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[05/21 12:14:51] detectron2 INFO: Rank of current process: 0. World size: 2
[05/21 12:14:53] detectron2 INFO: Environment info:
---------------------- ------------------------------------------------------------------------------------
sys.platform linux
Python 3.8.8 (default, Apr 13 2021, 19:58:26) [GCC 7.3.0]
numpy 1.20.2
detectron2 0.4 @/research/dbash/conda/envs/detec2cu11/lib/python3.8/site-packages/detectron2
Compiler GCC 7.3
CUDA compiler CUDA 11.1
detectron2 arch flags 3.7, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5, 8.0, 8.6
DETECTRON2_ENV_MODULE <not set>
PyTorch 1.8.1 @/research/dbash/conda/envs/detec2cu11/lib/python3.8/site-packages/torch
PyTorch debug build False
GPU available True
GPU 0,1 RTX A6000 (arch=8.6)
CUDA_HOME /usr/local/cuda
Pillow 8.2.0
torchvision 0.9.1 @/research/dbash/conda/envs/detec2cu11/lib/python3.8/site-packages/torchvision
torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
fvcore 0.1.3.post20210317
cv2 4.5.2
---------------------- ------------------------------------------------------------------------------------
PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.2-Product Build 20210312 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.1
- 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_61;-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 8.0.5
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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 -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-variable -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=1.8.1, 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=ON, USE_NNPACK=ON, USE_OPENMP=ON,
[05/21 12:14:53] detectron2 INFO: Command line arguments: Namespace(config_file='configs/exp_18.yaml', dataroot='/scratch/dbash_recycling_data_deblurred', dist_url='tcp://127.0.0.1:50200', eval_only=False, machine_rank=0, num_gpus=2, num_machines=1, opts=['OUTPUT_DIR', '/research/dbash/recycling_experiments/deeplab/exp_18', 'SOLVER.IMS_PER_BATCH', '40', 'DATALOADER.NUM_WORKERS', '8'], resume=True)
[05/21 12:14:53] detectron2 INFO: Contents of args.config_file=configs/exp_18.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"
FREEZE_AT: 2
RESNETS:
DEPTH: 101
NORM: "SyncBN" # FrozenBN, GN, "SyncBN", "BN"
OUT_FEATURES: ["res2", "res5"]
RES5_MULTI_GRID: [1, 2, 4]
STEM_TYPE: "deeplab"
STEM_OUT_CHANNELS: 128
STRIDE_IN_1X1: False
SEM_SEG_HEAD:
NAME: "DeepLabV3PlusHead"
IN_FEATURES: ["res2", "res5"]
PROJECT_FEATURES: ["res2"]
PROJECT_CHANNELS: [48]
NORM: "SyncBN" # FrozenBN, GN, "SyncBN", "BN"
COMMON_STRIDE: 4
NUM_CLASSES: 5
ASPP_DROPOUT: 0.8
IGNORE_VALUE: 255
INPUT:
MIN_SIZE_TRAIN: (512, 768, 1024, 1280, 1536, 1792, 2048)
MIN_SIZE_TRAIN_SAMPLING: "choice"
MIN_SIZE_TEST: 1024
MAX_SIZE_TRAIN: 4096
MAX_SIZE_TEST: 1024
CROP:
ENABLED: True
TYPE: "absolute"
SIZE: (512, 1024)
SINGLE_CATEGORY_MAX_AREA: 1.0
DATALOADER:
NUM_WORKERS: 8
DATASETS:
TRAIN: ("zero-waste-semseg-train",)
TEST: ("zero-waste-semseg-train", "zero-waste-semseg-val", "zero-waste-semseg-test")
TEST:
EVAL_PERIOD: 1000
SOLVER:
MAX_ITER: 10000
BASE_LR: 0.01
LR_SCHEDULER_NAME: "WarmupMultiStepLR" # See detectron2/solver/build.py for LR scheduler options
MOMENTUM: 0.9 # try also 0.93, 0.95
NESTEROV: False
WEIGHT_DECAY: 0.003
GAMMA: 0.1
STEPS: (2000, 4000, 6000, 9000) # The iteration number to decrease learning rate by GAMMA.
