在COCOTrain2017数据集训练，Val2017测试的AP是27.00，为什么这么低呢? #103

1032697379 · 2022-12-08T03:13:36Z

您好，您的工作我很喜欢，做的非常好。所以我想重新运行程序，复现结果。我只有一张GPU，batchsize=8, learning rate =0.00005, 使用sparse_inst_r50_giam.yaml配置，其余都没有更改，为什么结果这么低呢（您给的结果是33.4AP，我重新训练运行结果却只有27.00AP）。很期待您的回复，谢谢。

wondervictor · 2023-01-03T04:27:17Z

您好，非常感谢对SparseInst的关注这个主要原因是因为batch size太小了，我们默认的batch size=64，如果降低到batch size=8的话，性能会大幅下降，目前的话，我们有FP16的支持，如果您使用的是3090这样显存较大的GPU(>=24G)，您可以采用单卡batch size=32，应该可以获得和batch size=64相当的性能，或者使用较低显存的GPU(<=16G) 可以尝试batch size=16。

cici-999 · 2023-02-19T12:56:06Z

@wondervictor 您好，我是使用的3090的GPU，采用单卡batch size=16，其他地方都没有更改，得到的结果只有30.39AP，请问怎样可以获得跟你论文中一样的指标（33.4AP）呢？期待您的回复，谢谢。

wondervictor · 2023-02-20T02:46:03Z

@wondervictor 您好，我是使用的3090的GPU，采用单卡batch size=16，其他地方都没有更改，得到的结果只有30.39AP，请问怎样可以获得跟你论文中一样的指标（33.4AP）呢？期待您的回复，谢谢。

我们默认采用batch=64，如果batch size=16，性能下降会比较明显，如果使用3090 GPU，可以尝试打开FP16采用batch size=32训练。或者保持batch size=16然后延长迭代并相应调整学习率。

cici-999 · 2023-02-20T09:07:44Z

@wondervictor 您好，我是使用的3090的GPU，采用单卡batch size=16，其他地方都没有更改，得到的结果只有30.39AP，请问怎样可以获得跟你论文中一样的指标（33.4AP）呢？期待您的回复，谢谢。

我们默认采用batch=64，如果batch size=16，性能下降会比较明显，如果使用3090 GPU，可以尝试打开FP16采用batch size=32训练。或者保持batch size=16然后延长迭代并相应调整学习率。

您好，我按照您说的，尝试打开FP16采用batch size=32训练，会出现以下警告，请问这个要怎么解决呀？期待您的回复，谢谢。Command Line Args: Namespace(config_file='configs/sparse_inst_r50_giam_fp16.yaml', dist_url='tcp://127.0.0.1:49153', eval_only=False, machine_rank=0, num_gpus=1, num_machines=1, opts=['MODEL.WEIGHTS', 'pretrained_models/R-50.pkl', 'SOLVER.AMP.ENABLED', 'True'], resume=False)
[02/20 12:07:06 detectron2]: Rank of current process: 0. World size: 1
[02/20 12:07:07 detectron2]: Environment info:

sys.platform win32
Python 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 05:59:00) [MSC v.1929 64 bit (AMD64)]
numpy 1.23.3
detectron2 0.6 @d:\Users\Admin\miniconda3\envs\detectron\lib\site-packages\detectron2\detectron2
Compiler MSVC 192930137
CUDA compiler CUDA 11.3
detectron2 arch flags D:\Users\Admin\miniconda3\envs\detectron\lib\site-packages\detectron2\detectron2_C.cp38-win_amd64.pyd; cannot find cuobjdump
DETECTRON2_ENV_MODULE
PyTorch 1.10.0 @d:\Users\Admin\miniconda3\envs\detectron\lib\site-packages\torch
PyTorch debug build False
GPU available Yes
GPU 0 NVIDIA GeForce RTX 3090 (arch=8.6)
Driver version 516.40
CUDA_HOME D:\CUDA\v11.3\bin\nvcc.exe
D:\CUDA\v10.1\bin\nvcc.exe
D:\CUDA\v9.0\bin\nvcc.exe
D:\CUDA\v10.2 - invalid!
Pillow 9.2.0
torchvision 0.11.0 @d:\Users\Admin\miniconda3\envs\detectron\lib\site-packages\torchvision
torchvision arch flags D:\Users\Admin\miniconda3\envs\detectron\lib\site-packages\torchvision_C.pyd; cannot find cuobjdump
fvcore 0.1.5.post20220512
iopath 0.1.9
cv2 4.6.0

