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oluwandabira/deeplab.out

Created March 17, 2022 13:39
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Detetectron2 DeepLabV3 output
Raw
deeplab.out
Command Line Args: Namespace(config_file='configs/Cityscapes-SemanticSegmentation/deeplab_v3_R_103_os16_mg124_poly_90k_bs16.yaml', dist_url='tcp://127.0.0.1:51208', eval_only=False, machine_rank=0, num_gpus=1, num_machines=1, opts=[], resume=False)
[03/17 15:22:22 detectron2]: Rank of current process: 0. World size: 1
[03/17 15:22:23 detectron2]: Environment info:
---------------------- -----------------------------------------------------------------------------------------------------
sys.platform linux
Python 3.8.10 (default, Nov 26 2021, 20:14:08) [GCC 9.3.0]
numpy 1.22.3
detectron2 0.6 @/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2
Compiler GCC 9.4
CUDA compiler not available
DETECTRON2_ENV_MODULE <not set>
PyTorch 1.11.0+cu102 @/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch
PyTorch debug build False
GPU available Yes
GPU 0 NVIDIA GeForce RTX 2080 Ti (arch=7.5)
Driver version 465.19.01
CUDA_HOME /usr/local/cuda
Pillow 9.0.1
torchvision 0.12.0+cu102 @/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torchvision
torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5
fvcore 0.1.5.post20220305
iopath 0.1.9
cv2 Not found
---------------------- -----------------------------------------------------------------------------------------------------
PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 10.2
- 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_70,code=sm_70
- CuDNN 7.6.5
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
[03/17 15:22:23 detectron2]: Command line arguments: Namespace(config_file='configs/Cityscapes-SemanticSegmentation/deeplab_v3_R_103_os16_mg124_poly_90k_bs16.yaml', dist_url='tcp://127.0.0.1:51208', eval_only=False, machine_rank=0, num_gpus=1, num_machines=1, opts=[], resume=False)
[03/17 15:22:23 detectron2]: Contents of args.config_file=configs/Cityscapes-SemanticSegmentation/deeplab_v3_R_103_os16_mg124_poly_90k_bs16.yaml:
_BASE_: Base-DeepLabV3-OS16-Semantic.yaml
MODEL:
WEIGHTS: "detectron2://DeepLab/R-103.pkl"
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
BACKBONE:
NAME: "build_resnet_deeplab_backbone"
RESNETS:
DEPTH: 101
NORM: "SyncBN"
RES5_MULTI_GRID: [1, 2, 4]
STEM_TYPE: "deeplab"
STEM_OUT_CHANNELS: 128
STRIDE_IN_1X1: False
SEM_SEG_HEAD:
NAME: "DeepLabV3Head"
NORM: "SyncBN"
INPUT:
FORMAT: "RGB"
[03/17 15:22:23 detectron2]: Running with full config:
CUDNN_BENCHMARK: false
DATALOADER:
ASPECT_RATIO_GROUPING: true
FILTER_EMPTY_ANNOTATIONS: true
NUM_WORKERS: 10
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:
- cityscapes_fine_sem_seg_val
TRAIN:
- cityscapes_fine_sem_seg_train
GLOBAL:
HACK: 1.0
INPUT:
CROP:
ENABLED: true
SINGLE_CATEGORY_MAX_AREA: 1.0
SIZE:
- 512
- 1024
TYPE: absolute
FORMAT: RGB
MASK_FORMAT: polygon
MAX_SIZE_TEST: 2048
MAX_SIZE_TRAIN: 4096
MIN_SIZE_TEST: 1024
MIN_SIZE_TRAIN:
- 512
- 768
- 1024
- 1280
- 1536
- 1792
- 2048
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_deeplab_backbone
DEVICE: cuda
FPN:
FUSE_TYPE: sum
IN_FEATURES: []
NORM: ''
OUT_CHANNELS: 256
KEYPOINT_ON: false
LOAD_PROPOSALS: false
MASK_ON: false
META_ARCHITECTURE: SemanticSegmentor
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: 101
NORM: SyncBN
NUM_GROUPS: 1
OUT_FEATURES:
- res5
RES2_OUT_CHANNELS: 256
RES4_DILATION: 1
RES5_DILATION: 2
RES5_MULTI_GRID:
- 1
- 2
- 4
STEM_OUT_CHANNELS: 128
STEM_TYPE: deeplab
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: FastRCNNConvFCHead
NORM: ''
NUM_CONV: 0
NUM_FC: 2
POOLER_RESOLUTION: 7
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:
- res5
IOU_LABELS:
- 0
- 1
IOU_THRESHOLDS:
- 0.5
NAME: StandardROIHeads
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: 4
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:
- res5
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:
ASPP_CHANNELS: 256
ASPP_DILATIONS:
- 6
- 12
- 18
ASPP_DROPOUT: 0.1
COMMON_STRIDE: 16
CONVS_DIM: 256
IGNORE_VALUE: 255
IN_FEATURES:
- res5
LOSS_TYPE: hard_pixel_mining
LOSS_WEIGHT: 1.0
NAME: DeepLabV3Head
