WEIGHT: 5.0 MASK_ENCODING_METHOD: AE MASK_FEAT_DIM: 256 MASK_SIZE: 112 MASK_WEIGHT: 5.0 NHEADS: 8 NO_OBJECT_WEIGHT: 0.1 NUM_CLASSES: 5 NUM_CLS: 3 NUM_DYNAMIC: 2 NUM_HEADS: 6 NUM_MASK: 3 NUM_PROPOSALS: 300 NUM_REG: 3 PATH_COMPONENTS: /content/drive/MyDrive/imenselmi/ISTR_TRAIN/ISTR/projects/AE/checkpoints/AE_112_256.t7 PRIOR_PROB: 0.01 KEYPOINT_ON: false LOAD_PROPOSALS: false MASK_ON: true META_ARCHITECTURE: ISTR 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:
-
- 123.675
- 116.28
- 103.53 PIXEL_STD:
- 58.395
- 57.12
- 57.375
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:
- res2
- res3
- res4
- res5 RES2_OUT_CHANNELS: 256 RES5_DILATION: 1 STEM_OUT_CHANNELS: 64 STRIDE_IN_1X1: false WIDTH_PER_GROUP: 64 RETINANET: BBOX_REG_LOSS_TYPE: smooth_l1 BBOX_REG_WEIGHTS: &id001
- 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: 7 POOLER_SAMPLING_RATIO: 2 POOLER_TYPE: ROIAlignV2 SMOOTH_L1_BETA: 0.0 TRAIN_ON_PRED_BOXES: false ROI_HEADS: BATCH_SIZE_PER_IMAGE: 512 IN_FEATURES:
- p2
- p3
- p4
- p5 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: *id001 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: 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 SWINT: APE: false DEPTHS:
- 2
- 2
- 6
- 2 DROP_PATH_RATE: 0.2 EMBED_DIM: 96 MLP_RATIO: 4 NUM_HEADS:
- 3
- 6
- 12
- 24 OUT_FEATURES:
- stage2
- stage3
- stage4
- stage5 WINDOW_SIZE: 7 WEIGHTS: detectron2://ImageNetPretrained/torchvision/R-50.pkl OUTPUT_DIR: ./output SEED: 2333333 SOLVER: AMP: ENABLED: false BACKBONE_MULTIPLIER: 1.0 BASE_LR: 2.5e-05 BIAS_LR_FACTOR: 1.0 CHECKPOINT_PERIOD: 5000 CLIP_GRADIENTS: CLIP_TYPE: full_model CLIP_VALUE: 1.0 ENABLED: true NORM_TYPE: 2.0 GAMMA: 0.1 IMS_PER_BATCH: 16 LR_SCHEDULER_NAME: WarmupMultiStepLR MAX_ITER: 270000 MOMENTUM: 0.9 NESTEROV: false OPTIMIZER: ADAMW REFERENCE_WORLD_SIZE: 0 STEPS:
- 210000
- 250000
WARMUP_FACTOR: 0.01
WARMUP_ITERS: 1000
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: null
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: 7330 EXPECTED_RESULTS: [] KEYPOINT_OKS_SIGMAS: [] PRECISE_BN: ENABLED: false NUM_ITER: 200 VERSION: 2 VIS_PERIOD: 0
[08/25 11:15:41 detectron2]: Full config saved to ./output/config.yaml [08/25 11:15:47 d2.engine.defaults]: Model: ISTR( (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, 64, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv2): Conv2d( 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv3): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv2): Conv2d( 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv3): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv2): Conv2d( 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv3): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) ) (res3): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv1): Conv2d( 256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (3): BottleneckBlock( (conv1): Conv2d( 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) ) (res4): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv1): Conv2d( 512, 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=(2, 2), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 1024, 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), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 1024, 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), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (3): BottleneckBlock( (conv1): Conv2d( 1024, 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), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (4): BottleneckBlock( (conv1): Conv2d( 1024, 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), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (5): BottleneckBlock( (conv1): Conv2d( 1024, 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), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) ) (res5): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv1): Conv2d( 1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2): Conv2d( 512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2): Conv2d( 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2): Conv2d( 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) ) ) ) (pos_embeddings): Embedding(300, 256) (init_proposal_boxes): Embedding(300, 4) (IFE): ImgFeatExtractor() (mask_E): Encoder( (encoder): Sequential( (0): Conv2d(1, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=True) (3): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=True) (6): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ELU(alpha=True) (9): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (10): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (11): ELU(alpha=True) (12): Conv2d(128, 256, kernel_size=(7, 7), stride=(1, 1)) (13): View() ) ) (mask_D): Decoder( (decoder): Sequential( (0): View() (1): ConvTranspose2d(256, 