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)
aymennturki opened this issue · 3 comments
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
args=(args,),
File "/usr/local/lib/python3.7/dist-packages/detectron2/engine/launch.py", line 82, in launch
main_func(*args)
File "projects/ISTR/train_net.py", line 124, in main
return trainer.train()
File "/usr/local/lib/python3.7/dist-packages/detectron2/engine/defaults.py", line 484, in train
super().train(self.start_iter, self.max_iter)
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)
Traceback (most recent call last):
File "projects/ISTR/train_net.py", line 136, in
args=(args,),
File "/usr/local/lib/python3.7/dist-packages/detectron2/engine/launch.py", line 82, in launch
main_func(*args)
File "projects/ISTR/train_net.py", line 124, in main
return trainer.train()
File "/usr/local/lib/python3.7/dist-packages/detectron2/engine/defaults.py", line 484, in train
super().train(self.start_iter, self.max_iter)
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)
how can i fix this issue i trained the code in colab and locally and still the same problem always "CUDA out of memory."
how do you solve it？，my gpu is rtx 2080ti（memory 11G）.