CHECKPOINT_PERIOD: 1000 # Save a checkpoint after every this number of iterations
IMS_PER_BATCH: 40 # batch size
[05/21 12:14:53] detectron2 INFO: Running with full config:
CUDNN_BENCHMARK: False
DATALOADER:
ASPECT_RATIO_GROUPING: True
FILTER_EMPTY_ANNOTATIONS: True
NUM_WORKERS: 8
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: ('zero-waste-semseg-train', 'zero-waste-semseg-val', 'zero-waste-semseg-test')
TRAIN: ('zero-waste-semseg-train',)
GLOBAL:
HACK: 1.0
INPUT:
CROP:
ENABLED: True
SINGLE_CATEGORY_MAX_AREA: 1.0
SIZE: [512, 1024]
TYPE: absolute
FORMAT: BGR
MASK_FORMAT: polygon
MAX_SIZE_TEST: 1024
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: 2
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: ['res2', '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: (1.0, 1.0, 1.0, 1.0)
FOCAL_LOSS_ALPHA: 0.25
FOCAL_LOSS_GAMMA: 2.0
IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7']
IOU_LABELS: [0, -1, 1]
IOU_THRESHOLDS: [0.4, 0.5]
NMS_THRESH_TEST: 0.5
NORM:
NUM_CLASSES: 80
NUM_CONVS: 4
PRIOR_PROB: 0.01
SCORE_THRESH_TEST: 0.05
SMOOTH_L1_LOSS_BETA: 0.1
TOPK_CANDIDATES_TEST: 1000
ROI_BOX_CASCADE_HEAD:
BBOX_REG_WEIGHTS: ((10.0, 10.0, 5.0, 5.0), (20.0, 20.0, 10.0, 10.0), (30.0, 30.0, 15.0, 15.0))
IOUS: (0.5, 0.6, 0.7)
ROI_BOX_HEAD:
BBOX_REG_LOSS_TYPE: smooth_l1
BBOX_REG_LOSS_WEIGHT: 1.0
BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
CLS_AGNOSTIC_BBOX_REG: False
CONV_DIM: 256
FC_DIM: 1024
NAME: 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: (1.0, 1.0, 1.0, 1.0)
BOUNDARY_THRESH: -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: 0.8
COMMON_STRIDE: 4
CONVS_DIM: 256
IGNORE_VALUE: 255
IN_FEATURES: ['res2', 'res5']
LOSS_TYPE: hard_pixel_mining
LOSS_WEIGHT: 1.0
NAME: DeepLabV3PlusHead
NORM: SyncBN
NUM_CLASSES: 5
PROJECT_CHANNELS: [48]
PROJECT_FEATURES: ['res2']
USE_DEPTHWISE_SEPARABLE_CONV: False
WEIGHTS: detectron2://DeepLab/R-103.pkl
OUTPUT_DIR: /research/dbash/recycling_experiments/deeplab/exp_18
SEED: -1
SOLVER:
AMP:
ENABLED: False
BASE_LR: 0.01
BIAS_LR_FACTOR: 1.0
CHECKPOINT_PERIOD: 1000
CLIP_GRADIENTS:
CLIP_TYPE: value
CLIP_VALUE: 1.0
ENABLED: False
NORM_TYPE: 2.0
GAMMA: 0.1
IMS_PER_BATCH: 40
LR_SCHEDULER_NAME: WarmupMultiStepLR
MAX_ITER: 10000
MOMENTUM: 0.9
NESTEROV: False
POLY_LR_CONSTANT_ENDING: 0.0
POLY_LR_POWER: 0.9
REFERENCE_WORLD_SIZE: 0
STEPS: (2000, 4000, 6000, 9000)
WARMUP_FACTOR: 0.001
WARMUP_ITERS: 1000
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.003
WEIGHT_DECAY_BIAS: 0.0001
WEIGHT_DECAY_NORM: 0.0
TEST:
AUG:
ENABLED: False
FLIP: True
MAX_SIZE: 4000
MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
DETECTIONS_PER_IMAGE: 100
EVAL_PERIOD: 1000
EXPECTED_RESULTS: []
KEYPOINT_OKS_SIGMAS: []
PRECISE_BN:
ENABLED: False
NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0
[05/21 12:14:53] detectron2 INFO: Full config saved to /research/dbash/recycling_experiments/deeplab/exp_18/config.yaml
[05/21 12:14:53] d2.utils.env INFO: Using a generated random seed 54090609
[05/21 12:14:54] d2.engine.defaults INFO: Model:
SemanticSegmentor(
(backbone): ResNet(
(stem): DeepLabStem(
(conv1): Conv2d(
3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(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, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
)
(res2): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv1): Conv2d(
128, 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): 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): DeepLabV3PlusHead(
(decoder): ModuleDict(
(res2): ModuleDict(
(project_conv): Conv2d(
256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): SyncBatchNorm(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(fuse_conv): Sequential(
(0): Conv2d(
304, 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)
)
(1): 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)
)
)
)
(res5): ModuleDict(
(project_conv): 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)
)
)
(fuse_conv): None
)
)
(predictor): Conv2d(256, 5, kernel_size=(1, 1), stride=(1, 1))
(loss): DeepLabCE(
(criterion): CrossEntropyLoss()
)
)
)
[05/21 12:14:54] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [Resize(shape=(1024, 2048)), ResizeShortestEdge(short_edge_length=(512, 768, 1024, 1280, 1536, 1792, 2048), max_size=4096, sample_style='choice'), RandomRotation(angle=[-90, 90]), RandomBrightness(intensity_min=0.6, intensity_max=1.4), RandomSaturation(intensity_min=0.6, intensity_max=1.4), RandomLighting(scale=0.3), RandomCrop_CategoryAreaConstraint(crop_type='absolute', crop_size=[512, 1024], single_category_max_area=1.0, ignored_category=0.0), RandomFlip()]
[05/21 12:14:55] d2.data.datasets.coco WARNING: Directory /scratch/dbash_recycling_data_deblurred/train/data and /scratch/dbash_recycling_data_deblurred/train/sem_seg has 1245 and 1283 files, respectively.
[05/21 12:14:55] d2.data.datasets.coco WARNING: Will use their intersection of 1245 files.
[05/21 12:14:55] d2.data.datasets.coco INFO: Loaded 1245 images with semantic segmentation from /scratch/dbash_recycling_data_deblurred/train/data
[05/21 12:14:55] d2.data.build INFO: Using training sampler TrainingSampler
[05/21 12:14:55] d2.data.common INFO: Serializing 1245 elements to byte tensors and concatenating them all ...
[05/21 12:14:55] d2.data.common INFO: Serialized dataset takes 0.23 MiB
[05/21 12:14:55] fvcore.common.checkpoint INFO: Loading checkpoint from detectron2://DeepLab/R-103.pkl
[05/21 12:14:59] fvcore.common.checkpoint INFO: Reading a file from 'torchvision'
[05/21 12:14:59] d2.checkpoint.c2_model_loading INFO: 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,) (64,128,1,1) |
| 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,) (256,128,1,1) |
| 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.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.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,3,3,3) |
| stem.conv2.* | stem.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |
| stem.conv3.* | stem.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,64,3,3) |
[05/21 12:14:59] fvcore.common.checkpoint INFO: Some model parameters or buffers are not found in the checkpoint: