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log-pr473.txt
2019-02-26 17:02:56,932 maskrcnn_benchmark INFO: Using 2 GPUs
2019-02-26 17:02:56,932 maskrcnn_benchmark INFO: Namespace(config_file='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml', distributed=True, local_rank=0, opts=[], skip_test=False)
2019-02-26 17:02:56,932 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2019-02-26 17:02:58,730 maskrcnn_benchmark INFO:
PyTorch version: 1.0.1.post2
Is debug build: No
CUDA used to build PyTorch: 10.0.130
OS: Ubuntu 16.04.5 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
CMake version: version 3.5.1
Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce RTX 2080
GPU 1: GeForce RTX 2080
Nvidia driver version: 410.72
cuDNN version: Could not collect
Versions of relevant libraries:
[pip] Could not collect
[conda] pytorch 1.0.1 py3.7_cuda10.0.130_cudnn7.4.2_2 pytorch
Pillow (5.4.1)
2019-02-26 17:02:58,730 maskrcnn_benchmark INFO: Loaded configuration file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
2019-02-26 17:02:58,730 maskrcnn_benchmark INFO:
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
BACKBONE:
CONV_BODY: "R-50-FPN"
OUT_CHANNELS: 256
RPN:
USE_FPN: True
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
PRE_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TEST: 1000
ROI_HEADS:
USE_FPN: True
ROI_BOX_HEAD:
POOLER_RESOLUTION: 7
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POOLER_SAMPLING_RATIO: 2
FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
PREDICTOR: "FPNPredictor"
ROI_MASK_HEAD:
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
PREDICTOR: "MaskRCNNC4Predictor"
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
MASK_ON: True
DATASETS:
TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
TEST: ("coco_2014_minival",)
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.02
WEIGHT_DECAY: 0.0001
STEPS: (60000, 80000)
MAX_ITER: 90000
OUTPUT_DIR:
runs/COCO_2014
2019-02-26 17:02:58,731 maskrcnn_benchmark INFO: Running with config:
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 4
SIZE_DIVISIBILITY: 32
DATASETS:
TEST: ('coco_2014_minival',)
TRAIN: ('coco_2014_train', 'coco_2014_valminusminival')
INPUT:
MAX_SIZE_TEST: 1333
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MIN_SIZE_TRAIN: (800,)
PIXEL_MEAN: [102.9801, 115.9465, 122.7717]
PIXEL_STD: [1.0, 1.0, 1.0]
TO_BGR255: True
MODEL:
BACKBONE:
CONV_BODY: R-50-FPN
FREEZE_CONV_BODY_AT: 2
OUT_CHANNELS: 256
USE_GN: False
CLS_AGNOSTIC_BBOX_REG: False
DEVICE: cuda
FPN:
USE_GN: False
USE_RELU: False
GROUP_NORM:
DIM_PER_GP: -1
EPSILON: 1e-05
NUM_GROUPS: 32
KEYPOINT_ON: False
MASK_ON: True
META_ARCHITECTURE: GeneralizedRCNN
RESNETS:
NUM_GROUPS: 1
RES2_OUT_CHANNELS: 256
RES5_DILATION: 1
STEM_FUNC: StemWithFixedBatchNorm
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: True
TRANS_FUNC: BottleneckWithFixedBatchNorm
WIDTH_PER_GROUP: 64
RETINANET:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDES: (8, 16, 32, 64, 128)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BBOX_REG_BETA: 0.11
BBOX_REG_WEIGHT: 4.0
BG_IOU_THRESHOLD: 0.4
FG_IOU_THRESHOLD: 0.5
INFERENCE_TH: 0.05
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.4
NUM_CLASSES: 81
NUM_CONVS: 4
OCTAVE: 2.0
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
SCALES_PER_OCTAVE: 3
STRADDLE_THRESH: 0
USE_C5: True
RETINANET_ON: False
ROI_BOX_HEAD:
CONV_HEAD_DIM: 256
DILATION: 1
FEATURE_EXTRACTOR: FPN2MLPFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 81
NUM_STACKED_CONVS: 4
POOLER_RESOLUTION: 7
POOLER_SAMPLING_RATIO: 2
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
PREDICTOR: FPNPredictor
USE_GN: False
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 512
BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
BG_IOU_THRESHOLD: 0.5
DETECTIONS_PER_IMG: 100
FG_IOU_THRESHOLD: 0.5
NMS: 0.5
POSITIVE_FRACTION: 0.25
SCORE_THRESH: 0.05
USE_FPN: True
ROI_KEYPOINT_HEAD:
CONV_LAYERS: (512, 512, 512, 512, 512, 512, 512, 512)
FEATURE_EXTRACTOR: KeypointRCNNFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 17
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
PREDICTOR: KeypointRCNNPredictor
RESOLUTION: 14
SHARE_BOX_FEATURE_EXTRACTOR: True
ROI_MASK_HEAD:
CONV_LAYERS: (256, 256, 256, 256)
DILATION: 1
FEATURE_EXTRACTOR: MaskRCNNFPNFeatureExtractor
MLP_HEAD_DIM: 1024
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POSTPROCESS_MASKS: False
POSTPROCESS_MASKS_THRESHOLD: 0.5
PREDICTOR: MaskRCNNC4Predictor
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
USE_GN: False
RPN:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BATCH_SIZE_PER_IMAGE: 256
BG_IOU_THRESHOLD: 0.3
FG_IOU_THRESHOLD: 0.7
FPN_POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TRAIN: 2000
MIN_SIZE: 0
NMS_THRESH: 0.7
POSITIVE_FRACTION: 0.5
POST_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
PRE_NMS_TOP_N_TRAIN: 2000
RPN_HEAD: SingleConvRPNHead
STRADDLE_THRESH: 0
USE_FPN: True
RPN_ONLY: False
WEIGHT: catalog://ImageNetPretrained/MSRA/R-50
OUTPUT_DIR: runs/COCO_2014
PATHS_CATALOG: /home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/config/paths_catalog.py
SOLVER:
BASE_LR: 0.02
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 2500
GAMMA: 0.1
IMS_PER_BATCH: 16
MAX_ITER: 90000
MOMENTUM: 0.9
STEPS: (60000, 80000)
WARMUP_FACTOR: 0.3333333333333333
WARMUP_ITERS: 500
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
TEST:
DETECTIONS_PER_IMG: 100
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 8
2019-02-26 17:02:59,406 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50
2019-02-26 17:02:59,407 maskrcnn_benchmark.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
2019-02-26 17:02:59,423 maskrcnn_benchmark.utils.checkpoint INFO: url https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl cached in /home/csbotos/.torch/models/R-50.pkl
2019-02-26 17:02:59,477 maskrcnn_benchmark.utils.c2_model_loading INFO: Remapping C2 weights
2019-02-26 17:02:59,478 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: conv1_b mapped name: conv1.bias
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2019-02-26 17:02:59,500 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.0.weight loaded from layer4.0.downsample.0.weight of shape (2048, 1024, 1, 1)
2019-02-26 17:02:59,500 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.bias loaded from layer4.0.downsample.1.bias of shape (2048,)
2019-02-26 17:02:59,500 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.weight loaded from layer4.0.downsample.1.weight of shape (2048,)
2019-02-26 17:02:59,500 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.bias loaded from layer4.1.bn1.bias of shape (512,)
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2019-02-26 17:02:59,500 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.bias loaded from layer4.1.bn2.bias of shape (512,)
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2019-02-26 17:02:59,500 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.conv2.weight loaded from layer4.1.conv2.weight of shape (512, 512, 3, 3)
2019-02-26 17:02:59,500 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.conv3.weight loaded from layer4.1.conv3.weight of shape (2048, 512, 1, 1)
2019-02-26 17:02:59,500 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.bias loaded from layer4.2.bn1.bias of shape (512,)
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2019-02-26 17:02:59,501 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,)
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2019-02-26 17:02:59,527 maskrcnn_benchmark.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14
2019-02-26 17:03:12,577 maskrcnn_benchmark.trainer INFO: Start training
2019-02-26 17:04:33,061 maskrcnn_benchmark INFO: Using 2 GPUs
2019-02-26 17:04:33,061 maskrcnn_benchmark INFO: Namespace(config_file='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml', distributed=True, local_rank=0, opts=[], skip_test=False)
2019-02-26 17:04:33,061 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2019-02-26 17:04:34,930 maskrcnn_benchmark INFO:
PyTorch version: 1.0.1.post2
Is debug build: No
CUDA used to build PyTorch: 10.0.130
OS: Ubuntu 16.04.5 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
CMake version: version 3.5.1
Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce RTX 2080
GPU 1: GeForce RTX 2080
Nvidia driver version: 410.72
cuDNN version: Could not collect
Versions of relevant libraries:
[pip] Could not collect
[conda] pytorch 1.0.1 py3.7_cuda10.0.130_cudnn7.4.2_2 pytorch
Pillow (5.4.1)
2019-02-26 17:04:34,930 maskrcnn_benchmark INFO: Loaded configuration file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
2019-02-26 17:04:34,930 maskrcnn_benchmark INFO:
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
BACKBONE:
CONV_BODY: "R-50-FPN"
OUT_CHANNELS: 256
RPN:
USE_FPN: True
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
PRE_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TEST: 1000
ROI_HEADS:
USE_FPN: True
ROI_BOX_HEAD:
POOLER_RESOLUTION: 7
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POOLER_SAMPLING_RATIO: 2
FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
PREDICTOR: "FPNPredictor"
ROI_MASK_HEAD:
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
PREDICTOR: "MaskRCNNC4Predictor"
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
MASK_ON: True
DATASETS:
TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
TEST: ("coco_2014_minival",)
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.02
WEIGHT_DECAY: 0.0001
STEPS: (60000, 80000)
MAX_ITER: 90000
IMS_PER_BATCH: 8
TEST:
IMS_PER_BATCH: 4
OUTPUT_DIR:
runs/COCO_2014
2019-02-26 17:04:34,930 maskrcnn_benchmark INFO: Running with config:
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 4
SIZE_DIVISIBILITY: 32
DATASETS:
TEST: ('coco_2014_minival',)
TRAIN: ('coco_2014_train', 'coco_2014_valminusminival')
INPUT:
MAX_SIZE_TEST: 1333
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MIN_SIZE_TRAIN: (800,)
PIXEL_MEAN: [102.9801, 115.9465, 122.7717]
PIXEL_STD: [1.0, 1.0, 1.0]
TO_BGR255: True
MODEL:
BACKBONE:
CONV_BODY: R-50-FPN
FREEZE_CONV_BODY_AT: 2
OUT_CHANNELS: 256
USE_GN: False
CLS_AGNOSTIC_BBOX_REG: False
DEVICE: cuda
FPN:
USE_GN: False
USE_RELU: False
GROUP_NORM:
DIM_PER_GP: -1
EPSILON: 1e-05
NUM_GROUPS: 32
KEYPOINT_ON: False
MASK_ON: True
META_ARCHITECTURE: GeneralizedRCNN
RESNETS:
NUM_GROUPS: 1
RES2_OUT_CHANNELS: 256
RES5_DILATION: 1
STEM_FUNC: StemWithFixedBatchNorm
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: True
TRANS_FUNC: BottleneckWithFixedBatchNorm
WIDTH_PER_GROUP: 64
RETINANET:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDES: (8, 16, 32, 64, 128)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BBOX_REG_BETA: 0.11
BBOX_REG_WEIGHT: 4.0
BG_IOU_THRESHOLD: 0.4
FG_IOU_THRESHOLD: 0.5
INFERENCE_TH: 0.05
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.4
NUM_CLASSES: 81
NUM_CONVS: 4
OCTAVE: 2.0
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
SCALES_PER_OCTAVE: 3
STRADDLE_THRESH: 0
USE_C5: True
RETINANET_ON: False
ROI_BOX_HEAD:
CONV_HEAD_DIM: 256
DILATION: 1
FEATURE_EXTRACTOR: FPN2MLPFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 81
NUM_STACKED_CONVS: 4
POOLER_RESOLUTION: 7
POOLER_SAMPLING_RATIO: 2
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
PREDICTOR: FPNPredictor
USE_GN: False
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 512
BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
BG_IOU_THRESHOLD: 0.5
DETECTIONS_PER_IMG: 100
FG_IOU_THRESHOLD: 0.5
NMS: 0.5
POSITIVE_FRACTION: 0.25
SCORE_THRESH: 0.05
USE_FPN: True
ROI_KEYPOINT_HEAD:
CONV_LAYERS: (512, 512, 512, 512, 512, 512, 512, 512)
FEATURE_EXTRACTOR: KeypointRCNNFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 17
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