128, kernel_size=(7, 7), stride=(1, 1)) (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): ELU(alpha=1.0, inplace=True) (4): up_conv( (up): Sequential( (0): Upsample(scale_factor=2.0, mode=bilinear) (1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): ELU(alpha=1.0, inplace=True) ) ) (5): up_conv( (up): Sequential( (0): Upsample(scale_factor=2.0, mode=bilinear) (1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): ELU(alpha=1.0, inplace=True) ) ) (6): up_conv( (up): Sequential( (0): Upsample(scale_factor=2.0, mode=bilinear) (1): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): ELU(alpha=1.0, inplace=True) ) ) (7): up_conv( (up): Sequential( (0): Upsample(scale_factor=2.0, mode=bilinear) (1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): ELU(alpha=1.0, inplace=True) ) ) (8): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1)) (9): Sigmoid() (10): View() ) ) (head): DynamicHead( (box_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=2, aligned=True) (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=2, aligned=True) (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=2, aligned=True) (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=2, aligned=True) ) ) (mask_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(28, 28), spatial_scale=0.25, sampling_ratio=2, aligned=True) (1): ROIAlign(output_size=(28, 28), spatial_scale=0.125, sampling_ratio=2, aligned=True) (2): ROIAlign(output_size=(28, 28), spatial_scale=0.0625, sampling_ratio=2, aligned=True) (3): ROIAlign(output_size=(28, 28), spatial_scale=0.03125, sampling_ratio=2, aligned=True) ) ) (head_series): ModuleList( (0): RCNNHead( (self_attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (inst_interact): DynamicConv( (dynamic_layer): Linear(in_features=256, out_features=32768, bias=True) (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (activation): ELU(alpha=1.0, inplace=True) (out_layer): Linear(in_features=12544, out_features=256, bias=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) (linear1): Linear(in_features=256, out_features=2048, bias=True) (dropout): Dropout(p=0.0, inplace=False) (linear2): Linear(in_features=2048, out_features=256, bias=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (dropout1): Dropout(p=0.0, inplace=False) (dropout2): Dropout(p=0.0, inplace=False) (dropout3): Dropout(p=0.0, inplace=False) (activation): ELU(alpha=1.0, inplace=True) (cls_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (reg_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (mask_module): Sequential( (0): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=True) (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=True) (6): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1)) ) (ret_roi_layer_1): conv_block( (conv): Sequential( (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (ret_roi_layer_2): conv_block( (conv): Sequential( (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (class_logits): Linear(in_features=256, out_features=5, bias=True) (bboxes_delta): Linear(in_features=256, out_features=4, bias=True) ) (1): RCNNHead( (self_attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (inst_interact): DynamicConv( (dynamic_layer): Linear(in_features=256, out_features=32768, bias=True) (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (activation): ELU(alpha=1.0, inplace=True) (out_layer): Linear(in_features=12544, out_features=256, bias=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) (linear1): Linear(in_features=256, out_features=2048, bias=True) (dropout): Dropout(p=0.0, inplace=False) (linear2): Linear(in_features=2048, out_features=256, bias=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (dropout1): Dropout(p=0.0, inplace=False) (dropout2): Dropout(p=0.0, inplace=False) (dropout3): Dropout(p=0.0, inplace=False) (activation): ELU(alpha=1.0, inplace=True) (cls_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (reg_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (mask_module): Sequential( (0): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=True) (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=True) (6): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1)) ) (ret_roi_layer_1): conv_block( (conv): Sequential( (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (ret_roi_layer_2): conv_block( (conv): Sequential( (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (class_logits): Linear(in_features=256, out_features=5, bias=True) (bboxes_delta): Linear(in_features=256, out_features=4, bias=True) ) (2): RCNNHead( (self_attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (inst_interact): DynamicConv( (dynamic_layer): Linear(in_features=256, out_features=32768, bias=True) (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (activation): ELU(alpha=1.0, inplace