PyTorch built with:

C++ Version: 199711
MSVC 192829337
Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
OpenMP 2019
LAPACK is enabled (usually provided by MKL)
CPU capability usage: AVX512
CUDA Runtime 11.3
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_6
1;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
CuDNN 8.2
Magma 2.5.4
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=C:/cb/pytorch_1000000000000/work/tmp_bin/sccache-cl.exe, CXX_FL
AGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/cb/pytorch_1000000000000/work/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI
-DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VER
SION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON,

[02/20 12:07:07 detectron2]: Command line arguments: Namespace(config_file='configs/sparse_inst_r50_giam_fp16.yaml', dist_url='tcp://127.0.0.1:49153', eval_only=False, machine_rank=0, num_gpus=1, num_machines=1, opts=['MODEL.WEIGHTS', 'pretrained_models/R-50.pkl', 'SOLVER.AMP.ENABLED', 'True'], resume=False)
[02/20 12:07:07 detectron2]: Contents of args.config_file=configs/sparse_inst_r50_giam_fp16.yaml:
BASE: "Base-SparseInst.yaml"
MODEL:
WEIGHTS: "pretrained_models/R-50.pkl"
SOLVER:
AMP:
ENABLED: True
OUTPUT_DIR: "output/sparse_inst_r50_giam_fp16"
[02/20 12:07:07 detectron2]: Running with full config:
CUDNN_BENCHMARK: false
DATALOADER:
ASPECT_RATIO_GROUPING: true
FILTER_EMPTY_ANNOTATIONS: true
NUM_WORKERS: 6
REPEAT_THRESHOLD: 0.0
SAMPLER_TRAIN: TrainingSampler
DATASETS:
PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
PROPOSAL_FILES_TEST: []
PROPOSAL_FILES_TRAIN: []
TEST:

coco_2017_val
TRAIN:
coco_2017_train
GLOBAL:
HACK: 1.0
INPUT:
CROP:
ENABLED: false
SIZE:
- 0.9
- 0.9
  TYPE: relative_range
  FORMAT: RGB
  MASK_FORMAT: bitmask
  MAX_SIZE_TEST: 853
  MAX_SIZE_TRAIN: 853
  MIN_SIZE_TEST: 640
  MIN_SIZE_TRAIN:
416
448
480
512
544
576
608
640
MIN_SIZE_TRAIN_SAMPLING: choice
RANDOM_FLIP: horizontal
MODEL:
ANCHOR_GENERATOR:
ANGLES:
- - -90
  - 0
  - 90
    ASPECT_RATIOS:
- - 0.5
  - 1.0
  - 2.0
    NAME: DefaultAnchorGenerator
    OFFSET: 0.0
    SIZES:
- - 32
  - 64
  - 128
  - 256
  - 512
    BACKBONE:
    FREEZE_AT: 0
    NAME: build_resnet_backbone
    CSPNET:
    NAME: darknet53
    NORM: ''
    OUT_FEATURES:
- csp1
- csp2
- csp3
- csp4
  DEVICE: cuda
  FPN:
  FUSE_TYPE: sum
  IN_FEATURES: []
  NORM: ''
  OUT_CHANNELS: 256
  KEYPOINT_ON: false
  LOAD_PROPOSALS: false
  MASK_ON: true
  META_ARCHITECTURE: SparseInst
  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
PVT:
LINEAR: false
NAME: b1
OUT_FEATURES:
- p2
- p3
- p4
  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:
- 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: &id002
- 1.0
- 1.0
- 1.0
- 1.0
  FOCAL_LOSS_ALPHA: 0.25
  FOCAL_LOSS_GAMMA: 2.0
  IN_FEATURES:
- p3
- p4
- p5
- p6
- p7
  IOU_LABELS:
- 0
- -1
- 1
  IOU_THRESHOLDS:
- 0.4
- 0.5
  NMS_THRESH_TEST: 0.5
  NORM: ''
  NUM_CLASSES: 80
  NUM_CONVS: 4
  PRIOR_PROB: 0.01
  SCORE_THRESH_TEST: 0.05
  SMOOTH_L1_LOSS_BETA: 0.1
  TOPK_CANDIDATES_TEST: 1000
  ROI_BOX_CASCADE_HEAD:
  BBOX_REG_WEIGHTS:
- &id001
  - 10.0
  - 10.0
  - 5.0
  - 5.0
- - 20.0
  - 20.0
  - 10.0
  - 10.0
- - 30.0
  - 30.0
  - 15.0
  - 15.0
    IOUS:
- 0.5
- 0.6
- 0.7
  ROI_BOX_HEAD:
  BBOX_REG_LOSS_TYPE: smooth_l1
  BBOX_REG_LOSS_WEIGHT: 1.0
  BBOX_REG_WEIGHTS: *id001
  CLS_AGNOSTIC_BBOX_REG: false
  CONV_DIM: 256
  FC_DIM: 1024
  NAME: ''
  NORM: ''
  NUM_CONV: 0
  NUM_FC: 0
  POOLER_RESOLUTION: 14
  POOLER_SAMPLING_RATIO: 0
  POOLER_TYPE: ROIAlignV2
  SMOOTH_L1_BETA: 0.0
  TRAIN_ON_PRED_BOXES: false
  ROI_HEADS:
  BATCH_SIZE_PER_IMAGE: 512
  IN_FEATURES:
- res4
  IOU_LABELS:
- 0
- 1
  IOU_THRESHOLDS:
- 0.5
  NAME: Res5ROIHeads
  NMS_THRESH_TEST: 0.5
  NUM_CLASSES: 80
  POSITIVE_FRACTION: 0.25
  PROPOSAL_APPEND_GT: true
  SCORE_THRESH_TEST: 0.05
  ROI_KEYPOINT_HEAD:
  CONV_DIMS:
- 512
- 512
- 512
- 512
- 512
- 512
- 512
- 512
  LOSS_WEIGHT: 1.0
  MIN_KEYPOINTS_PER_IMAGE: 1