PREDICTOR: KeypointRCNNPredictor
RESOLUTION: 14
SHARE_BOX_FEATURE_EXTRACTOR: True
ROI_MASK_HEAD:
CONV_LAYERS: (256, 256, 256, 256)
DILATION: 1
FEATURE_EXTRACTOR: MaskRCNNFPNFeatureExtractor
MLP_HEAD_DIM: 1024
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POSTPROCESS_MASKS: False
POSTPROCESS_MASKS_THRESHOLD: 0.5
PREDICTOR: MaskRCNNC4Predictor
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
USE_GN: False
RPN:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BATCH_SIZE_PER_IMAGE: 256
BG_IOU_THRESHOLD: 0.3
FG_IOU_THRESHOLD: 0.7
FPN_POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TRAIN: 2000
MIN_SIZE: 0
NMS_THRESH: 0.7
POSITIVE_FRACTION: 0.5
POST_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
PRE_NMS_TOP_N_TRAIN: 2000
RPN_HEAD: SingleConvRPNHead
STRADDLE_THRESH: 0
USE_FPN: True
RPN_ONLY: False
WEIGHT: catalog://ImageNetPretrained/MSRA/R-50
OUTPUT_DIR: runs/COCO_2014
PATHS_CATALOG: /home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/config/paths_catalog.py
SOLVER:
BASE_LR: 0.02
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 2500
GAMMA: 0.1
IMS_PER_BATCH: 8
MAX_ITER: 90000
MOMENTUM: 0.9
STEPS: (60000, 80000)
WARMUP_FACTOR: 0.3333333333333333
WARMUP_ITERS: 500
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
TEST:
DETECTIONS_PER_IMG: 100
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 4
2019-02-26 17:04:35,462 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50
2019-02-26 17:04:35,463 maskrcnn_benchmark.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
2019-02-26 17:04:35,480 maskrcnn_benchmark.utils.checkpoint INFO: url https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl cached in /home/csbotos/.torch/models/R-50.pkl
2019-02-26 17:04:35,530 maskrcnn_benchmark.utils.c2_model_loading INFO: Remapping C2 weights
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2019-02-26 17:04:35,555 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.bn1.bias loaded from layer3.4.bn1.bias of shape (256,)
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2019-02-26 17:04:35,555 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.bn3.bias loaded from layer3.4.bn3.bias of shape (1024,)
2019-02-26 17:04:35,555 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.bn3.weight loaded from layer3.4.bn3.weight of shape (1024,)
2019-02-26 17:04:35,555 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.conv1.weight loaded from layer3.4.conv1.weight of shape (256, 1024, 1, 1)
2019-02-26 17:04:35,555 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.conv2.weight loaded from layer3.4.conv2.weight of shape (256, 256, 3, 3)
2019-02-26 17:04:35,555 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.conv3.weight loaded from layer3.4.conv3.weight of shape (1024, 256, 1, 1)
2019-02-26 17:04:35,555 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn1.bias loaded from layer3.5.bn1.bias of shape (256,)
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2019-02-26 17:04:35,556 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn2.bias loaded from layer3.5.bn2.bias of shape (256,)
2019-02-26 17:04:35,556 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn2.weight loaded from layer3.5.bn2.weight of shape (256,)
2019-02-26 17:04:35,556 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn3.bias loaded from layer3.5.bn3.bias of shape (1024,)
2019-02-26 17:04:35,556 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn3.weight loaded from layer3.5.bn3.weight of shape (1024,)
2019-02-26 17:04:35,556 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.conv1.weight loaded from layer3.5.conv1.weight of shape (256, 1024, 1, 1)
2019-02-26 17:04:35,556 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.conv2.weight loaded from layer3.5.conv2.weight of shape (256, 256, 3, 3)
2019-02-26 17:04:35,556 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.conv3.weight loaded from layer3.5.conv3.weight of shape (1024, 256, 1, 1)
2019-02-26 17:04:35,556 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn1.bias loaded from layer4.0.bn1.bias of shape (512,)
2019-02-26 17:04:35,556 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn1.weight loaded from layer4.0.bn1.weight of shape (512,)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn2.bias loaded from layer4.0.bn2.bias of shape (512,)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn2.weight loaded from layer4.0.bn2.weight of shape (512,)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn3.bias loaded from layer4.0.bn3.bias of shape (2048,)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn3.weight loaded from layer4.0.bn3.weight of shape (2048,)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.conv1.weight loaded from layer4.0.conv1.weight of shape (512, 1024, 1, 1)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.conv2.weight loaded from layer4.0.conv2.weight of shape (512, 512, 3, 3)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.conv3.weight loaded from layer4.0.conv3.weight of shape (2048, 512, 1, 1)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.0.weight loaded from layer4.0.downsample.0.weight of shape (2048, 1024, 1, 1)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.bias loaded from layer4.0.downsample.1.bias of shape (2048,)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.weight loaded from layer4.0.downsample.1.weight of shape (2048,)
2019-02-26 17:04:35,557 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.bias loaded from layer4.1.bn1.bias of shape (512,)
2019-02-26 17:04:35,558 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.weight loaded from layer4.1.bn1.weight of shape (512,)
2019-02-26 17:04:35,558 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.bias loaded from layer4.1.bn2.bias of shape (512,)
2019-02-26 17:04:35,558 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.weight loaded from layer4.1.bn2.weight of shape (512,)
2019-02-26 17:04:35,558 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.bias loaded from layer4.1.bn3.bias of shape (2048,)
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2019-02-26 17:04:35,558 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.conv2.weight loaded from layer4.1.conv2.weight of shape (512, 512, 3, 3)
2019-02-26 17:04:35,558 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.conv3.weight loaded from layer4.1.conv3.weight of shape (2048, 512, 1, 1)
2019-02-26 17:04:35,558 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.bias loaded from layer4.2.bn1.bias of shape (512,)
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2019-02-26 17:04:35,559 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.conv2.weight loaded from layer4.2.conv2.weight of shape (512, 512, 3, 3)
2019-02-26 17:04:35,559 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.conv3.weight loaded from layer4.2.conv3.weight of shape (2048, 512, 1, 1)
2019-02-26 17:04:35,559 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,)
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2019-02-26 17:04:35,586 maskrcnn_benchmark.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14
2019-02-26 17:04:48,312 maskrcnn_benchmark.trainer INFO: Start training
2019-02-26 17:07:53,964 maskrcnn_benchmark INFO: Using 2 GPUs
2019-02-26 17:07:53,964 maskrcnn_benchmark INFO: Namespace(config_file='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml', distributed=True, local_rank=0, opts=[], skip_test=False)
2019-02-26 17:07:53,964 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2019-02-26 17:07:55,828 maskrcnn_benchmark INFO:
PyTorch version: 1.0.1.post2
Is debug build: No
CUDA used to build PyTorch: 10.0.130
OS: Ubuntu 16.04.5 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
CMake version: version 3.5.1
Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce RTX 2080
GPU 1: GeForce RTX 2080
Nvidia driver version: 410.72
cuDNN version: Could not collect
Versions of relevant libraries:
[pip] Could not collect
[conda] pytorch 1.0.1 py3.7_cuda10.0.130_cudnn7.4.2_2 pytorch
Pillow (5.4.1)
2019-02-26 17:07:55,829 maskrcnn_benchmark INFO: Loaded configuration file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
2019-02-26 17:07:55,829 maskrcnn_benchmark INFO:
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
BACKBONE:
CONV_BODY: "R-50-FPN"
OUT_CHANNELS: 256
RPN:
USE_FPN: True
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
PRE_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TEST: 1000
ROI_HEADS:
USE_FPN: True
ROI_BOX_HEAD:
POOLER_RESOLUTION: 7
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POOLER_SAMPLING_RATIO: 2
FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
PREDICTOR: "FPNPredictor"
ROI_MASK_HEAD:
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
PREDICTOR: "MaskRCNNC4Predictor"
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
MASK_ON: True
DATASETS:
TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
TEST: ("coco_2014_minival",)
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.02
WEIGHT_DECAY: 0.0001
STEPS: (60000, 80000)
MAX_ITER: 90000
IMS_PER_BATCH: 4
TEST:
IMS_PER_BATCH: 4
OUTPUT_DIR:
runs/COCO_2014
2019-02-26 17:07:55,829 maskrcnn_benchmark INFO: Running with config:
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 4
SIZE_DIVISIBILITY: 32
DATASETS:
TEST: ('coco_2014_minival',)
TRAIN: ('coco_2014_train', 'coco_2014_valminusminival')
INPUT:
MAX_SIZE_TEST: 1333
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MIN_SIZE_TRAIN: (800,)
PIXEL_MEAN: [102.9801, 115.9465, 122.7717]
PIXEL_STD: [1.0, 1.0, 1.0]
TO_BGR255: True
MODEL:
BACKBONE:
CONV_BODY: R-50-FPN
FREEZE_CONV_BODY_AT: 2
OUT_CHANNELS: 256
USE_GN: False
CLS_AGNOSTIC_BBOX_REG: False
DEVICE: cuda
FPN:
USE_GN: False
USE_RELU: False
GROUP_NORM:
DIM_PER_GP: -1
EPSILON: 1e-05
NUM_GROUPS: 32
KEYPOINT_ON: False
MASK_ON: True
META_ARCHITECTURE: GeneralizedRCNN
RESNETS:
NUM_GROUPS: 1
RES2_OUT_CHANNELS: 256
RES5_DILATION: 1
STEM_FUNC: StemWithFixedBatchNorm
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: True
TRANS_FUNC: BottleneckWithFixedBatchNorm
WIDTH_PER_GROUP: 64
RETINANET:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDES: (8, 16, 32, 64, 128)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BBOX_REG_BETA: 0.11
BBOX_REG_WEIGHT: 4.0
BG_IOU_THRESHOLD: 0.4
FG_IOU_THRESHOLD: 0.5
INFERENCE_TH: 0.05
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.4
NUM_CLASSES: 81
NUM_CONVS: 4
OCTAVE: 2.0
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
SCALES_PER_OCTAVE: 3
STRADDLE_THRESH: 0
USE_C5: True
RETINANET_ON: False
ROI_BOX_HEAD:
CONV_HEAD_DIM: 256
DILATION: 1
FEATURE_EXTRACTOR: FPN2MLPFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 81
NUM_STACKED_CONVS: 4
POOLER_RESOLUTION: 7
POOLER_SAMPLING_RATIO: 2
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
PREDICTOR: FPNPredictor
USE_GN: False
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 512
BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
BG_IOU_THRESHOLD: 0.5
DETECTIONS_PER_IMG: 100
FG_IOU_THRESHOLD: 0.5
NMS: 0.5
POSITIVE_FRACTION: 0.25
SCORE_THRESH: 0.05
USE_FPN: True
ROI_KEYPOINT_HEAD:
CONV_LAYERS: (512, 512, 512, 512, 512, 512, 512, 512)
FEATURE_EXTRACTOR: KeypointRCNNFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 17
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
PREDICTOR: KeypointRCNNPredictor
RESOLUTION: 14
SHARE_BOX_FEATURE_EXTRACTOR: True
ROI_MASK_HEAD:
CONV_LAYERS: (256, 256, 256, 256)
DILATION: 1
FEATURE_EXTRACTOR: MaskRCNNFPNFeatureExtractor
MLP_HEAD_DIM: 1024
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POSTPROCESS_MASKS: False
POSTPROCESS_MASKS_THRESHOLD: 0.5
PREDICTOR: MaskRCNNC4Predictor
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
USE_GN: False
RPN:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BATCH_SIZE_PER_IMAGE: 256
BG_IOU_THRESHOLD: 0.3