=True) (out_layer): Linear(in_features=12544, out_features=256, bias=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) (linear1): Linear(in_features=256, out_features=2048, bias=True) (dropout): Dropout(p=0.0, inplace=False) (linear2): Linear(in_features=2048, out_features=256, bias=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (dropout1): Dropout(p=0.0, inplace=False) (dropout2): Dropout(p=0.0, inplace=False) (dropout3): Dropout(p=0.0, inplace=False) (activation): ELU(alpha=1.0, inplace=True) (cls_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (reg_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (mask_module): Sequential( (0): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=True) (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=True) (6): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1)) ) (ret_roi_layer_1): conv_block( (conv): Sequential( (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (ret_roi_layer_2): conv_block( (conv): Sequential( (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (class_logits): Linear(in_features=256, out_features=5, bias=True) (bboxes_delta): Linear(in_features=256, out_features=4, bias=True) ) (3): RCNNHead( (self_attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (inst_interact): DynamicConv( (dynamic_layer): Linear(in_features=256, out_features=32768, bias=True) (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (activation): ELU(alpha=1.0, inplace=True) (out_layer): Linear(in_features=12544, out_features=256, bias=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) (linear1): Linear(in_features=256, out_features=2048, bias=True) (dropout): Dropout(p=0.0, inplace=False) (linear2): Linear(in_features=2048, out_features=256, bias=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (dropout1): Dropout(p=0.0, inplace=False) (dropout2): Dropout(p=0.0, inplace=False) (dropout3): Dropout(p=0.0, inplace=False) (activation): ELU(alpha=1.0, inplace=True) (cls_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (reg_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (mask_module): Sequential( (0): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=True) (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=True) (6): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1)) ) (ret_roi_layer_1): conv_block( (conv): Sequential( (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (ret_roi_layer_2): conv_block( (conv): Sequential( (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (class_logits): Linear(in_features=256, out_features=5, bias=True) (bboxes_delta): Linear(in_features=256, out_features=4, bias=True) ) (4): RCNNHead( (self_attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (inst_interact): DynamicConv( (dynamic_layer): Linear(in_features=256, out_features=32768, bias=True) (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (activation): ELU(alpha=1.0, inplace=True) (out_layer): Linear(in_features=12544, out_features=256, bias=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) (linear1): Linear(in_features=256, out_features=2048, bias=True) (dropout): Dropout(p=0.0, inplace=False) (linear2): Linear(in_features=2048, out_features=256, bias=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (dropout1): Dropout(p=0.0, inplace=False) (dropout2): Dropout(p=0.0, inplace=False) (dropout3): Dropout(p=0.0, inplace=False) (activation): ELU(alpha=1.0, inplace=True) (cls_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (reg_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (mask_module): Sequential( (0): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=True) (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=True) (6): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1)) ) (ret_roi_layer_1): conv_block( (conv): Sequential( (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (ret_roi_layer_2): conv_block( (conv): Sequential( (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (class_logits): Linear(in_features=256, out_features=5, bias=True) (bboxes_delta): Linear(in_features=256, out_features=4, bias=True) ) (5): RCNNHead( (self_attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (inst_interact): DynamicConv( (dynamic_layer): Linear(in_features=256, out_features=32768, bias=True) (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (activation): ELU(alpha=1.0, inplace=True) (out_layer): Linear(in_features=12544, out_features=256, bias=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) ) (linear1): Linear(in_features=256, out_features=2048, bias=True) (dropout): Dropout(p=0.0, inplace=False) (linear2): Linear(in_features=2048, out_features=256, bias=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (dropout1): Dropout(p=0.0, inplace=False) (dropout2): Dropout(p=0.0, inplace=False) (dropout3): Dropout(p=0.0, inplace=False) (activation): ELU(alpha=1.0, inplace=True) (cls_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (reg_module): ModuleList( (0): Linear(in_features=256, out_features=256, bias=False) (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (2): ELU(alpha=1.0, inplace=True) (3): Linear(in_features=256, out_features=256, bias=False) (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (5): ELU(alpha=1.0, inplace=True) (6): Linear(in_features=256, out_features=256, bias=False) (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (8): ELU(alpha=1.0, inplace=True) ) (mask_module): Sequential( (0): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=True) (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=True) (6): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1)) ) (ret_roi_layer_1): conv_block( (conv): Sequential( (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (ret_roi_layer_2): conv_block( (conv): Sequential( (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ELU(alpha=1.0, inplace=True) (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ELU(alpha=1.0, inplace=True) ) ) (class_logits): Linear(in_features=256, out_features=5, bias=True) (bboxes_delta): Linear(in_features=256, out_features=4, bias=True) ) ) ) (criterion): SetCriterion( (matcher): HungarianMatcher() ) ) [08/25 11:15:53 d2.data.datasets.coco]: Loading /content/drive/MyDrive/imenselmi/ISTR_TRAIN/data/result/train.json takes 6.61 seconds. [08/25 11:15:54 d2.data.datasets.coco]: Loaded 43480 images in COCO format from /content/drive/MyDrive/imenselmi/ISTR_TRAIN/data/result/train.json [08/25 11:15:57 d2.data.build]: Removed 0 images with no usable annotations. 43480 images left. [08/25 11:15:59 d2.data.build]: Distribution of instances among all 5 categories:
| category | #instances | category | #instances | category | #instances |
|---|---|---|---|---|---|
| short_sleev.. | 18359 | long_sleeve.. | 14566 | long_sleeve.. | 10492 |
| shorts | 12123 | trousers | 18227 | ||
| total | 73767 |
pos_embeddings.weight
WARNING [08/25 11:16:04 fvcore.common.checkpoint]: The checkpoint state_dict contains keys that are not used by the model:
stem.fc.{bias, weight}
[08/25 11:16:04 d2.engine.train_loop]: Starting training from iteration 0
/usr/local/lib/python3.7/dist-packages/fvcore/transforms/transform.py:724: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the geoms property to access the constituent parts of a multi-part geometry.
for poly in cropped:
/usr/local/lib/python3.7/dist-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /pytorch/aten/src/ATen/native/BinaryOps.cpp:467.)
return torch.floor_divide(self, other)
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
ERROR [08/25 11:16:05 d2.engine.train_loop]: Exception during training:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/detectron2/engine/train_loop.py", line 149, in train
self.run_step()
File "/usr/local/lib/python3.7/dist-packages/detectron2/engine/defaults.py", line 494, in run_step
self._trainer.run_step()
File "/usr/local/lib/python3.7/dist-packages/detectron2/engine/train_loop.py", line 273, in run_step
loss_dict = self.model(data)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/content/drive/.shortcut-targets-by-id/190HFmYfsGdKfNWeUiqnpTgh7X3m3GFmF/ISTR_TRAIN/ISTR/projects/ISTR/istr/inseg.py", line 162, in forward
src = self.backbone(images.tensor)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/detectron2/modeling/backbone/fpn.py", line 126, in forward
bottom_up_features = self.bottom_up(x)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/detectron2/modeling/backbone/resnet.py", line 449, in forward
x = stage(x)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py", line 139, in forward
input = module(input)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/detectron2/modeling/backbone/resnet.py", line 201, in forward
out = self.conv3(out)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/detectron2/layers/wrappers.py", line 110, in forward
x = self.norm(x)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/detectron2/layers/batch_norm.py", line 53, in forward
return x * scale.to(out_dtype) + bias.to(out_dtype)
RuntimeError: CUDA out of memory. Tried to allocate 672.00 MiB (GPU 0; 15.78 GiB total capacity; 13.42 GiB already allocated; 50.75 MiB free; 14.41 GiB reserved in total by PyTorch)
[08/25 11:16:05 d2.engine.hooks]: Total training time: 0:00:01 (0:00:00 on hooks)
[08/25 11:16:05 d2.utils.events]: iter: 0 lr: N/A max_mem: 14075M
Traceback (most recent call last):
File "projects/ISTR/train_net.py", line 136, in
Traceback (most recent call last):
File "projects/ISTR/train_net.py", line 136, in
how can i fix this issue i trained the code in colab and locally and still the same problem always "CUDA out of memory."