  NAME: KRCNNConvDeconvUpsampleHead
  NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true
  NUM_KEYPOINTS: 17
  POOLER_RESOLUTION: 14
  POOLER_SAMPLING_RATIO: 0
  POOLER_TYPE: ROIAlignV2
  ROI_MASK_HEAD:
  CLS_AGNOSTIC_MASK: false
  CONV_DIM: 256
  NAME: MaskRCNNConvUpsampleHead
  NORM: ''
  NUM_CONV: 0
  POOLER_RESOLUTION: 14
  POOLER_SAMPLING_RATIO: 0
  POOLER_TYPE: ROIAlignV2
  RPN:
  BATCH_SIZE_PER_IMAGE: 256
  BBOX_REG_LOSS_TYPE: smooth_l1
  BBOX_REG_LOSS_WEIGHT: 1.0
  BBOX_REG_WEIGHTS: *id002
  BOUNDARY_THRESH: -1
  CONV_DIMS:
- -1
  HEAD_NAME: StandardRPNHead
  IN_FEATURES:
- res4
  IOU_LABELS:
- 0
- -1
- 1
  IOU_THRESHOLDS:
- 0.3
- 0.7
  LOSS_WEIGHT: 1.0
  NMS_THRESH: 0.7
  POSITIVE_FRACTION: 0.5
  POST_NMS_TOPK_TEST: 1000
  POST_NMS_TOPK_TRAIN: 2000
  PRE_NMS_TOPK_TEST: 6000
  PRE_NMS_TOPK_TRAIN: 12000
  SMOOTH_L1_BETA: 0.0
  SEM_SEG_HEAD:
  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
  SPARSE_INST:
  CLS_THRESHOLD: 0.005
  DATASET_MAPPER: SparseInstDatasetMapper
  DECODER:
  GROUPS: 4
  INST:
  CONVS: 6
  DIM: 256
  KERNEL_DIM: 128
  MASK:
  CONVS: 6
  DIM: 256
  NAME: GroupIAMDecoder
  NUM_CLASSES: 80
  NUM_MASKS: 100
  OUTPUT_IAM: false
  SCALE_FACTOR: 2.0
  ENCODER:
  IN_FEATURES:
  - res3
  - res4
  - res5
    NAME: InstanceContextEncoder
    NORM: ''
    NUM_CHANNELS: 256
    LOSS:
    CLASS_WEIGHT: 2.0
    ITEMS:
  - labels
  - masks
    MASK_DICE_WEIGHT: 2.0
    MASK_PIXEL_WEIGHT: 5.0
    NAME: SparseInstCriterion
    OBJECTNESS_WEIGHT: 1.0
    MASK_THRESHOLD: 0.45
    MATCHER:
    ALPHA: 0.8
    BETA: 0.2
    NAME: SparseInstMatcher
    MAX_DETECTIONS: 100
    WEIGHTS: pretrained_models/R-50.pkl
    OUTPUT_DIR: output/sparse_inst_r50_giam_fp16
    SEED: -1
    SOLVER:
    AMP:
    ENABLED: true
    AMSGRAD: false
    BACKBONE_MULTIPLIER: 1.0
    BASE_LR: 5.0e-05
    BIAS_LR_FACTOR: 1.0
    CHECKPOINT_PERIOD: 5000
    CLIP_GRADIENTS:
    CLIP_TYPE: value
    CLIP_VALUE: 1.0
    ENABLED: false
    NORM_TYPE: 2.0
    GAMMA: 0.1
    IMS_PER_BATCH: 32
    LR_SCHEDULER_NAME: WarmupMultiStepLR
    MAX_ITER: 270000
    MOMENTUM: 0.9
    NESTEROV: false
    OPTIMIZER: ADAMW
    REFERENCE_WORLD_SIZE: 0
    STEPS:
210000
250000
WARMUP_FACTOR: 0.001
WARMUP_ITERS: 1000
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.05
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