FG_IOU_THRESHOLD: 0.7
FPN_POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TRAIN: 2000
MIN_SIZE: 0
NMS_THRESH: 0.7
POSITIVE_FRACTION: 0.5
POST_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
PRE_NMS_TOP_N_TRAIN: 2000
RPN_HEAD: SingleConvRPNHead
STRADDLE_THRESH: 0
USE_FPN: True
RPN_ONLY: False
WEIGHT: catalog://ImageNetPretrained/MSRA/R-50
OUTPUT_DIR: runs/COCO_2014
PATHS_CATALOG: /home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/config/paths_catalog.py
SOLVER:
BASE_LR: 0.02
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 2500
GAMMA: 0.1
IMS_PER_BATCH: 4
MAX_ITER: 90000
MOMENTUM: 0.9
STEPS: (60000, 80000)
WARMUP_FACTOR: 0.3333333333333333
WARMUP_ITERS: 500
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
TEST:
DETECTIONS_PER_IMG: 100
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 4
2019-02-26 17:07:56,377 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50
2019-02-26 17:07:56,378 maskrcnn_benchmark.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
2019-02-26 17:07:56,395 maskrcnn_benchmark.utils.checkpoint INFO: url https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl cached in /home/csbotos/.torch/models/R-50.pkl
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: Remapping C2 weights
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: conv1_b mapped name: conv1.bias
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: conv1_w mapped name: conv1.weight
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: fc1000_b mapped name: fc1000.bias
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: fc1000_w mapped name: fc1000.weight
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_b mapped name: layer1.0.downsample.0.bias
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_b mapped name: layer1.0.downsample.1.bias
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_s mapped name: layer1.0.downsample.1.weight
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_w mapped name: layer1.0.downsample.0.weight
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_b mapped name: layer1.0.conv1.bias
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_b mapped name: layer1.0.bn1.bias
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_s mapped name: layer1.0.bn1.weight
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_w mapped name: layer1.0.conv1.weight
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_b mapped name: layer1.0.conv2.bias
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_b mapped name: layer1.0.bn2.bias
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_s mapped name: layer1.0.bn2.weight
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_w mapped name: layer1.0.conv2.weight
2019-02-26 17:07:56,438 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_b mapped name: layer1.0.conv3.bias
2019-02-26 17:07:56,439 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_bn_b mapped name: layer1.0.bn3.bias
2019-02-26 17:07:56,439 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_bn_s mapped name: layer1.0.bn3.weight
2019-02-26 17:07:56,439 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_w mapped name: layer1.0.conv3.weight
2019-02-26 17:07:56,439 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_b mapped name: layer1.1.conv1.bias
2019-02-26 17:07:56,439 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_bn_b mapped name: layer1.1.bn1.bias
2019-02-26 17:07:56,439 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_bn_s mapped name: layer1.1.bn1.weight
2019-02-26 17:07:56,439 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_w mapped name: layer1.1.conv1.weight
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2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.1.bn1.weight loaded from layer3.1.bn1.weight of shape (256,)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.1.bn2.bias loaded from layer3.1.bn2.bias of shape (256,)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.1.bn2.weight loaded from layer3.1.bn2.weight of shape (256,)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.1.bn3.bias loaded from layer3.1.bn3.bias of shape (1024,)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.1.bn3.weight loaded from layer3.1.bn3.weight of shape (1024,)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.1.conv1.weight loaded from layer3.1.conv1.weight of shape (256, 1024, 1, 1)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.1.conv2.weight loaded from layer3.1.conv2.weight of shape (256, 256, 3, 3)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.1.conv3.weight loaded from layer3.1.conv3.weight of shape (1024, 256, 1, 1)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.bn1.bias loaded from layer3.2.bn1.bias of shape (256,)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.bn1.weight loaded from layer3.2.bn1.weight of shape (256,)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.bn2.bias loaded from layer3.2.bn2.bias of shape (256,)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.bn2.weight loaded from layer3.2.bn2.weight of shape (256,)
2019-02-26 17:07:56,457 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.bn3.bias loaded from layer3.2.bn3.bias of shape (1024,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.bn3.weight loaded from layer3.2.bn3.weight of shape (1024,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.conv1.weight loaded from layer3.2.conv1.weight of shape (256, 1024, 1, 1)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.conv2.weight loaded from layer3.2.conv2.weight of shape (256, 256, 3, 3)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.conv3.weight loaded from layer3.2.conv3.weight of shape (1024, 256, 1, 1)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.bn1.bias loaded from layer3.3.bn1.bias of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.bn1.weight loaded from layer3.3.bn1.weight of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.bn2.bias loaded from layer3.3.bn2.bias of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.bn2.weight loaded from layer3.3.bn2.weight of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.bn3.bias loaded from layer3.3.bn3.bias of shape (1024,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.bn3.weight loaded from layer3.3.bn3.weight of shape (1024,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.conv1.weight loaded from layer3.3.conv1.weight of shape (256, 1024, 1, 1)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.conv2.weight loaded from layer3.3.conv2.weight of shape (256, 256, 3, 3)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.conv3.weight loaded from layer3.3.conv3.weight of shape (1024, 256, 1, 1)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.bn1.bias loaded from layer3.4.bn1.bias of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.bn1.weight loaded from layer3.4.bn1.weight of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.bn2.bias loaded from layer3.4.bn2.bias of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.bn2.weight loaded from layer3.4.bn2.weight of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.bn3.bias loaded from layer3.4.bn3.bias of shape (1024,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.bn3.weight loaded from layer3.4.bn3.weight of shape (1024,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.conv1.weight loaded from layer3.4.conv1.weight of shape (256, 1024, 1, 1)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.conv2.weight loaded from layer3.4.conv2.weight of shape (256, 256, 3, 3)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.4.conv3.weight loaded from layer3.4.conv3.weight of shape (1024, 256, 1, 1)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn1.bias loaded from layer3.5.bn1.bias of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn1.weight loaded from layer3.5.bn1.weight of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn2.bias loaded from layer3.5.bn2.bias of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn2.weight loaded from layer3.5.bn2.weight of shape (256,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn3.bias loaded from layer3.5.bn3.bias of shape (1024,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.bn3.weight loaded from layer3.5.bn3.weight of shape (1024,)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.conv1.weight loaded from layer3.5.conv1.weight of shape (256, 1024, 1, 1)
2019-02-26 17:07:56,458 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.conv2.weight loaded from layer3.5.conv2.weight of shape (256, 256, 3, 3)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.5.conv3.weight loaded from layer3.5.conv3.weight of shape (1024, 256, 1, 1)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn1.bias loaded from layer4.0.bn1.bias of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn1.weight loaded from layer4.0.bn1.weight of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn2.bias loaded from layer4.0.bn2.bias of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn2.weight loaded from layer4.0.bn2.weight of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn3.bias loaded from layer4.0.bn3.bias of shape (2048,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn3.weight loaded from layer4.0.bn3.weight of shape (2048,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.conv1.weight loaded from layer4.0.conv1.weight of shape (512, 1024, 1, 1)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.conv2.weight loaded from layer4.0.conv2.weight of shape (512, 512, 3, 3)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.conv3.weight loaded from layer4.0.conv3.weight of shape (2048, 512, 1, 1)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.0.weight loaded from layer4.0.downsample.0.weight of shape (2048, 1024, 1, 1)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.bias loaded from layer4.0.downsample.1.bias of shape (2048,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.weight loaded from layer4.0.downsample.1.weight of shape (2048,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.bias loaded from layer4.1.bn1.bias of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.weight loaded from layer4.1.bn1.weight of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.bias loaded from layer4.1.bn2.bias of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.weight loaded from layer4.1.bn2.weight of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.bias loaded from layer4.1.bn3.bias of shape (2048,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.weight loaded from layer4.1.bn3.weight of shape (2048,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.conv1.weight loaded from layer4.1.conv1.weight of shape (512, 2048, 1, 1)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.conv2.weight loaded from layer4.1.conv2.weight of shape (512, 512, 3, 3)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.conv3.weight loaded from layer4.1.conv3.weight of shape (2048, 512, 1, 1)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.bias loaded from layer4.2.bn1.bias of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.weight loaded from layer4.2.bn1.weight of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.bias loaded from layer4.2.bn2.bias of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.weight loaded from layer4.2.bn2.weight of shape (512,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.bias loaded from layer4.2.bn3.bias of shape (2048,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.weight loaded from layer4.2.bn3.weight of shape (2048,)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.conv1.weight loaded from layer4.2.conv1.weight of shape (512, 2048, 1, 1)
2019-02-26 17:07:56,459 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.conv2.weight loaded from layer4.2.conv2.weight of shape (512, 512, 3, 3)
2019-02-26 17:07:56,460 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.conv3.weight loaded from layer4.2.conv3.weight of shape (2048, 512, 1, 1)
2019-02-26 17:07:56,460 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,)
2019-02-26 17:07:56,460 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.weight loaded from bn1.weight of shape (64,)
2019-02-26 17:07:56,460 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.conv1.weight loaded from conv1.weight of shape (64, 3, 7, 7)
2019-02-26 17:07:56,487 maskrcnn_benchmark.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14
2019-02-26 17:08:09,283 maskrcnn_benchmark.trainer INFO: Start training
2019-02-26 17:08:17,953 maskrcnn_benchmark.trainer INFO: eta: 10:49:59 iter: 20 loss: 1.8445 (2.0956) loss_box_reg: 0.0555 (0.0676) loss_classifier: 0.4840 (0.6889) loss_mask: 0.7149 (0.7622) loss_objectness: 0.3720 (0.4381) loss_rpn_box_reg: 0.0900 (0.1388) time: 0.3795 (0.4334) data: 0.0060 (0.0382) lr: 0.007173 max mem: 3481
2019-02-26 17:08:25,797 maskrcnn_benchmark.trainer INFO: eta: 10:18:58 iter: 40 loss: 1.4523 (1.7668) loss_box_reg: 0.0756 (0.0809) loss_classifier: 0.3642 (0.5420) loss_mask: 0.6861 (0.7239) loss_objectness: 0.1810 (0.3152) loss_rpn_box_reg: 0.0572 (0.1048) time: 0.3846 (0.4128) data: 0.0049 (0.0218) lr: 0.007707 max mem: 3482
2019-02-26 17:08:33,483 maskrcnn_benchmark.trainer INFO: eta: 10:04:34 iter: 60 loss: 1.2473 (1.6727) loss_box_reg: 0.0377 (0.0766) loss_classifier: 0.2751 (0.4757) loss_mask: 0.6850 (0.7102) loss_objectness: 0.2085 (0.2984) loss_rpn_box_reg: 0.0823 (0.1118) time: 0.3798 (0.4033) data: 0.0065 (0.0167) lr: 0.008240 max mem: 3482
2019-02-26 17:08:41,016 maskrcnn_benchmark.trainer INFO: eta: 9:54:26 iter: 80 loss: 1.2097 (1.5717) loss_box_reg: 0.0244 (0.0699) loss_classifier: 0.2403 (0.4222) loss_mask: 0.6758 (0.7009) loss_objectness: 0.1787 (0.2706) loss_rpn_box_reg: 0.0722 (0.1081) time: 0.3756 (0.3966) data: 0.0046 (0.0139) lr: 0.008773 max mem: 3482
2019-02-26 17:09:06,283 maskrcnn_benchmark INFO: Using 2 GPUs
2019-02-26 17:09:06,283 maskrcnn_benchmark INFO: Namespace(config_file='configs/e2e_mask_rcnn_R_50_FPN_1x.yaml', distributed=True, local_rank=0, opts=[], skip_test=False)
2019-02-26 17:09:06,283 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2019-02-26 17:09:08,179 maskrcnn_benchmark INFO:
PyTorch version: 1.0.1.post2
Is debug build: No
CUDA used to build PyTorch: 10.0.130
OS: Ubuntu 16.04.5 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
CMake version: version 3.5.1
Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce RTX 2080
GPU 1: GeForce RTX 2080
Nvidia driver version: 410.72
cuDNN version: Could not collect
Versions of relevant libraries:
[pip] Could not collect
[conda] pytorch 1.0.1 py3.7_cuda10.0.130_cudnn7.4.2_2 pytorch
Pillow (5.4.1)
2019-02-26 17:09:08,179 maskrcnn_benchmark INFO: Loaded configuration file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
2019-02-26 17:09:08,179 maskrcnn_benchmark INFO:
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
BACKBONE:
CONV_BODY: "R-50-FPN"
OUT_CHANNELS: 256
RPN:
USE_FPN: True
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
PRE_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TEST: 1000
ROI_HEADS:
USE_FPN: True
ROI_BOX_HEAD:
POOLER_RESOLUTION: 7
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POOLER_SAMPLING_RATIO: 2
FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
PREDICTOR: "FPNPredictor"
ROI_MASK_HEAD:
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
PREDICTOR: "MaskRCNNC4Predictor"
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
MASK_ON: True
DATASETS:
TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
TEST: ("coco_2014_minival",)
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.02
WEIGHT_DECAY: 0.0001
STEPS: (60000, 80000)
MAX_ITER: 90000
IMS_PER_BATCH: 4
TEST:
IMS_PER_BATCH: 4
OUTPUT_DIR:
runs/COCO_2014
2019-02-26 17:09:08,180 maskrcnn_benchmark INFO: Running with config:
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 4
SIZE_DIVISIBILITY: 32
DATASETS:
TEST: ('coco_2014_minival',)
TRAIN: ('coco_2014_train', 'coco_2014_valminusminival')
INPUT:
MAX_SIZE_TEST: 1333
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MIN_SIZE_TRAIN: (800,)
PIXEL_MEAN: [102.9801, 115.9465, 122.7717]
PIXEL_STD: [1.0, 1.0, 1.0]
TO_BGR255: True
MODEL:
BACKBONE:
CONV_BODY: R-50-FPN
FREEZE_CONV_BODY_AT: 2
OUT_CHANNELS: 256
USE_GN: False
CLS_AGNOSTIC_BBOX_REG: False
DEVICE: cuda
FPN:
USE_GN: False
USE_RELU: False
GROUP_NORM:
DIM_PER_GP: -1
EPSILON: 1e-05
NUM_GROUPS: 32
KEYPOINT_ON: False
MASK_ON: True
META_ARCHITECTURE: GeneralizedRCNN
RESNETS:
NUM_GROUPS: 1
RES2_OUT_CHANNELS: 256
RES5_DILATION: 1
STEM_FUNC: StemWithFixedBatchNorm
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: True
TRANS_FUNC: BottleneckWithFixedBatchNorm
WIDTH_PER_GROUP: 64
RETINANET:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDES: (8, 16, 32, 64, 128)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BBOX_REG_BETA: 0.11
BBOX_REG_WEIGHT: 4.0
BG_IOU_THRESHOLD: 0.4
FG_IOU_THRESHOLD: 0.5
INFERENCE_TH: 0.05
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.4
NUM_CLASSES: 81
NUM_CONVS: 4
OCTAVE: 2.0
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
SCALES_PER_OCTAVE: 3
STRADDLE_THRESH: 0
USE_C5: True
RETINANET_ON: False
ROI_BOX_HEAD:
CONV_HEAD_DIM: 256
DILATION: 1
FEATURE_EXTRACTOR: FPN2MLPFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 81
NUM_STACKED_CONVS: 4
POOLER_RESOLUTION: 7
POOLER_SAMPLING_RATIO: 2
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
PREDICTOR: FPNPredictor
USE_GN: False
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 512
BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
BG_IOU_THRESHOLD: 0.5
DETECTIONS_PER_IMG: 100
FG_IOU_THRESHOLD: 0.5
NMS: 0.5
POSITIVE_FRACTION: 0.25
SCORE_THRESH: 0.05
USE_FPN: True
ROI_KEYPOINT_HEAD:
CONV_LAYERS: (512, 512, 512, 512, 512, 512, 512, 512)
FEATURE_EXTRACTOR: KeypointRCNNFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 17
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
PREDICTOR: KeypointRCNNPredictor
RESOLUTION: 14
SHARE_BOX_FEATURE_EXTRACTOR: True
ROI_MASK_HEAD:
CONV_LAYERS: (256, 256, 256, 256)
DILATION: 1
FEATURE_EXTRACTOR: MaskRCNNFPNFeatureExtractor
MLP_HEAD_DIM: 1024
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POSTPROCESS_MASKS: False
POSTPROCESS_MASKS_THRESHOLD: 0.5
PREDICTOR: MaskRCNNC4Predictor
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
USE_GN: False
RPN:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BATCH_SIZE_PER_IMAGE: 256
BG_IOU_THRESHOLD: 0.3
FG_IOU_THRESHOLD: 0.7
FPN_POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TRAIN: 2000
MIN_SIZE: 0
NMS_THRESH: 0.7
POSITIVE_FRACTION: 0.5
POST_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
PRE_NMS_TOP_N_TRAIN: 2000
RPN_HEAD: SingleConvRPNHead
STRADDLE_THRESH: 0
USE_FPN: True
RPN_ONLY: False
WEIGHT: catalog://ImageNetPretrained/MSRA/R-50
OUTPUT_DIR: runs/COCO_2014
PATHS_CATALOG: /home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/config/paths_catalog.py
SOLVER:
BASE_LR: 0.02
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 2500
GAMMA: 0.1
IMS_PER_BATCH: 4
MAX_ITER: 90000
MOMENTUM: 0.9
STEPS: (60000, 80000)
WARMUP_FACTOR: 0.3333333333333333
WARMUP_ITERS: 500
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
TEST:
DETECTIONS_PER_IMG: 100
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 4
2019-02-26 17:09:08,693 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50
2019-02-26 17:09:08,694 maskrcnn_benchmark.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
2019-02-26 17:09:08,728 maskrcnn_benchmark.utils.checkpoint INFO: url https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl cached in /home/csbotos/.torch/models/R-50.pkl
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2019-02-26 17:09:08,798 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.bias loaded from layer4.2.bn1.bias of shape (512,)
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2019-02-26 17:09:08,799 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,)
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2019-02-26 17:09:08,826 maskrcnn_benchmark.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14
2019-02-26 17:09:21,521 maskrcnn_benchmark.trainer INFO: Start training
2019-02-26 17:09:29,926 maskrcnn_benchmark.trainer INFO: eta: 10:30:10 iter: 20 loss: 1.7093 (2.0555) loss_box_reg: 0.0336 (0.0540) loss_classifier: 0.3341 (0.6290) loss_mask: 0.7156 (0.7832) loss_objectness: 0.3665 (0.4536) loss_rpn_box_reg: 0.0907 (0.1356) time: 0.3809 (0.4202) data: 0.0048 (0.0375) lr: 0.007173 max mem: 3483
2019-02-26 17:09:37,786 maskrcnn_benchmark.trainer INFO: eta: 10:09:36 iter: 40 loss: 1.4653 (1.7505) loss_box_reg: 0.0717 (0.0678) loss_classifier: 0.3622 (0.5103) loss_mask: 0.6912 (0.7366) loss_objectness: 0.1844 (0.3320) loss_rpn_box_reg: 0.0500 (0.1038) time: 0.3875 (0.4066) data: 0.0049 (0.0214) lr: 0.007707 max mem: 3483
2019-02-26 17:09:45,784 maskrcnn_benchmark.trainer INFO: eta: 10:06:08 iter: 60 loss: 1.4863 (1.7214) loss_box_reg: 0.1004 (0.0815) loss_classifier: 0.4001 (0.4851) loss_mask: 0.6924 (0.7224) loss_objectness: 0.2354 (0.3204) loss_rpn_box_reg: 0.0885 (0.1120) time: 0.4003 (0.4044) data: 0.0060 (0.0164) lr: 0.008240 max mem: 3483
2019-02-26 17:09:53,527 maskrcnn_benchmark.trainer INFO: eta: 9:59:33 iter: 80 loss: 1.2677 (1.6293) loss_box_reg: 0.0607 (0.0782) loss_classifier: 0.2808 (0.4394) loss_mask: 0.6848 (0.7132) loss_objectness: 0.1916 (0.2902) loss_rpn_box_reg: 0.0745 (0.1083) time: 0.3815 (0.4001) data: 0.0046 (0.0136) lr: 0.008773 max mem: 3483
2019-02-26 17:10:01,426 maskrcnn_benchmark.trainer INFO: eta: 9:57:53 iter: 100 loss: 1.3474 (1.5831) loss_box_reg: 0.0585 (0.0764) loss_classifier: 0.2924 (0.4170) loss_mask: 0.6853 (0.7068) loss_objectness: 0.2075 (0.2732) loss_rpn_box_reg: 0.1132 (0.1096) time: 0.3948 (0.3990) data: 0.0050 (0.0120) lr: 0.009307 max mem: 3483
2019-02-26 17:10:09,462 maskrcnn_benchmark.trainer INFO: eta: 9:58:27 iter: 120 loss: 1.2514 (1.5365) loss_box_reg: 0.0378 (0.0726) loss_classifier: 0.2166 (0.3926) loss_mask: 0.6706 (0.7008) loss_objectness: 0.1899 (0.2626) loss_rpn_box_reg: 0.0787 (0.1080) time: 0.4005 (0.3995) data: 0.0058 (0.0111) lr: 0.009840 max mem: 3483
2019-02-26 17:10:17,692 maskrcnn_benchmark.trainer INFO: eta: 10:00:52 iter: 140 loss: 1.3545 (1.5221) loss_box_reg: 0.0786 (0.0752) loss_classifier: 0.3483 (0.3908) loss_mask: 0.6736 (0.6975) loss_objectness: 0.1952 (0.2522) loss_rpn_box_reg: 0.0654 (0.1065) time: 0.4074 (0.4012) data: 0.0058 (0.0103) lr: 0.010373 max mem: 3483
2019-02-26 17:10:25,961 maskrcnn_benchmark.trainer INFO: eta: 10:03:02 iter: 160 loss: 1.3227 (1.5084) loss_box_reg: 0.0777 (0.0753) loss_classifier: 0.3429 (0.3851) loss_mask: 0.6656 (0.6930) loss_objectness: 0.1960 (0.2468) loss_rpn_box_reg: 0.0809 (0.1082) time: 0.4157 (0.4027) data: 0.0060 (0.0098) lr: 0.010907 max mem: 3483
2019-02-26 17:10:34,376 maskrcnn_benchmark.trainer INFO: eta: 10:05:54 iter: 180 loss: 1.5138 (1.5087) loss_box_reg: 0.1136 (0.0803) loss_classifier: 0.4060 (0.3884) loss_mask: 0.6634 (0.6893) loss_objectness: 0.1574 (0.2417) loss_rpn_box_reg: 0.1046 (0.1091) time: 0.4243 (0.4047) data: 0.0054 (0.0094) lr: 0.011440 max mem: 3483
2019-02-26 17:10:42,806 maskrcnn_benchmark.trainer INFO: eta: 10:08:16 iter: 200 loss: 1.3821 (1.5060) loss_box_reg: 0.1218 (0.0850) loss_classifier: 0.3835 (0.3923) loss_mask: 0.6557 (0.6859) loss_objectness: 0.1319 (0.2349) loss_rpn_box_reg: 0.0560 (0.1080) time: 0.4217 (0.4064) data: 0.0055 (0.0090) lr: 0.011973 max mem: 3607
2019-02-26 17:10:51,279 maskrcnn_benchmark.trainer INFO: eta: 10:10:28 iter: 220 loss: 1.3811 (1.5128) loss_box_reg: 0.1265 (0.0904) loss_classifier: 0.4035 (0.4003) loss_mask: 0.6486 (0.6828) loss_objectness: 0.1565 (0.2311) loss_rpn_box_reg: 0.0690 (0.1082) time: 0.4177 (0.4080) data: 0.0052 (0.0087) lr: 0.012507 max mem: 3718
2019-02-26 17:10:59,777 maskrcnn_benchmark.trainer INFO: eta: 10:12:27 iter: 240 loss: 1.3966 (1.5107) loss_box_reg: 0.1121 (0.0928) loss_classifier: 0.3904 (0.4029) loss_mask: 0.6379 (0.6787) loss_objectness: 0.1916 (0.2284) loss_rpn_box_reg: 0.0766 (0.1079) time: 0.4214 (0.4094) data: 0.0060 (0.0085) lr: 0.013040 max mem: 3718
2019-02-26 17:11:08,251 maskrcnn_benchmark.trainer INFO: eta: 10:13:57 iter: 260 loss: 1.3857 (1.5100) loss_box_reg: 0.1214 (0.0965) loss_classifier: 0.3769 (0.4065) loss_mask: 0.6301 (0.6748) loss_objectness: 0.1642 (0.2242) loss_rpn_box_reg: 0.0953 (0.1081) time: 0.4214 (0.4105) data: 0.0061 (0.0084) lr: 0.013573 max mem: 3807
2019-02-26 17:11:17,035 maskrcnn_benchmark.trainer INFO: eta: 10:16:53 iter: 280 loss: 1.6235 (1.5132) loss_box_reg: 0.1410 (0.1010) loss_classifier: 0.4234 (0.4124) loss_mask: 0.6117 (0.6700) loss_objectness: 0.1559 (0.2203) loss_rpn_box_reg: 0.0946 (0.1095) time: 0.4337 (0.4125) data: 0.0057 (0.0082) lr: 0.014107 max mem: 3807
2019-02-26 17:11:25,823 maskrcnn_benchmark.trainer INFO: eta: 10:19:25 iter: 300 loss: 1.4247 (1.5140) loss_box_reg: 0.1348 (0.1039) loss_classifier: 0.3964 (0.4137) loss_mask: 0.6078 (0.6662) loss_objectness: 0.1982 (0.2207) loss_rpn_box_reg: 0.0898 (0.1095) time: 0.4363 (0.4143) data: 0.0065 (0.0081) lr: 0.014640 max mem: 3807
2019-02-26 17:11:34,477 maskrcnn_benchmark.trainer INFO: eta: 10:21:00 iter: 320 loss: 1.3533 (1.5089) loss_box_reg: 0.1176 (0.1059) loss_classifier: 0.3912 (0.4131) loss_mask: 0.6066 (0.6623) loss_objectness: 0.1340 (0.2176) loss_rpn_box_reg: 0.0846 (0.1098) time: 0.4249 (0.4155) data: 0.0055 (0.0080) lr: 0.015173 max mem: 3807
2019-02-26 17:11:43,226 maskrcnn_benchmark.trainer INFO: eta: 10:22:48 iter: 340 loss: 1.4995 (1.5096) loss_box_reg: 0.1205 (0.1078) loss_classifier: 0.4090 (0.4147) loss_mask: 0.6170 (0.6597) loss_objectness: 0.1853 (0.2164) loss_rpn_box_reg: 0.0933 (0.1109) time: 0.4298 (0.4168) data: 0.0065 (0.0079) lr: 0.015707 max mem: 3807
2019-02-26 17:11:52,047 maskrcnn_benchmark.trainer INFO: eta: 10:24:40 iter: 360 loss: 1.4876 (1.5084) loss_box_reg: 0.1595 (0.1105) loss_classifier: 0.4884 (0.4190) loss_mask: 0.6008 (0.6565) loss_objectness: 0.1254 (0.2124) loss_rpn_box_reg: 0.0727 (0.1101) time: 0.4371 (0.4181) data: 0.0051 (0.0077) lr: 0.016240 max mem: 3807
2019-02-26 17:12:00,916 maskrcnn_benchmark.trainer INFO: eta: 10:26:31 iter: 380 loss: 1.6006 (1.5113) loss_box_reg: 0.1707 (0.1137) loss_classifier: 0.4725 (0.4228) loss_mask: 0.5988 (0.6525) loss_objectness: 0.1868 (0.2113) loss_rpn_box_reg: 0.1225 (0.1110) time: 0.4394 (0.4195) data: 0.0056 (0.0077) lr: 0.016773 max mem: 3807
2019-02-26 17:12:09,736 maskrcnn_benchmark.trainer INFO: eta: 10:27:59 iter: 400 loss: 1.3504 (1.5073) loss_box_reg: 0.1359 (0.1153) loss_classifier: 0.3833 (0.4229) loss_mask: 0.5917 (0.6497) loss_objectness: 0.1302 (0.2088) loss_rpn_box_reg: 0.0678 (0.1106) time: 0.4290 (0.4205) data: 0.0058 (0.0076) lr: 0.017307 max mem: 3807
2019-02-26 17:12:18,353 maskrcnn_benchmark.trainer INFO: eta: 10:28:35 iter: 420 loss: 1.3680 (1.5043) loss_box_reg: 0.1200 (0.1164) loss_classifier: 0.3833 (0.4221) loss_mask: 0.5936 (0.6470) loss_objectness: 0.1720 (0.2078) loss_rpn_box_reg: 0.1018 (0.1109) time: 0.4250 (0.4210) data: 0.0052 (0.0075) lr: 0.017840 max mem: 3807
2019-02-26 17:12:27,117 maskrcnn_benchmark.trainer INFO: eta: 10:29:37 iter: 440 loss: 1.3670 (1.5051) loss_box_reg: 0.1285 (0.1180) loss_classifier: 0.4140 (0.4252) loss_mask: 0.5669 (0.6431) loss_objectness: 0.1490 (0.2062) loss_rpn_box_reg: 0.1157 (0.1127) time: 0.4244 (0.4218) data: 0.0058 (0.0075) lr: 0.018373 max mem: 3807
2019-02-26 17:12:35,903 maskrcnn_benchmark.trainer INFO: eta: 10:30:36 iter: 460 loss: 1.4865 (1.5055) loss_box_reg: 0.1565 (0.1201) loss_classifier: 0.4528 (0.4272) loss_mask: 0.5565 (0.6394) loss_objectness: 0.1725 (0.2048) loss_rpn_box_reg: 0.1089 (0.1138) time: 0.4373 (0.4226) data: 0.0060 (0.0074) lr: 0.018907 max mem: 3807
2019-02-26 17:12:44,402 maskrcnn_benchmark.trainer INFO: eta: 10:30:36 iter: 480 loss: 1.3709 (1.4976) loss_box_reg: 0.1054 (0.1196) loss_classifier: 0.4062 (0.4246) loss_mask: 0.5733 (0.6368) loss_objectness: 0.1536 (0.2031) loss_rpn_box_reg: 0.0765 (0.1134) time: 0.4266 (0.4227) data: 0.0054 (0.0074) lr: 0.019440 max mem: 3807
2019-02-26 17:12:53,151 maskrcnn_benchmark.trainer INFO: eta: 10:31:21 iter: 500 loss: 1.4127 (1.4980) loss_box_reg: 0.1343 (0.1211) loss_classifier: 0.4550 (0.4277) loss_mask: 0.5488 (0.6338) loss_objectness: 0.1675 (0.2023) loss_rpn_box_reg: 0.0840 (0.1132) time: 0.4366 (0.4233) data: 0.0053 (0.0073) lr: 0.019973 max mem: 3834
2019-02-26 17:13:01,900 maskrcnn_benchmark.trainer INFO: eta: 10:32:01 iter: 520 loss: 1.3270 (1.4975) loss_box_reg: 0.1431 (0.1233) loss_classifier: 0.4170 (0.4305) loss_mask: 0.5492 (0.6309) loss_objectness: 0.1363 (0.2000) loss_rpn_box_reg: 0.0669 (0.1129) time: 0.4308 (0.4238) data: 0.0060 (0.0073) lr: 0.020000 max mem: 3834
2019-02-26 17:13:10,344 maskrcnn_benchmark.trainer INFO: eta: 10:31:48 iter: 540 loss: 1.3684 (1.4939) loss_box_reg: 0.1247 (0.1238) loss_classifier: 0.3790 (0.4301) loss_mask: 0.5320 (0.6269) loss_objectness: 0.1636 (0.1993) loss_rpn_box_reg: 0.0835 (0.1139) time: 0.4225 (0.4237) data: 0.0057 (0.0072) lr: 0.020000 max mem: 3834
2019-02-26 17:13:19,075 maskrcnn_benchmark.trainer INFO: eta: 10:32:20 iter: 560 loss: 1.4901 (1.4948) loss_box_reg: 0.1470 (0.1248) loss_classifier: 0.4444 (0.4316) loss_mask: 0.5702 (0.6247) loss_objectness: 0.1661 (0.1994) loss_rpn_box_reg: 0.0690 (0.1143) time: 0.4311 (0.4242) data: 0.0059 (0.0072) lr: 0.020000 max mem: 3834
2019-02-26 17:13:27,633 maskrcnn_benchmark.trainer INFO: eta: 10:32:23 iter: 580 loss: 1.4121 (1.4912) loss_box_reg: 0.1190 (0.1251) loss_classifier: 0.3642 (0.4320) loss_mask: 0.5295 (0.6217) loss_objectness: 0.1568 (0.1981) loss_rpn_box_reg: 0.0987 (0.1143) time: 0.4241 (0.4243) data: 0.0060 (0.0072) lr: 0.020000 max mem: 3864
2019-02-26 17:13:36,262 maskrcnn_benchmark.trainer INFO: eta: 10:32:36 iter: 600 loss: 1.3608 (1.4868) loss_box_reg: 0.1245 (0.1255) loss_classifier: 0.3870 (0.4316) loss_mask: 0.4910 (0.6175) loss_objectness: 0.1815 (0.1977) loss_rpn_box_reg: 0.0824 (0.1146) time: 0.4324 (0.4246) data: 0.0058 (0.0072) lr: 0.020000 max mem: 3864
2019-02-26 17:13:44,832 maskrcnn_benchmark.trainer INFO: eta: 10:32:39 iter: 620 loss: 1.3555 (1.4837) loss_box_reg: 0.1164 (0.1260) loss_classifier: 0.3991 (0.4314) loss_mask: 0.5238 (0.6145) loss_objectness: 0.1571 (0.1967) loss_rpn_box_reg: 0.0868 (0.1152) time: 0.4334 (0.4247) data: 0.0061 (0.0071) lr: 0.020000 max mem: 3864
2019-02-26 17:13:53,720 maskrcnn_benchmark.trainer INFO: eta: 10:33:25 iter: 640 loss: 1.3758 (1.4842) loss_box_reg: 0.1650 (0.1278) loss_classifier: 0.4975 (0.4351) loss_mask: 0.5255 (0.6117) loss_objectness: 0.1252 (0.1952) loss_rpn_box_reg: 0.0633 (0.1143) time: 0.4354 (0.4253) data: 0.0060 (0.0071) lr: 0.020000 max mem: 3864
2019-02-26 17:14:02,387 maskrcnn_benchmark.trainer INFO: eta: 10:33:38 iter: 660 loss: 1.4006 (1.4839) loss_box_reg: 0.1893 (0.1294) loss_classifier: 0.5288 (0.4376) loss_mask: 0.5289 (0.6085) loss_objectness: 0.1558 (0.1942) loss_rpn_box_reg: 0.0781 (0.1142) time: 0.4337 (0.4256) data: 0.0052 (0.0071) lr: 0.020000 max mem: 3864
2019-02-26 17:14:11,176 maskrcnn_benchmark.trainer INFO: eta: 10:34:06 iter: 680 loss: 1.6425 (1.4877) loss_box_reg: 0.1672 (0.1305) loss_classifier: 0.5525 (0.4397) loss_mask: 0.5260 (0.6059) loss_objectness: 0.2445 (0.1963) loss_rpn_box_reg: 0.1170 (0.1152) time: 0.4358 (0.4260) data: 0.0058 (0.0070) lr: 0.020000 max mem: 3864
2019-02-26 17:14:19,679 maskrcnn_benchmark.trainer INFO: eta: 10:33:56 iter: 700 loss: 1.4630 (1.4857) loss_box_reg: 0.1199 (0.1306) loss_classifier: 0.4614 (0.4395) loss_mask: 0.5110 (0.6034) loss_objectness: 0.1923 (0.1966) loss_rpn_box_reg: 0.0889 (0.1156) time: 0.4180 (0.4259) data: 0.0050 (0.0070) lr: 0.020000 max mem: 3864
2019-02-26 17:14:28,603 maskrcnn_benchmark.trainer INFO: eta: 10:34:37 iter: 720 loss: 1.4740 (1.4869) loss_box_reg: 0.1712 (0.1322) loss_classifier: 0.4607 (0.4412) loss_mask: 0.5237 (0.6013) loss_objectness: 0.1978 (0.1960) loss_rpn_box_reg: 0.1029 (0.1162) time: 0.4392 (0.4265) data: 0.0053 (0.0070) lr: 0.020000 max mem: 3864
2019-02-26 17:14:37,204 maskrcnn_benchmark.trainer INFO: eta: 10:34:38 iter: 740 loss: 1.4081 (1.4860) loss_box_reg: 0.1311 (0.1328) loss_classifier: 0.4637 (0.4419) loss_mask: 0.5180 (0.5993) loss_objectness: 0.1851 (0.1954) loss_rpn_box_reg: 0.0995 (0.1167) time: 0.4359 (0.4266) data: 0.0059 (0.0070) lr: 0.020000 max mem: 3864
2019-02-26 17:14:45,889 maskrcnn_benchmark.trainer INFO: eta: 10:34:47 iter: 760 loss: 1.3733 (1.4857) loss_box_reg: 0.1720 (0.1339) loss_classifier: 0.4995 (0.4437) loss_mask: 0.5058 (0.5968) loss_objectness: 0.1073 (0.1949) loss_rpn_box_reg: 0.0601 (0.1164) time: 0.4291 (0.4268) data: 0.0054 (0.0070) lr: 0.020000 max mem: 3864
2019-02-26 17:14:54,574 maskrcnn_benchmark.trainer INFO: eta: 10:34:56 iter: 780 loss: 1.4387 (1.4850) loss_box_reg: 0.1550 (0.1347) loss_classifier: 0.4732 (0.4446) loss_mask: 0.5106 (0.5945) loss_objectness: 0.1778 (0.1951) loss_rpn_box_reg: 0.0785 (0.1161) time: 0.4281 (0.4270) data: 0.0056 (0.0069) lr: 0.020000 max mem: 3864
2019-02-26 17:15:03,076 maskrcnn_benchmark.trainer INFO: eta: 10:34:43 iter: 800 loss: 1.4054 (1.4825) loss_box_reg: 0.1589 (0.1354) loss_classifier: 0.4303 (0.4448) loss_mask: 0.5066 (0.5927) loss_objectness: 0.1203 (0.1938) loss_rpn_box_reg: 0.0708 (0.1158) time: 0.4224 (0.4269) data: 0.0054 (0.0069) lr: 0.020000 max mem: 3864
2019-02-26 17:15:11,773 maskrcnn_benchmark.trainer INFO: eta: 10:34:51 iter: 820 loss: 1.2928 (1.4800) loss_box_reg: 0.1385 (0.1360) loss_classifier: 0.4326 (0.4454) loss_mask: 0.4942 (0.5900) loss_objectness: 0.1395 (0.1931) loss_rpn_box_reg: 0.0699 (0.1155) time: 0.4319 (0.4271) data: 0.0059 (0.0069) lr: 0.020000 max mem: 3864
2019-02-26 17:15:20,316 maskrcnn_benchmark.trainer INFO: eta: 10:34:43 iter: 840 loss: 1.3642 (1.4786) loss_box_reg: 0.1871 (0.1370) loss_classifier: 0.4892 (0.4465) loss_mask: 0.4662 (0.5874) loss_objectness: 0.1392 (0.1922) loss_rpn_box_reg: 0.0698 (0.1155) time: 0.4222 (0.4271) data: 0.0058 (0.0069) lr: 0.020000 max mem: 3864
2019-02-26 17:15:29,171 maskrcnn_benchmark.trainer INFO: eta: 10:35:07 iter: 860 loss: 1.3754 (1.4782) loss_box_reg: 0.1775 (0.1381) loss_classifier: 0.4970 (0.4478) loss_mask: 0.4804 (0.5850) loss_objectness: 0.1481 (0.1916) loss_rpn_box_reg: 0.0969 (0.1158) time: 0.4463 (0.4275) data: 0.0058 (0.0068) lr: 0.020000 max mem: 3864
2019-02-26 17:15:37,678 maskrcnn_benchmark.trainer INFO: eta: 10:34:54 iter: 880 loss: 1.3349 (1.4757) loss_box_reg: 0.1191 (0.1381) loss_classifier: 0.3839 (0.4473) loss_mask: 0.4927 (0.5829) loss_objectness: 0.1609 (0.1912) loss_rpn_box_reg: 0.0921 (0.1162) time: 0.4237 (0.4275) data: 0.0063 (0.0068) lr: 0.020000 max mem: 3864
2019-02-26 17:15:46,306 maskrcnn_benchmark.trainer INFO: eta: 10:34:53 iter: 900 loss: 1.3310 (1.4732) loss_box_reg: 0.1349 (0.1382) loss_classifier: 0.4385 (0.4472) loss_mask: 0.4825 (0.5809) loss_objectness: 0.1561 (0.1906) loss_rpn_box_reg: 0.0876 (0.1163) time: 0.4278 (0.4275) data: 0.0064 (0.0068) lr: 0.020000 max mem: 3864
2019-02-26 17:15:55,153 maskrcnn_benchmark.trainer INFO: eta: 10:35:13 iter: 920 loss: 1.3376 (1.4726) loss_box_reg: 0.1382 (0.1384) loss_classifier: 0.4107 (0.4469) loss_mask: 0.4840 (0.5791) loss_objectness: 0.1571 (0.1906) loss_rpn_box_reg: 0.0944 (0.1175) time: 0.4405 (0.4279) data: 0.0063 (0.0068) lr: 0.020000 max mem: 3864
2019-02-26 17:16:03,561 maskrcnn_benchmark.trainer INFO: eta: 10:34:50 iter: 940 loss: 1.3053 (1.4699) loss_box_reg: 0.1237 (0.1383) loss_classifier: 0.4161 (0.4459) loss_mask: 0.4841 (0.5772) loss_objectness: 0.1328 (0.1910) loss_rpn_box_reg: 0.0486 (0.1175) time: 0.4191 (0.4277) data: 0.0048 (0.0068) lr: 0.020000 max mem: 3864
2019-02-26 17:16:12,142 maskrcnn_benchmark.trainer INFO: eta: 10:34:44 iter: 960 loss: 1.4498 (1.4698) loss_box_reg: 0.1481 (0.1385) loss_classifier: 0.4145 (0.4455) loss_mask: 0.5051 (0.5755) loss_objectness: 0.2341 (0.1926) loss_rpn_box_reg: 0.0729 (0.1177) time: 0.4239 (0.4277) data: 0.0065 (0.0068) lr: 0.020000 max mem: 3864
2019-02-26 17:16:20,573 maskrcnn_benchmark.trainer INFO: eta: 10:34:25 iter: 980 loss: 1.3300 (1.4682) loss_box_reg: 0.1165 (0.1382) loss_classifier: 0.3393 (0.4446) loss_mask: 0.5037 (0.5745) loss_objectness: 0.1633 (0.1928) loss_rpn_box_reg: 0.1020 (0.1182) time: 0.4233 (0.4276) data: 0.0056 (0.0068) lr: 0.020000 max mem: 3864
2019-02-26 17:16:29,233 maskrcnn_benchmark.trainer INFO: eta: 10:34:26 iter: 1000 loss: 1.3464 (1.4674) loss_box_reg: 0.1291 (0.1382) loss_classifier: 0.3806 (0.4439) loss_mask: 0.5228 (0.5731) loss_objectness: 0.2111 (0.1935) loss_rpn_box_reg: 0.1093 (0.1187) time: 0.4248 (0.4277) data: 0.0056 (0.0068) lr: 0.020000 max mem: 3864
2019-02-26 17:16:37,822 maskrcnn_benchmark.trainer INFO: eta: 10:34:20 iter: 1020 loss: 1.3272 (1.4658) loss_box_reg: 0.1253 (0.1382) loss_classifier: 0.4189 (0.4435) loss_mask: 0.4823 (0.5718) loss_objectness: 0.1469 (0.1934) loss_rpn_box_reg: 0.0791 (0.1189) time: 0.4237 (0.4277) data: 0.0057 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:16:46,370 maskrcnn_benchmark.trainer INFO: eta: 10:34:11 iter: 1040 loss: 1.2630 (1.4630) loss_box_reg: 0.1341 (0.1382) loss_classifier: 0.3823 (0.4431) loss_mask: 0.4975 (0.5704) loss_objectness: 0.1328 (0.1929) loss_rpn_box_reg: 0.0477 (0.1184) time: 0.4304 (0.4277) data: 0.0054 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:16:54,935 maskrcnn_benchmark.trainer INFO: eta: 10:34:03 iter: 1060 loss: 1.2804 (1.4602) loss_box_reg: 0.1169 (0.1380) loss_classifier: 0.3812 (0.4426) loss_mask: 0.5022 (0.5688) loss_objectness: 0.1761 (0.1926) loss_rpn_box_reg: 0.0875 (0.1182) time: 0.4251 (0.4277) data: 0.0055 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:17:03,540 maskrcnn_benchmark.trainer INFO: eta: 10:33:59 iter: 1080 loss: 1.2288 (1.4568) loss_box_reg: 0.1242 (0.1379) loss_classifier: 0.3886 (0.4416) loss_mask: 0.5065 (0.5677) loss_objectness: 0.1427 (0.1918) loss_rpn_box_reg: 0.0891 (0.1179) time: 0.4249 (0.4278) data: 0.0061 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:17:12,007 maskrcnn_benchmark.trainer INFO: eta: 10:33:43 iter: 1100 loss: 1.2185 (1.4531) loss_box_reg: 0.1241 (0.1379) loss_classifier: 0.4240 (0.4410) loss_mask: 0.4821 (0.5664) loss_objectness: 0.1156 (0.1908) loss_rpn_box_reg: 0.0581 (0.1170) time: 0.4175 (0.4277) data: 0.0055 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:17:20,581 maskrcnn_benchmark.trainer INFO: eta: 10:33:36 iter: 1120 loss: 1.4246 (1.4542) loss_box_reg: 0.1793 (0.1386) loss_classifier: 0.5605 (0.4427) loss_mask: 0.4719 (0.5648) loss_objectness: 0.1326 (0.1909) loss_rpn_box_reg: 0.0755 (0.1172) time: 0.4209 (0.4277) data: 0.0057 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:17:28,886 maskrcnn_benchmark.trainer INFO: eta: 10:33:08 iter: 1140 loss: 1.4450 (1.4559) loss_box_reg: 0.0997 (0.1380) loss_classifier: 0.3191 (0.4413) loss_mask: 0.5860 (0.5654) loss_objectness: 0.2429 (0.1932) loss_rpn_box_reg: 0.0994 (0.1180) time: 0.4155 (0.4275) data: 0.0061 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:17:37,473 maskrcnn_benchmark.trainer INFO: eta: 10:33:02 iter: 1160 loss: 1.4061 (1.4563) loss_box_reg: 0.1493 (0.1381) loss_classifier: 0.4119 (0.4411) loss_mask: 0.5454 (0.5649) loss_objectness: 0.2021 (0.1942) loss_rpn_box_reg: 0.0960 (0.1180) time: 0.4261 (0.4275) data: 0.0067 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:17:46,282 maskrcnn_benchmark.trainer INFO: eta: 10:33:13 iter: 1180 loss: 1.5007 (1.4580) loss_box_reg: 0.1376 (0.1384) loss_classifier: 0.4953 (0.4421) loss_mask: 0.4998 (0.5636) loss_objectness: 0.2325 (0.1953) loss_rpn_box_reg: 0.0798 (0.1187) time: 0.4383 (0.4278) data: 0.0066 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:17:54,777 maskrcnn_benchmark.trainer INFO: eta: 10:33:00 iter: 1200 loss: 1.2480 (1.4555) loss_box_reg: 0.1260 (0.1382) loss_classifier: 0.3598 (0.4415) loss_mask: 0.4896 (0.5625) loss_objectness: 0.1560 (0.1946) loss_rpn_box_reg: 0.0651 (0.1186) time: 0.4270 (0.4277) data: 0.0054 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:18:03,167 maskrcnn_benchmark.trainer INFO: eta: 10:32:40 iter: 1220 loss: 1.2025 (1.4528) loss_box_reg: 0.0793 (0.1376) loss_classifier: 0.3305 (0.4401) loss_mask: 0.4894 (0.5611) loss_objectness: 0.1751 (0.1949) loss_rpn_box_reg: 0.0989 (0.1191) time: 0.4154 (0.4276) data: 0.0047 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:18:11,868 maskrcnn_benchmark.trainer INFO: eta: 10:32:42 iter: 1240 loss: 1.2243 (1.4501) loss_box_reg: 0.1522 (0.1377) loss_classifier: 0.3982 (0.4396) loss_mask: 0.4765 (0.5597) loss_objectness: 0.1556 (0.1943) loss_rpn_box_reg: 0.0775 (0.1188) time: 0.4376 (0.4277) data: 0.0067 (0.0067) lr: 0.020000 max mem: 3864
2019-02-26 17:18:20,398 maskrcnn_benchmark.trainer INFO: eta: 10:32:32 iter: 1260 loss: 1.2548 (1.4471) loss_box_reg: 0.1174 (0.1374) loss_classifier: 0.4012 (0.4390) loss_mask: 0.4803 (0.5584) loss_objectness: 0.1723 (0.1938) loss_rpn_box_reg: 0.1069 (0.1186) time: 0.4257 (0.4277) data: 0.0052 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:18:28,829 maskrcnn_benchmark.trainer INFO: eta: 10:32:15 iter: 1280 loss: 1.2273 (1.4447) loss_box_reg: 0.1025 (0.1369) loss_classifier: 0.3113 (0.4373) loss_mask: 0.4514 (0.5569) loss_objectness: 0.1675 (0.1950) loss_rpn_box_reg: 0.0849 (0.1186) time: 0.4214 (0.4276) data: 0.0054 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:18:37,436 maskrcnn_benchmark.trainer INFO: eta: 10:32:10 iter: 1300 loss: 1.4857 (1.4451) loss_box_reg: 0.1234 (0.1369) loss_classifier: 0.4655 (0.4373) loss_mask: 0.5202 (0.5562) loss_objectness: 0.2525 (0.1960) loss_rpn_box_reg: 0.1304 (0.1187) time: 0.4298 (0.4276) data: 0.0063 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:18:46,186 maskrcnn_benchmark.trainer INFO: eta: 10:32:15 iter: 1320 loss: 1.3633 (1.4442) loss_box_reg: 0.1436 (0.1370) loss_classifier: 0.4294 (0.4373) loss_mask: 0.4542 (0.5549) loss_objectness: 0.1907 (0.1960) loss_rpn_box_reg: 0.1029 (0.1190) time: 0.4361 (0.4278) data: 0.0059 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:18:54,762 maskrcnn_benchmark.trainer INFO: eta: 10:32:07 iter: 1340 loss: 1.4079 (1.4437) loss_box_reg: 0.1469 (0.1372) loss_classifier: 0.4349 (0.4374) loss_mask: 0.4603 (0.5535) loss_objectness: 0.1917 (0.1966) loss_rpn_box_reg: 0.0652 (0.1190) time: 0.4279 (0.4278) data: 0.0059 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:19:03,213 maskrcnn_benchmark.trainer INFO: eta: 10:31:52 iter: 1360 loss: 1.2995 (1.4434) loss_box_reg: 0.1028 (0.1369) loss_classifier: 0.3712 (0.4369) loss_mask: 0.5108 (0.5528) loss_objectness: 0.1985 (0.1975) loss_rpn_box_reg: 0.0962 (0.1194) time: 0.4247 (0.4277) data: 0.0056 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:19:11,803 maskrcnn_benchmark.trainer INFO: eta: 10:31:46 iter: 1380 loss: 1.4132 (1.4445) loss_box_reg: 0.1471 (0.1371) loss_classifier: 0.4354 (0.4375) loss_mask: 0.5293 (0.5526) loss_objectness: 0.2269 (0.1981) loss_rpn_box_reg: 0.0815 (0.1193) time: 0.4236 (0.4277) data: 0.0058 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:19:20,427 maskrcnn_benchmark.trainer INFO: eta: 10:31:42 iter: 1400 loss: 1.3064 (1.4433) loss_box_reg: 0.1210 (0.1370) loss_classifier: 0.3908 (0.4371) loss_mask: 0.4968 (0.5518) loss_objectness: 0.1783 (0.1979) loss_rpn_box_reg: 0.1020 (0.1194) time: 0.4304 (0.4278) data: 0.0055 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:19:29,010 maskrcnn_benchmark.trainer INFO: eta: 10:31:35 iter: 1420 loss: 1.3975 (1.4437) loss_box_reg: 0.1667 (0.1373) loss_classifier: 0.5171 (0.4378) loss_mask: 0.4932 (0.5510) loss_objectness: 0.1848 (0.1979) loss_rpn_box_reg: 0.0899 (0.1196) time: 0.4236 (0.4278) data: 0.0059 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:19:37,510 maskrcnn_benchmark.trainer INFO: eta: 10:31:23 iter: 1440 loss: 1.2908 (1.4423) loss_box_reg: 0.1159 (0.1372) loss_classifier: 0.3702 (0.4374) loss_mask: 0.5021 (0.5504) loss_objectness: 0.1643 (0.1977) loss_rpn_box_reg: 0.0840 (0.1196) time: 0.4215 (0.4278) data: 0.0055 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:19:46,240 maskrcnn_benchmark.trainer INFO: eta: 10:31:25 iter: 1460 loss: 1.1731 (1.4408) loss_box_reg: 0.1261 (0.1372) loss_classifier: 0.3907 (0.4371) loss_mask: 0.4852 (0.5497) loss_objectness: 0.1492 (0.1975) loss_rpn_box_reg: 0.0571 (0.1193) time: 0.4327 (0.4279) data: 0.0053 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:19:54,736 maskrcnn_benchmark.trainer INFO: eta: 10:31:12 iter: 1480 loss: 1.3164 (1.4390) loss_box_reg: 0.1334 (0.1372) loss_classifier: 0.4193 (0.4370) loss_mask: 0.4804 (0.5488) loss_objectness: 0.1129 (0.1969) loss_rpn_box_reg: 0.0593 (0.1190) time: 0.4245 (0.4278) data: 0.0056 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:20:03,445 maskrcnn_benchmark.trainer INFO: eta: 10:31:13 iter: 1500 loss: 1.2690 (1.4378) loss_box_reg: 0.1311 (0.1374) loss_classifier: 0.4358 (0.4370) loss_mask: 0.4710 (0.5479) loss_objectness: 0.1438 (0.1966) loss_rpn_box_reg: 0.0847 (0.1190) time: 0.4331 (0.4279) data: 0.0048 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:20:12,001 maskrcnn_benchmark.trainer INFO: eta: 10:31:04 iter: 1520 loss: 1.1930 (1.4359) loss_box_reg: 0.1343 (0.1373) loss_classifier: 0.3960 (0.4367) loss_mask: 0.4520 (0.5468) loss_objectness: 0.1440 (0.1962) loss_rpn_box_reg: 0.0730 (0.1189) time: 0.4238 (0.4279) data: 0.0055 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:20:20,589 maskrcnn_benchmark.trainer INFO: eta: 10:30:57 iter: 1540 loss: 1.1967 (1.4340) loss_box_reg: 0.1121 (0.1372) loss_classifier: 0.3387 (0.4360) loss_mask: 0.4615 (0.5457) loss_objectness: 0.1794 (0.1960) loss_rpn_box_reg: 0.1046 (0.1191) time: 0.4285 (0.4280) data: 0.0053 (0.0066) lr: 0.020000 max mem: 3864
2019-02-26 17:20:29,409 maskrcnn_benchmark.trainer INFO: eta: 10:31:04 iter: 1560 loss: 1.3761 (1.4336) loss_box_reg: 0.1496 (0.1375) loss_classifier: 0.4617 (0.4369) loss_mask: 0.4641 (0.5447) loss_objectness: 0.1636 (0.1957) loss_rpn_box_reg: 0.1118 (0.1188) time: 0.4344 (0.4281) data: 0.0051 (0.0065) lr: 0.020000 max mem: 3864
2019-02-26 17:20:38,145 maskrcnn_benchmark.trainer INFO: eta: 10:31:05 iter: 1580 loss: 1.3345 (1.4317) loss_box_reg: 0.1276 (0.1375) loss_classifier: 0.4069 (0.4366) loss_mask: 0.4325 (0.5434) loss_objectness: 0.1344 (0.1952) loss_rpn_box_reg: 0.0687 (0.1190) time: 0.4328 (0.4282) data: 0.0055 (0.0065) lr: 0.020000 max mem: 3864
2019-02-26 17:20:46,805 maskrcnn_benchmark.trainer INFO: eta: 10:31:01 iter: 1600 loss: 1.2137 (1.4298) loss_box_reg: 0.1270 (0.1375) loss_classifier: 0.3837 (0.4364) loss_mask: 0.4622 (0.5423) loss_objectness: 0.1298 (0.1946) loss_rpn_box_reg: 0.1024 (0.1189) time: 0.4250 (0.4283) data: 0.0054 (0.0065) lr: 0.020000 max mem: 3864
2019-02-26 17:20:55,327 maskrcnn_benchmark.trainer INFO: eta: 10:30:50 iter: 1620 loss: 1.3413 (1.4282) loss_box_reg: 0.1378 (0.1378) loss_classifier: 0.4504 (0.4365) loss_mask: 0.4446 (0.5413) loss_objectness: 0.1372 (0.1940) loss_rpn_box_reg: 0.0766 (0.1187) time: 0.4298 (0.4283) data: 0.0056 (0.0065) lr: 0.020000 max mem: 3864