NORM: SyncBN
NUM_CLASSES: 19
PROJECT_CHANNELS:
- 48
PROJECT_FEATURES:
- res2
USE_DEPTHWISE_SEPARABLE_CONV: false
WEIGHTS: detectron2://DeepLab/R-103.pkl
OUTPUT_DIR: ./output
SEED: -1
SOLVER:
AMP:
ENABLED: false
BASE_LR: 0.01
BASE_LR_END: 0.0
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: 16
LR_SCHEDULER_NAME: WarmupPolyLR
MAX_ITER: 90000
MOMENTUM: 0.9
NESTEROV: false
POLY_LR_CONSTANT_ENDING: 0.0
POLY_LR_POWER: 0.9
REFERENCE_WORLD_SIZE: 0
STEPS:
- 60000
- 80000
WARMUP_FACTOR: 0.001
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: 0
EXPECTED_RESULTS: []
KEYPOINT_OKS_SIGMAS: []
PRECISE_BN:
ENABLED: false
NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0
[03/17 15:22:23 detectron2]: Full config saved to ./output/config.yaml
[03/17 15:22:23 d2.utils.env]: Using a generated random seed 26157796
[03/17 15:22:25 d2.engine.defaults]: Model:
SemanticSegmentor(
(backbone): ResNet(
(stem): DeepLabStem(
(conv1): Conv2d(
3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
(norm): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(res2): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d(
128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(res3): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d(
256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
(norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(3): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(res4): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d(
512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(3): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(5): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(6): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(7): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(8): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(9): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(10): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(12): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(13): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(14): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(15): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(16): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(17): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(18): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(19): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(20): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(21): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(22): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(res5): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Conv2d(
1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8), bias=False
(norm): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(sem_seg_head): DeepLabV3Head(
(aspp): ASPP(
(convs): ModuleList(
(0): Conv2d(
2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): Conv2d(
2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(6, 6), dilation=(6, 6), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): Conv2d(
2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(3): Conv2d(
2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(18, 18), dilation=(18, 18), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(4): Sequential(
(0): AvgPool2d(kernel_size=(32, 64), stride=1, padding=0)
(1): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(project): Conv2d(
1280, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(predictor): Conv2d(256, 19, kernel_size=(1, 1), stride=(1, 1))
(loss): DeepLabCE(
(criterion): CrossEntropyLoss()
)
)
)
[03/17 15:22:25 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(512, 768, 1024, 1280, 1536, 1792, 2048), max_size=4096, sample_style='choice'), RandomCrop_CategoryAreaConstraint(crop_type='absolute', crop_size=[512, 1024], single_category_max_area=1.0, ignored_category=255), RandomFlip()]
[03/17 15:22:25 d2.data.datasets.cityscapes]: 18 cities found in '/illukas/home/olalaw/data/cityscapes/leftImg8bit/train/'.
[03/17 15:22:29 d2.data.build]: Using training sampler TrainingSampler
[03/17 15:22:29 d2.data.common]: Serializing 2975 elements to byte tensors and concatenating them all ...
[03/17 15:22:29 d2.data.common]: Serialized dataset takes 0.80 MiB
[03/17 15:22:29 fvcore.common.checkpoint]: [Checkpointer] Loading from detectron2://DeepLab/R-103.pkl ...
[03/17 15:22:29 fvcore.common.checkpoint]: Reading a file from 'torchvision'
[03/17 15:22:29 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.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) () (64,) (64,) (64,) (64,128,1,1) |
| res2.0.conv2.* | res2.0.conv2.{norm.bias,norm.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,128,1,1) |
| res2.1.conv1.* | res2.1.conv1.{norm.bias,norm.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.10.conv1.* | res4.10.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.10.conv2.* | res4.10.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.10.conv3.* | res4.10.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.11.conv1.* | res4.11.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.11.conv2.* | res4.11.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.11.conv3.* | res4.11.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.12.conv1.* | res4.12.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.12.conv2.* | res4.12.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.12.conv3.* | res4.12.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.13.conv1.* | res4.13.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.13.conv2.* | res4.13.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.13.conv3.* | res4.13.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.14.conv1.* | res4.14.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.14.conv2.* | res4.14.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.14.conv3.* | res4.14.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.15.conv1.* | res4.15.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.15.conv2.* | res4.15.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.15.conv3.* | res4.15.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.16.conv1.* | res4.16.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.16.conv2.* | res4.16.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.16.conv3.* | res4.16.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.17.conv1.* | res4.17.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.17.conv2.* | res4.17.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.17.conv3.* | res4.17.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.18.conv1.* | res4.18.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.18.conv2.* | res4.18.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.18.conv3.* | res4.18.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.19.conv1.* | res4.19.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.19.conv2.* | res4.19.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.19.conv3.* | res4.19.conv3.{norm.bias,norm.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.20.conv1.* | res4.20.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.20.conv2.* | res4.20.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.20.conv3.* | res4.20.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.21.conv1.* | res4.21.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.21.conv2.* | res4.21.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.21.conv3.* | res4.21.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.22.conv1.* | res4.22.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.22.conv2.* | res4.22.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.22.conv3.* | res4.22.conv3.{norm.bias,norm.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.6.conv1.* | res4.6.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.6.conv2.* | res4.6.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.6.conv3.* | res4.6.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.7.conv1.* | res4.7.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.7.conv2.* | res4.7.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.7.conv3.* | res4.7.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.8.conv1.* | res4.8.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.8.conv2.* | res4.8.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.8.conv3.* | res4.8.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) () (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.9.conv1.* | res4.9.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,1024,1,1) |
| res4.9.conv2.* | res4.9.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) () (256,) (256,) (256,) (256,256,3,3) |
| res4.9.conv3.* | res4.9.conv3.{norm.bias,norm.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,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.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) () (2048,) (2048,) (2048,) (2048,512,1,1) |
| stem.conv1.* | stem.conv1.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) () (64,) (64,) (64,) (64,3,3,3) |
| stem.conv2.* | stem.conv2.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) () (64,) (64,) (64,) (64,64,3,3) |
| stem.conv3.* | stem.conv3.{norm.bias,norm.num_batches_tracked,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) () (128,) (128,) (128,) (128,64,3,3) |
WARNING [03/17 15:22:29 fvcore.common.checkpoint]: Some model parameters or buffers are not found in the checkpoint:
sem_seg_head.aspp.convs.0.norm.{bias, running_mean, running_var, weight}
sem_seg_head.aspp.convs.0.weight
sem_seg_head.aspp.convs.1.norm.{bias, running_mean, running_var, weight}
sem_seg_head.aspp.convs.1.weight
sem_seg_head.aspp.convs.2.norm.{bias, running_mean, running_var, weight}
sem_seg_head.aspp.convs.2.weight
sem_seg_head.aspp.convs.3.norm.{bias, running_mean, running_var, weight}
sem_seg_head.aspp.convs.3.weight
sem_seg_head.aspp.convs.4.1.{bias, weight}
sem_seg_head.aspp.project.norm.{bias, running_mean, running_var, weight}
sem_seg_head.aspp.project.weight
sem_seg_head.predictor.{bias, weight}
WARNING [03/17 15:22:29 fvcore.common.checkpoint]: The checkpoint state_dict contains keys that are not used by the model:
stem.fc.{bias, weight}
[03/17 15:22:29 d2.engine.train_loop]: Starting training from iteration 0
ERROR [03/17 15:22:30 d2.engine.train_loop]: Exception during training:
Traceback (most recent call last):
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/engine/train_loop.py", line 149, in train
self.run_step()
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/engine/defaults.py", line 494, in run_step
self._trainer.run_step()
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/engine/train_loop.py", line 273, in run_step
loss_dict = self.model(data)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/modeling/meta_arch/semantic_seg.py", line 104, in forward
features = self.backbone(images.tensor)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/modeling/backbone/resnet.py", line 445, in forward
x = self.stem(x)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/projects/DeepLab/deeplab/resnet.py", line 60, in forward
x = self.conv1(x)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/layers/wrappers.py", line 110, in forward
x = self.norm(x)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/batchnorm.py", line 731, in forward
world_size = torch.distributed.get_world_size(process_group)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 867, in get_world_size
return _get_group_size(group)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 325, in _get_group_size
default_pg = _get_default_group()
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 429, in _get_default_group
raise RuntimeError(
RuntimeError: Default process group has not been initialized, please make sure to call init_process_group.
[03/17 15:22:30 d2.engine.hooks]: Total training time: 0:00:00 (0:00:00 on hooks)
[03/17 15:22:30 d2.utils.events]:  iter: 0 lr: N/A max_mem: 833M
Traceback (most recent call last):
File "train_net.py", line 129, in <module>
launch(
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/engine/launch.py", line 82, in launch
main_func(*args)
File "train_net.py", line 123, in main
return trainer.train()
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/engine/defaults.py", line 484, in train
super().train(self.start_iter, self.max_iter)
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/engine/train_loop.py", line 149, in train
self.run_step()
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/engine/defaults.py", line 494, in run_step
self._trainer.run_step()
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/engine/train_loop.py", line 273, in run_step
loss_dict = self.model(data)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/modeling/meta_arch/semantic_seg.py", line 104, in forward
features = self.backbone(images.tensor)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/modeling/backbone/resnet.py", line 445, in forward
x = self.stem(x)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/projects/DeepLab/deeplab/resnet.py", line 60, in forward
x = self.conv1(x)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/detectron2/detectron2/layers/wrappers.py", line 110, in forward
x = self.norm(x)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/nn/modules/batchnorm.py", line 731, in forward
world_size = torch.distributed.get_world_size(process_group)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 867, in get_world_size
return _get_group_size(group)
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 325, in _get_group_size
default_pg = _get_default_group()
File "/illukas/home/olalaw/repos/suim-detectron/.venv/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 429, in _get_default_group
raise RuntimeError(
RuntimeError: Default process group has not been initialized, please make sure to call init_process_group.
Command exited with non-zero status 1
Command being timed: "python train_net.py --config-file configs/Cityscapes-SemanticSegmentation/deeplab_v3_R_103_os16_mg124_poly_90k_bs16.yaml"
User time (seconds): 16.14
System time (seconds): 5.06
Percent of CPU this job got: 214%
Elapsed (wall clock) time (h:mm:ss or m:ss): 0:09.86
Average shared text size (kbytes): 0
Average unshared data size (kbytes): 0
Average stack size (kbytes): 0
Average total size (kbytes): 0
Maximum resident set size (kbytes): 3032480
Average resident set size (kbytes): 0
Major (requiring I/O) page faults: 0
Minor (reclaiming a frame) page faults: 1110179
Voluntary context switches: 13077
Involuntary context switches: 2773
Swaps: 0
File system inputs: 141008
File system outputs: 296
Socket messages sent: 0
Socket messages received: 0
Signals delivered: 0
Page size (bytes): 4096
Exit status: 1
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