[02/20 12:07:07 detectron2]: Full config saved to output/sparse_inst_r50_giam_fp16\config.yaml
[02/20 12:07:07 d2.utils.env]: Using a generated random seed 7587500
[02/20 12:07:08 d2.engine.defaults]: Model:
SparseInst(
(backbone): 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)
)
)
)
)
(encoder): InstanceContextEncoder(
(fpn_laterals): ModuleList(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(fpn_outputs): ModuleList(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(ppm): PyramidPoolingModule(
(stages): ModuleList(
(0): Sequential(
(0): AdaptiveAvgPool2d(output_size=(1, 1))
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
)
(1): Sequential(
(0): AdaptiveAvgPool2d(output_size=(2, 2))
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
)
(2): Sequential(
(0): AdaptiveAvgPool2d(output_size=(3, 3))
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
)
(3): Sequential(
(0): AdaptiveAvgPool2d(output_size=(6, 6))
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
)
)
(bottleneck): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(fusion): Conv2d(768, 256, kernel_size=(1, 1), stride=(1, 1))
)
(decoder): GroupIAMDecoder(
(inst_branch): GroupInstanceBranch(
(inst_convs): Sequential(
(0): Conv2d(258, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace=True)
(4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): ReLU(inplace=True)
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
)
(iam_conv): Conv2d(256, 400, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=4)
(fc): Linear(in_features=1024, out_features=1024, bias=True)
(cls_score): Linear(in_features=1024, out_features=80, bias=True)
(mask_kernel): Linear(in_features=1024, out_features=128, bias=True)
(objectness): Linear(in_features=1024, out_features=1, bias=True)
)
(mask_branch): MaskBranch(
(mask_convs): Sequential(
(0): Conv2d(258, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace=True)
(4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): ReLU(inplace=True)
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
)
(projection): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
)
)
(criterion): SparseInstCriterion(
(matcher): SparseInstMatcher()
)
)
[02/20 12:07:08 sparseinst.dataset_mapper]: [DatasetMapper] Augmentations used in training: [RandomFlip(), ResizeShortestEdge(short_edge_length=(416, 448, 480, 512, 544, 576, 608, 640), max_size=853, sample_style='choice')]
[02/20 12:07:26 d2.data.datasets.coco]: Loading datasets\coco/annotations/instances_train2017.json takes 18.47 seconds.
[02/20 12:07:27 d2.data.datasets.coco]: Loaded 118287 images in COCO format from datasets\coco/annotations/instances_train2017.json
[02/20 12:07:35 d2.data.build]: Removed 1021 images with no usable annotations. 117266 images left.
[02/20 12:07:40 d2.data.build]: Distribution of instances among all 80 categories:

category	#instances	category	#instances	category	#instances
person	257253	bicycle	7056	car	43533
motorcycle	8654	airplane	5129	bus	6061
train	4570	truck	9970	boat	10576
traffic light	12842	fire hydrant	1865	stop sign	1983
parking meter	1283	bench	9820	bird	10542
cat	4766	dog	5500	horse	6567
sheep	9223	cow	8014	elephant	5484
bear	1294	zebra	5269	giraffe	5128
backpack	8714	umbrella	11265	handbag	12342
tie	6448	suitcase	6112	frisbee	2681
skis	6623	snowboard	2681	sports ball	6299
kite	8802	baseball bat	3273	baseball gl..	3747
skateboard	5536	surfboard	6095	tennis racket	4807
bottle	24070	wine glass	7839	cup	20574
fork	5474	knife	7760	spoon	6159
bowl	14323	banana	9195	apple	5776
sandwich	4356	orange	6302	broccoli	7261
carrot	7758	hot dog	2884	pizza	5807
donut	7005	cake	6296	chair	38073
couch	5779	potted plant	8631	bed	4192
dining table	15695	toilet	4149	tv	5803
laptop	4960	mouse	2261	remote	5700
keyboard	2854	cell phone	6422	microwave	1672
oven	3334	toaster	225	sink	5609
refrigerator	2634	book	24077	clock	6320
vase	6577	scissors	1464	teddy bear	4729
hair drier	198	toothbrush	1945
total	849949
[02/20 12:07:40 d2.data.build]: Using training sampler TrainingSampler
[02/20 12:07:40 d2.data.common]: Serializing 117266 elements to byte tensors and concatenating them all ...
[02/20 12:07:43 d2.data.common]: Serialized dataset takes 451.21 MiB
[02/20 12:07:46 fvcore.common.checkpoint]: [Checkpointer] Loading from pretrained_models/R-50.pkl ...
[02/20 12:07:46 fvcore.common.checkpoint]: Reading a file from 'torchvision'
[02/20 12:07:46 d2.checkpoint.c2_model_loading]: Following weights matched with submodule backbone:
Names in Model	Names in Checkpoint	Shapes
:------------------	:----------------------------------------------------------------------------------	:------------------------------------------------
res2.0.conv1.*	res2.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(64,) (64,) (64,) (64,) (64,64,1,1)
res2.0.conv2.*	res2.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(64,) (64,) (64,) (64,) (64,64,3,3)
res2.0.conv3.*	res2.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,64,1,1)
res2.0.shortcut.*	res2.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,64,1,1)
res2.1.conv1.*	res2.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(64,) (64,) (64,) (64,) (64,256,1,1)
res2.1.conv2.*	res2.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(64,) (64,) (64,) (64,) (64,64,3,3)
res2.1.conv3.*	res2.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,64,1,1)
res2.2.conv1.*	res2.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(64,) (64,) (64,) (64,) (64,256,1,1)
res2.2.conv2.*	res2.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(64,) (64,) (64,) (64,) (64,64,3,3)
res2.2.conv3.*	res2.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,64,1,1)
res3.0.conv1.*	res3.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(128,) (128,) (128,) (128,) (128,256,1,1)
res3.0.conv2.*	res3.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(128,) (128,) (128,) (128,) (128,128,3,3)
res3.0.conv3.*	res3.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,128,1,1)
res3.0.shortcut.*	res3.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,256,1,1)
res3.1.conv1.*	res3.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(128,) (128,) (128,) (128,) (128,512,1,1)
res3.1.conv2.*	res3.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(128,) (128,) (128,) (128,) (128,128,3,3)
res3.1.conv3.*	res3.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,128,1,1)
res3.2.conv1.*	res3.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(128,) (128,) (128,) (128,) (128,512,1,1)
res3.2.conv2.*	res3.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(128,) (128,) (128,) (128,) (128,128,3,3)
res3.2.conv3.*	res3.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,128,1,1)
res3.3.conv1.*	res3.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(128,) (128,) (128,) (128,) (128,512,1,1)
res3.3.conv2.*	res3.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(128,) (128,) (128,) (128,) (128,128,3,3)
res3.3.conv3.*	res3.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,128,1,1)
res4.0.conv1.*	res4.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,512,1,1)
res4.0.conv2.*	res4.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,256,3,3)
res4.0.conv3.*	res4.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(1024,) (1024,) (1024,) (1024,) (1024,256,1,1)
res4.0.shortcut.*	res4.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(1024,) (1024,) (1024,) (1024,) (1024,512,1,1)
res4.1.conv1.*	res4.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,1024,1,1)
res4.1.conv2.*	res4.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,256,3,3)
res4.1.conv3.*	res4.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(1024,) (1024,) (1024,) (1024,) (1024,256,1,1)
res4.2.conv1.*	res4.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,1024,1,1)
res4.2.conv2.*	res4.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,256,3,3)
res4.2.conv3.*	res4.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(1024,) (1024,) (1024,) (1024,) (1024,256,1,1)
res4.3.conv1.*	res4.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,1024,1,1)
res4.3.conv2.*	res4.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,256,3,3)
res4.3.conv3.*	res4.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(1024,) (1024,) (1024,) (1024,) (1024,256,1,1)
res4.4.conv1.*	res4.4.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,1024,1,1)
res4.4.conv2.*	res4.4.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,256,3,3)
res4.4.conv3.*	res4.4.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(1024,) (1024,) (1024,) (1024,) (1024,256,1,1)
res4.5.conv1.*	res4.5.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,1024,1,1)
res4.5.conv2.*	res4.5.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(256,) (256,) (256,) (256,) (256,256,3,3)
res4.5.conv3.*	res4.5.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(1024,) (1024,) (1024,) (1024,) (1024,256,1,1)
res5.0.conv1.*	res5.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,1024,1,1)
res5.0.conv2.*	res5.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,512,3,3)
res5.0.conv3.*	res5.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(2048,) (2048,) (2048,) (2048,) (2048,512,1,1)
res5.0.shortcut.*	res5.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(2048,) (2048,) (2048,) (2048,) (2048,1024,1,1)
res5.1.conv1.*	res5.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,2048,1,1)
res5.1.conv2.*	res5.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,512,3,3)
res5.1.conv3.*	res5.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(2048,) (2048,) (2048,) (2048,) (2048,512,1,1)
res5.2.conv1.*	res5.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,2048,1,1)
res5.2.conv2.*	res5.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(512,) (512,) (512,) (512,) (512,512,3,3)
res5.2.conv3.*	res5.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(2048,) (2048,) (2048,) (2048,) (2048,512,1,1)
stem.conv1.*	stem.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}	(64,) (64,) (64,) (64,) (64,3,7,7)
WARNING [02/20 12:07:46 fvcore.common.checkpoint]: Some model parameters or buffers are not found in the checkpoint:
decoder.inst_branch.cls_score.{bias, weight}
decoder.inst_branch.fc.{bias, weight}
decoder.inst_branch.iam_conv.{bias, weight}
decoder.inst_branch.inst_convs.0.{bias, weight}
decoder.inst_branch.inst_convs.10.{bias, weight}
decoder.inst_branch.inst_convs.2.{bias, weight}
decoder.inst_branch.inst_convs.4.{bias, weight}
decoder.inst_branch.inst_convs.6.{bias, weight}
decoder.inst_branch.inst_convs.8.{bias, weight}
decoder.inst_branch.mask_kernel.{bias, weight}
decoder.inst_branch.objectness.{bias, weight}
decoder.mask_branch.mask_convs.0.{bias, weight}
decoder.mask_branch.mask_convs.10.{bias, weight}
decoder.mask_branch.mask_convs.2.{bias, weight}
decoder.mask_branch.mask_convs.4.{bias, weight}
decoder.mask_branch.mask_convs.6.{bias, weight}
decoder.mask_branch.mask_convs.8.{bias, weight}
decoder.mask_branch.projection.{bias, weight}
encoder.fpn_laterals.0.{bias, weight}
encoder.fpn_laterals.1.{bias, weight}
encoder.fpn_laterals.2.{bias, weight}
encoder.fpn_outputs.0.{bias, weight}
encoder.fpn_outputs.1.{bias, weight}
encoder.fpn_outputs.2.{bias, weight}
encoder.fusion.{bias, weight}
encoder.ppm.bottleneck.{bias, weight}
encoder.ppm.stages.0.1.{bias, weight}
encoder.ppm.stages.1.1.{bias, weight}
encoder.ppm.stages.2.1.{bias, weight}
encoder.ppm.stages.3.1.{bias, weight}
WARNING [02/20 12:07:46 fvcore.common.checkpoint]: The checkpoint state_dict contains keys that are not used by the model:
stem.fc.{bias, weight}
[02/20 12:07:46 d2.engine.train_loop]: Starting training from iteration 0
D:\Users\Admin\miniconda3\envs\detectron\lib\site-packages\detectron2\detectron2\structures\image_list.py:88: UserWarning: floordiv is deprecated, and its behavior wil
l change 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').
max_size = (max_size + (stride - 1)) // stride * stride
D:\Users\Admin\miniconda3\envs\detectron\lib\site-packages\torch\functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ..\aten\src\ATen\native\TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
D:\Users\Admin\miniconda3\envs\detectron\lib\site-packages\torch\optim\lr_scheduler.py:129: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`.
In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "

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在COCOTrain2017数据集训练，Val2017测试的AP是27.00，为什么这么低呢? #103

在COCOTrain2017数据集训练，Val2017测试的AP是27.00，为什么这么低呢? #103

1032697379 commented Dec 8, 2022

wondervictor commented Jan 3, 2023

cici-999 commented Feb 19, 2023

wondervictor commented Feb 20, 2023

cici-999 commented Feb 20, 2023

在COCOTrain2017数据集训练，Val2017测试的AP是27.00，为什么这么低呢? #103

在COCOTrain2017数据集训练，Val2017测试的AP是27.00，为什么这么低呢? #103

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1032697379 commented Dec 8, 2022

wondervictor commented Jan 3, 2023

cici-999 commented Feb 19, 2023

wondervictor commented Feb 20, 2023

cici-999 commented Feb 20, 2023