2019-02-26 17:21:03,817 maskrcnn_benchmark.trainer INFO: eta: 10:30:38 iter: 1640 loss: 1.2249 (1.4263) loss_box_reg: 0.1306 (0.1378) loss_classifier: 0.3807 (0.4359) loss_mask: 0.4727 (0.5406) loss_objectness: 0.1178 (0.1934) loss_rpn_box_reg: 0.0982 (0.1187) time: 0.4225 (0.4282) data: 0.0063 (0.0065) lr: 0.020000 max mem: 3864
2019-02-26 17:21:12,628 maskrcnn_benchmark.trainer INFO: eta: 10:30:42 iter: 1660 loss: 1.2954 (1.4252) loss_box_reg: 0.1398 (0.1380) loss_classifier: 0.3983 (0.4359) loss_mask: 0.4588 (0.5398) loss_objectness: 0.1494 (0.1931) loss_rpn_box_reg: 0.0807 (0.1184) time: 0.4320 (0.4284) data: 0.0052 (0.0065) lr: 0.020000 max mem: 3905
2019-02-26 17:21:21,316 maskrcnn_benchmark.trainer INFO: eta: 10:30:40 iter: 1680 loss: 1.2170 (1.4231) loss_box_reg: 0.1560 (0.1382) loss_classifier: 0.3954 (0.4359) loss_mask: 0.4583 (0.5389) loss_objectness: 0.1130 (0.1922) loss_rpn_box_reg: 0.0712 (0.1179) time: 0.4339 (0.4284) data: 0.0061 (0.0065) lr: 0.020000 max mem: 3905
2019-02-26 17:21:29,977 maskrcnn_benchmark.trainer INFO: eta: 10:30:36 iter: 1700 loss: 1.1095 (1.4206) loss_box_reg: 0.1298 (0.1383) loss_classifier: 0.3491 (0.4355) loss_mask: 0.4635 (0.5377) loss_objectness: 0.1127 (0.1915) loss_rpn_box_reg: 0.0646 (0.1177) time: 0.4301 (0.4285) data: 0.0055 (0.0065) lr: 0.020000 max mem: 3905
2019-02-26 17:21:38,718 maskrcnn_benchmark.trainer INFO: eta: 10:30:36 iter: 1720 loss: 1.3470 (1.4199) loss_box_reg: 0.1353 (0.1385) loss_classifier: 0.4432 (0.4357) loss_mask: 0.4690 (0.5370) loss_objectness: 0.1378 (0.1911) loss_rpn_box_reg: 0.1072 (0.1176) time: 0.4304 (0.4286) data: 0.0064 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:21:47,254 maskrcnn_benchmark.trainer INFO: eta: 10:30:26 iter: 1740 loss: 1.2910 (1.4187) loss_box_reg: 0.1477 (0.1387) loss_classifier: 0.4296 (0.4354) loss_mask: 0.4701 (0.5363) loss_objectness: 0.1538 (0.1907) loss_rpn_box_reg: 0.0848 (0.1176) time: 0.4306 (0.4286) data: 0.0057 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:21:55,967 maskrcnn_benchmark.trainer INFO: eta: 10:30:25 iter: 1760 loss: 1.3535 (1.4178) loss_box_reg: 0.1448 (0.1390) loss_classifier: 0.4318 (0.4357) loss_mask: 0.4684 (0.5356) loss_objectness: 0.1414 (0.1903) loss_rpn_box_reg: 0.0713 (0.1172) time: 0.4358 (0.4287) data: 0.0063 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:22:04,481 maskrcnn_benchmark.trainer INFO: eta: 10:30:13 iter: 1780 loss: 1.1757 (1.4161) loss_box_reg: 0.1359 (0.1390) loss_classifier: 0.3770 (0.4353) loss_mask: 0.4548 (0.5347) loss_objectness: 0.1183 (0.1899) loss_rpn_box_reg: 0.0781 (0.1172) time: 0.4265 (0.4286) data: 0.0058 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:22:13,102 maskrcnn_benchmark.trainer INFO: eta: 10:30:07 iter: 1800 loss: 1.2308 (1.4140) loss_box_reg: 0.1467 (0.1392) loss_classifier: 0.4157 (0.4355) loss_mask: 0.4311 (0.5337) loss_objectness: 0.1013 (0.1891) loss_rpn_box_reg: 0.0467 (0.1166) time: 0.4285 (0.4287) data: 0.0053 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:22:22,111 maskrcnn_benchmark.trainer INFO: eta: 10:30:19 iter: 1820 loss: 1.3043 (1.4130) loss_box_reg: 0.1610 (0.1395) loss_classifier: 0.4489 (0.4358) loss_mask: 0.4203 (0.5326) loss_objectness: 0.1358 (0.1888) loss_rpn_box_reg: 0.0875 (0.1163) time: 0.4496 (0.4289) data: 0.0048 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:22:30,680 maskrcnn_benchmark.trainer INFO: eta: 10:30:10 iter: 1840 loss: 1.1318 (1.4107) loss_box_reg: 0.1365 (0.1394) loss_classifier: 0.3906 (0.4355) loss_mask: 0.4264 (0.5315) loss_objectness: 0.1504 (0.1884) loss_rpn_box_reg: 0.0581 (0.1158) time: 0.4288 (0.4289) data: 0.0053 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:22:39,311 maskrcnn_benchmark.trainer INFO: eta: 10:30:04 iter: 1860 loss: 1.3085 (1.4090) loss_box_reg: 0.1315 (0.1395) loss_classifier: 0.3575 (0.4355) loss_mask: 0.4280 (0.5304) loss_objectness: 0.1137 (0.1879) loss_rpn_box_reg: 0.0817 (0.1158) time: 0.4354 (0.4289) data: 0.0053 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:22:48,037 maskrcnn_benchmark.trainer INFO: eta: 10:30:03 iter: 1880 loss: 1.1772 (1.4074) loss_box_reg: 0.1393 (0.1395) loss_classifier: 0.4343 (0.4355) loss_mask: 0.4052 (0.5292) loss_objectness: 0.1280 (0.1875) loss_rpn_box_reg: 0.0632 (0.1157) time: 0.4332 (0.4290) data: 0.0053 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:22:56,757 maskrcnn_benchmark.trainer INFO: eta: 10:30:01 iter: 1900 loss: 1.2466 (1.4063) loss_box_reg: 0.1303 (0.1395) loss_classifier: 0.4011 (0.4352) loss_mask: 0.4651 (0.5285) loss_objectness: 0.1633 (0.1872) loss_rpn_box_reg: 0.0960 (0.1159) time: 0.4420 (0.4291) data: 0.0058 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:23:05,293 maskrcnn_benchmark.trainer INFO: eta: 10:29:50 iter: 1920 loss: 1.2715 (1.4053) loss_box_reg: 0.1412 (0.1396) loss_classifier: 0.4135 (0.4352) loss_mask: 0.4286 (0.5277) loss_objectness: 0.1327 (0.1870) loss_rpn_box_reg: 0.0822 (0.1159) time: 0.4246 (0.4290) data: 0.0053 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:23:13,886 maskrcnn_benchmark.trainer INFO: eta: 10:29:42 iter: 1940 loss: 1.2664 (1.4040) loss_box_reg: 0.1303 (0.1396) loss_classifier: 0.3876 (0.4349) loss_mask: 0.4344 (0.5268) loss_objectness: 0.1255 (0.1868) loss_rpn_box_reg: 0.0749 (0.1160) time: 0.4251 (0.4291) data: 0.0049 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:23:22,432 maskrcnn_benchmark.trainer INFO: eta: 10:29:32 iter: 1960 loss: 1.1611 (1.4024) loss_box_reg: 0.1152 (0.1394) loss_classifier: 0.3759 (0.4345) loss_mask: 0.4430 (0.5260) loss_objectness: 0.1297 (0.1868) loss_rpn_box_reg: 0.0647 (0.1157) time: 0.4254 (0.4290) data: 0.0054 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:23:30,988 maskrcnn_benchmark.trainer INFO: eta: 10:29:22 iter: 1980 loss: 1.2337 (1.4010) loss_box_reg: 0.1516 (0.1395) loss_classifier: 0.3918 (0.4342) loss_mask: 0.4332 (0.5251) loss_objectness: 0.1537 (0.1865) loss_rpn_box_reg: 0.0631 (0.1156) time: 0.4219 (0.4290) data: 0.0054 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:23:39,652 maskrcnn_benchmark.trainer INFO: eta: 10:29:17 iter: 2000 loss: 1.2162 (1.3999) loss_box_reg: 0.1501 (0.1396) loss_classifier: 0.3948 (0.4340) loss_mask: 0.4362 (0.5243) loss_objectness: 0.1580 (0.1864) loss_rpn_box_reg: 0.1052 (0.1156) time: 0.4338 (0.4291) data: 0.0063 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:23:48,304 maskrcnn_benchmark.trainer INFO: eta: 10:29:12 iter: 2020 loss: 1.1554 (1.3981) loss_box_reg: 0.1233 (0.1395) loss_classifier: 0.3847 (0.4338) loss_mask: 0.4169 (0.5235) loss_objectness: 0.0789 (0.1858) loss_rpn_box_reg: 0.0423 (0.1153) time: 0.4262 (0.4291) data: 0.0048 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:23:56,958 maskrcnn_benchmark.trainer INFO: eta: 10:29:06 iter: 2040 loss: 1.2636 (1.3971) loss_box_reg: 0.1409 (0.1397) loss_classifier: 0.4151 (0.4341) loss_mask: 0.4560 (0.5230) loss_objectness: 0.1140 (0.1854) loss_rpn_box_reg: 0.0713 (0.1150) time: 0.4355 (0.4291) data: 0.0055 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:24:05,528 maskrcnn_benchmark.trainer INFO: eta: 10:28:57 iter: 2060 loss: 1.2296 (1.3966) loss_box_reg: 0.1409 (0.1398) loss_classifier: 0.3867 (0.4342) loss_mask: 0.4588 (0.5224) loss_objectness: 0.1147 (0.1851) loss_rpn_box_reg: 0.1181 (0.1152) time: 0.4342 (0.4291) data: 0.0060 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:24:14,038 maskrcnn_benchmark.trainer INFO: eta: 10:28:45 iter: 2080 loss: 1.0696 (1.3942) loss_box_reg: 0.1128 (0.1397) loss_classifier: 0.3397 (0.4338) loss_mask: 0.3951 (0.5212) loss_objectness: 0.1140 (0.1846) loss_rpn_box_reg: 0.0685 (0.1150) time: 0.4138 (0.4291) data: 0.0054 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:24:22,722 maskrcnn_benchmark.trainer INFO: eta: 10:28:41 iter: 2100 loss: 1.2925 (1.3935) loss_box_reg: 0.1302 (0.1397) loss_classifier: 0.4321 (0.4338) loss_mask: 0.4581 (0.5206) loss_objectness: 0.1502 (0.1843) loss_rpn_box_reg: 0.0924 (0.1151) time: 0.4282 (0.4291) data: 0.0059 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:24:31,341 maskrcnn_benchmark.trainer INFO: eta: 10:28:34 iter: 2120 loss: 1.2634 (1.3929) loss_box_reg: 0.1394 (0.1398) loss_classifier: 0.3865 (0.4338) loss_mask: 0.4183 (0.5197) loss_objectness: 0.1877 (0.1845) loss_rpn_box_reg: 0.0833 (0.1150) time: 0.4293 (0.4292) data: 0.0065 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:24:40,057 maskrcnn_benchmark.trainer INFO: eta: 10:28:31 iter: 2140 loss: 1.3331 (1.3926) loss_box_reg: 0.1605 (0.1400) loss_classifier: 0.4805 (0.4344) loss_mask: 0.4328 (0.5189) loss_objectness: 0.1202 (0.1844) loss_rpn_box_reg: 0.0627 (0.1148) time: 0.4334 (0.4292) data: 0.0053 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:24:48,602 maskrcnn_benchmark.trainer INFO: eta: 10:28:21 iter: 2160 loss: 1.1041 (1.3904) loss_box_reg: 0.1092 (0.1399) loss_classifier: 0.3395 (0.4338) loss_mask: 0.3951 (0.5181) loss_objectness: 0.1433 (0.1841) loss_rpn_box_reg: 0.0744 (0.1146) time: 0.4253 (0.4292) data: 0.0054 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:24:57,201 maskrcnn_benchmark.trainer INFO: eta: 10:28:13 iter: 2180 loss: 1.2180 (1.3889) loss_box_reg: 0.1289 (0.1399) loss_classifier: 0.3959 (0.4338) loss_mask: 0.3895 (0.5170) loss_objectness: 0.1505 (0.1837) loss_rpn_box_reg: 0.0715 (0.1145) time: 0.4220 (0.4292) data: 0.0051 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:25:05,743 maskrcnn_benchmark.trainer INFO: eta: 10:28:02 iter: 2200 loss: 1.1523 (1.3877) loss_box_reg: 0.1113 (0.1398) loss_classifier: 0.3245 (0.4333) loss_mask: 0.4370 (0.5165) loss_objectness: 0.1553 (0.1837) loss_rpn_box_reg: 0.0687 (0.1143) time: 0.4208 (0.4292) data: 0.0052 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:25:14,636 maskrcnn_benchmark.trainer INFO: eta: 10:28:06 iter: 2220 loss: 1.2908 (1.3870) loss_box_reg: 0.1573 (0.1400) loss_classifier: 0.4257 (0.4335) loss_mask: 0.4367 (0.5158) loss_objectness: 0.1591 (0.1835) loss_rpn_box_reg: 0.0922 (0.1142) time: 0.4430 (0.4293) data: 0.0056 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:25:23,240 maskrcnn_benchmark.trainer INFO: eta: 10:27:58 iter: 2240 loss: 1.2713 (1.3864) loss_box_reg: 0.1092 (0.1399) loss_classifier: 0.3490 (0.4332) loss_mask: 0.4316 (0.5151) loss_objectness: 0.1549 (0.1839) loss_rpn_box_reg: 0.0842 (0.1143) time: 0.4245 (0.4293) data: 0.0051 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:25:31,659 maskrcnn_benchmark.trainer INFO: eta: 10:27:43 iter: 2260 loss: 1.1338 (1.3851) loss_box_reg: 0.1209 (0.1398) loss_classifier: 0.3209 (0.4327) loss_mask: 0.4496 (0.5145) loss_objectness: 0.1409 (0.1839) loss_rpn_box_reg: 0.0662 (0.1142) time: 0.4184 (0.4293) data: 0.0050 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:25:40,401 maskrcnn_benchmark.trainer INFO: eta: 10:27:41 iter: 2280 loss: 1.2111 (1.3839) loss_box_reg: 0.1561 (0.1399) loss_classifier: 0.4381 (0.4328) loss_mask: 0.4322 (0.5138) loss_objectness: 0.1201 (0.1836) loss_rpn_box_reg: 0.0723 (0.1139) time: 0.4362 (0.4293) data: 0.0051 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:25:48,955 maskrcnn_benchmark.trainer INFO: eta: 10:27:31 iter: 2300 loss: 1.1680 (1.3831) loss_box_reg: 0.1407 (0.1399) loss_classifier: 0.4445 (0.4329) loss_mask: 0.4232 (0.5131) loss_objectness: 0.1413 (0.1834) loss_rpn_box_reg: 0.0690 (0.1137) time: 0.4211 (0.4293) data: 0.0049 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:25:57,718 maskrcnn_benchmark.trainer INFO: eta: 10:27:29 iter: 2320 loss: 1.2972 (1.3824) loss_box_reg: 0.1465 (0.1401) loss_classifier: 0.4755 (0.4332) loss_mask: 0.4272 (0.5124) loss_objectness: 0.1231 (0.1831) loss_rpn_box_reg: 0.0892 (0.1137) time: 0.4375 (0.4294) data: 0.0056 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:26:06,246 maskrcnn_benchmark.trainer INFO: eta: 10:27:18 iter: 2340 loss: 1.2279 (1.3815) loss_box_reg: 0.1429 (0.1401) loss_classifier: 0.3905 (0.4331) loss_mask: 0.4083 (0.5116) loss_objectness: 0.1312 (0.1829) loss_rpn_box_reg: 0.1218 (0.1137) time: 0.4226 (0.4294) data: 0.0059 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:26:15,000 maskrcnn_benchmark.trainer INFO: eta: 10:27:16 iter: 2360 loss: 1.2377 (1.3807) loss_box_reg: 0.1238 (0.1402) loss_classifier: 0.4090 (0.4332) loss_mask: 0.4124 (0.5109) loss_objectness: 0.1363 (0.1825) loss_rpn_box_reg: 0.1101 (0.1138) time: 0.4428 (0.4294) data: 0.0060 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:26:23,839 maskrcnn_benchmark.trainer INFO: eta: 10:27:16 iter: 2380 loss: 1.3229 (1.3803) loss_box_reg: 0.1671 (0.1404) loss_classifier: 0.4664 (0.4336) loss_mask: 0.4368 (0.5102) loss_objectness: 0.1357 (0.1822) loss_rpn_box_reg: 0.0954 (0.1139) time: 0.4416 (0.4295) data: 0.0056 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:26:32,543 maskrcnn_benchmark.trainer INFO: eta: 10:27:12 iter: 2400 loss: 1.0175 (1.3778) loss_box_reg: 0.0945 (0.1402) loss_classifier: 0.2716 (0.4326) loss_mask: 0.4106 (0.5094) loss_objectness: 0.1449 (0.1820) loss_rpn_box_reg: 0.0519 (0.1135) time: 0.4305 (0.4296) data: 0.0054 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:26:41,202 maskrcnn_benchmark.trainer INFO: eta: 10:27:06 iter: 2420 loss: 1.0719 (1.3758) loss_box_reg: 0.1268 (0.1401) loss_classifier: 0.3440 (0.4320) loss_mask: 0.3855 (0.5084) loss_objectness: 0.1369 (0.1817) loss_rpn_box_reg: 0.0934 (0.1136) time: 0.4361 (0.4296) data: 0.0053 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:26:49,715 maskrcnn_benchmark.trainer INFO: eta: 10:26:54 iter: 2440 loss: 1.1324 (1.3741) loss_box_reg: 0.1161 (0.1399) loss_classifier: 0.3286 (0.4311) loss_mask: 0.4030 (0.5076) loss_objectness: 0.1258 (0.1817) loss_rpn_box_reg: 0.1205 (0.1138) time: 0.4252 (0.4296) data: 0.0052 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:26:58,181 maskrcnn_benchmark.trainer INFO: eta: 10:26:41 iter: 2460 loss: 1.2234 (1.3730) loss_box_reg: 0.1165 (0.1398) loss_classifier: 0.3666 (0.4308) loss_mask: 0.4082 (0.5069) loss_objectness: 0.1700 (0.1816) loss_rpn_box_reg: 0.1119 (0.1138) time: 0.4190 (0.4295) data: 0.0066 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:27:06,514 maskrcnn_benchmark.trainer INFO: eta: 10:26:23 iter: 2480 loss: 1.1603 (1.3722) loss_box_reg: 0.0851 (0.1395) loss_classifier: 0.3022 (0.4299) loss_mask: 0.4205 (0.5064) loss_objectness: 0.2114 (0.1822) loss_rpn_box_reg: 0.0883 (0.1142) time: 0.4087 (0.4294) data: 0.0047 (0.0064) lr: 0.020000 max mem: 3982
2019-02-26 17:27:14,899 maskrcnn_benchmark.trainer INFO: eta: 10:26:08 iter: 2500 loss: 0.9583 (1.3694) loss_box_reg: 0.0770 (0.1391) loss_classifier: 0.2717 (0.4289) loss_mask: 0.4067 (0.5057) loss_objectness: 0.1255 (0.1819) loss_rpn_box_reg: 0.0509 (0.1138) time: 0.4158 (0.4294) data: 0.0049 (0.0063) lr: 0.020000 max mem: 3982
2019-02-26 17:27:14,900 maskrcnn_benchmark.utils.checkpoint INFO: Saving checkpoint to runs/COCO_2014/model_0002500.pth
2019-02-26 17:27:23,608 maskrcnn_benchmark.trainer INFO: eta: 10:26:03 iter: 2520 loss: 1.0898 (1.3679) loss_box_reg: 0.1013 (0.1389) loss_classifier: 0.3159 (0.4283) loss_mask: 0.4433 (0.5053) loss_objectness: 0.1218 (0.1817) loss_rpn_box_reg: 0.0935 (0.1137) time: 0.4211 (0.4294) data: 0.0059 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:27:32,294 maskrcnn_benchmark.trainer INFO: eta: 10:25:58 iter: 2540 loss: 1.1555 (1.3669) loss_box_reg: 0.1133 (0.1388) loss_classifier: 0.3558 (0.4279) loss_mask: 0.4400 (0.5048) loss_objectness: 0.1302 (0.1816) loss_rpn_box_reg: 0.0912 (0.1138) time: 0.4375 (0.4294) data: 0.0050 (0.0065) lr: 0.020000 max mem: 3982
2019-02-26 17:27:41,015 maskrcnn_benchmark.trainer INFO: eta: 10:25:54 iter: 2560 loss: 1.3751 (1.3678) loss_box_reg: 0.1332 (0.1388) loss_classifier: 0.4167 (0.4280) loss_mask: 0.4432 (0.5044) loss_objectness: 0.2054 (0.1824) loss_rpn_box_reg: 0.1001 (0.1142) time: 0.4275 (0.4295) data: 0.0058 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:27:49,615 maskrcnn_benchmark.trainer INFO: eta: 10:25:46 iter: 2580 loss: 1.3545 (1.3678) loss_box_reg: 0.0942 (0.1385) loss_classifier: 0.3331 (0.4274) loss_mask: 0.4678 (0.5042) loss_objectness: 0.2151 (0.1829) loss_rpn_box_reg: 0.1131 (0.1148) time: 0.4283 (0.4295) data: 0.0051 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:27:57,956 maskrcnn_benchmark.trainer INFO: eta: 10:25:29 iter: 2600 loss: 1.5580 (nan) loss_box_reg: 0.0928 (nan) loss_classifier: 0.4651 (nan) loss_mask: 0.5728 (inf) loss_objectness: 0.3759 (307661464585.6659) loss_rpn_box_reg: 0.0891 (26815601856.3370) time: 0.4150 (0.4294) data: 0.0058 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:28:05,402 maskrcnn_benchmark.trainer INFO: eta: 10:24:42 iter: 2620 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: 0.6889 (inf) loss_objectness: 0.3525 (305312903787.3050) loss_rpn_box_reg: 0.0747 (26610902605.5261) time: 0.3740 (0.4290) data: 0.0060 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:28:12,908 maskrcnn_benchmark.trainer INFO: eta: 10:23:58 iter: 2640 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: 0.6868 (inf) loss_objectness: 0.3651 (302999927243.4648) loss_rpn_box_reg: 0.0858 (26409304858.5153) time: 0.3728 (0.4286) data: 0.0053 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:28:20,425 maskrcnn_benchmark.trainer INFO: eta: 10:23:15 iter: 2660 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: 0.6851 (inf) loss_objectness: 0.3720 (300721732301.7876) loss_rpn_box_reg: 0.0728 (26210738656.5726) time: 0.3766 (0.4282) data: 0.0051 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:28:27,833 maskrcnn_benchmark.trainer INFO: eta: 10:22:29 iter: 2680 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: 0.6821 (inf) loss_objectness: 0.4124 (298477540269.6880) loss_rpn_box_reg: 0.0958 (26015136129.2858) time: 0.3713 (0.4277) data: 0.0064 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:28:35,284 maskrcnn_benchmark.trainer INFO: eta: 10:21:44 iter: 2700 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: 0.6800 (nan) loss_objectness: 0.3217 (296266595526.9524) loss_rpn_box_reg: 0.0602 (25822431417.2176) time: 0.3700 (0.4273) data: 0.0054 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:28:42,798 maskrcnn_benchmark.trainer INFO: eta: 10:21:03 iter: 2720 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3776 (294088164677.4924) loss_rpn_box_reg: 0.0794 (25632560597.9741) time: 0.3711 (0.4269) data: 0.0054 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:28:50,315 maskrcnn_benchmark.trainer INFO: eta: 10:20:22 iter: 2740 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4144 (291941535738.2438) loss_rpn_box_reg: 0.1166 (25445461615.5080) time: 0.3734 (0.4266) data: 0.0067 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:28:57,819 maskrcnn_benchmark.trainer INFO: eta: 10:19:41 iter: 2760 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4245 (289826017363.3324) loss_rpn_box_reg: 0.1540 (25261074212.4983) time: 0.3747 (0.4262) data: 0.0062 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:29:05,355 maskrcnn_benchmark.trainer INFO: eta: 10:19:01 iter: 2780 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3568 (287740938101.7286) loss_rpn_box_reg: 0.0751 (25079339865.6466) time: 0.3772 (0.4258) data: 0.0063 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:29:12,848 maskrcnn_benchmark.trainer INFO: eta: 10:18:21 iter: 2800 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4129 (285685645686.7193) loss_rpn_box_reg: 0.1124 (24900201723.7501) time: 0.3726 (0.4255) data: 0.0061 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:29:20,426 maskrcnn_benchmark.trainer INFO: eta: 10:17:43 iter: 2820 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3769 (283659506355.6106) loss_rpn_box_reg: 0.0893 (24723604548.4051) time: 0.3761 (0.4251) data: 0.0049 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:29:28,009 maskrcnn_benchmark.trainer INFO: eta: 10:17:07 iter: 2840 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3484 (281661904198.1794) loss_rpn_box_reg: 0.0904 (24549494657.2199) time: 0.3753 (0.4248) data: 0.0048 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:29:35,674 maskrcnn_benchmark.trainer INFO: eta: 10:16:33 iter: 2860 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4038 (279692240532.4607) loss_rpn_box_reg: 0.0884 (24377819869.4081) time: 0.3843 (0.4245) data: 0.0047 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:29:43,326 maskrcnn_benchmark.trainer INFO: eta: 10:15:59 iter: 2880 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3768 (277749933306.5437) loss_rpn_box_reg: 0.0703 (24208529453.6491) time: 0.3826 (0.4242) data: 0.0052 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:29:50,797 maskrcnn_benchmark.trainer INFO: eta: 10:15:20 iter: 2900 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4537 (275834416525.1224) loss_rpn_box_reg: 0.1260 (24041574078.1077) time: 0.3712 (0.4239) data: 0.0060 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:29:58,449 maskrcnn_benchmark.trainer INFO: eta: 10:14:47 iter: 2920 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3712 (273945139699.6107) loss_rpn_box_reg: 0.0767 (23876905762.5050) time: 0.3781 (0.4236) data: 0.0059 (0.0065) lr: 0.020000 max mem: 4106
2019-02-26 17:30:06,111 maskrcnn_benchmark.trainer INFO: eta: 10:14:15 iter: 2940 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3691 (272081567320.7045) loss_rpn_box_reg: 0.1002 (23714477832.1486) time: 0.3827 (0.4233) data: 0.0056 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:30:13,621 maskrcnn_benchmark.trainer INFO: eta: 10:13:38 iter: 2960 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3441 (270243178352.3239) loss_rpn_box_reg: 0.0650 (23554244873.8239) time: 0.3774 (0.4230) data: 0.0055 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:30:21,093 maskrcnn_benchmark.trainer INFO: eta: 10:13:01 iter: 2980 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.2654 (268429465745.9349) loss_rpn_box_reg: 0.0583 (23396162693.4633) time: 0.3732 (0.4227) data: 0.0049 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:30:28,704 maskrcnn_benchmark.trainer INFO: eta: 10:12:28 iter: 3000 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3353 (266639935974.2978) loss_rpn_box_reg: 0.0730 (23240188275.5075) time: 0.3801 (0.4224) data: 0.0058 (0.0064) lr: 0.020000 max mem: 4106
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2019-02-26 17:30:43,918 maskrcnn_benchmark.trainer INFO: eta: 10:11:23 iter: 3040 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3367 (263131515764.1143) loss_rpn_box_reg: 0.0647 (22934396324.5151) time: 0.3825 (0.4218) data: 0.0046 (0.0064) lr: 0.020000 max mem: 4106
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2019-02-26 17:31:52,270 maskrcnn_benchmark.trainer INFO: eta: 10:06:43 iter: 3220 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3749 (248422300597.1983) loss_rpn_box_reg: 0.0788 (21652349325.0143) time: 0.3763 (0.4195) data: 0.0059 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:31:59,919 maskrcnn_benchmark.trainer INFO: eta: 10:06:14 iter: 3240 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3578 (246888829605.8598) loss_rpn_box_reg: 0.0692 (21518692847.6999) time: 0.3799 (0.4193) data: 0.0055 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:32:07,238 maskrcnn_benchmark.trainer INFO: eta: 10:05:38 iter: 3260 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4108 (245374174209.5074) loss_rpn_box_reg: 0.0961 (21386676327.1626) time: 0.3661 (0.4189) data: 0.0049 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:32:14,917 maskrcnn_benchmark.trainer INFO: eta: 10:05:11 iter: 3280 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3518 (243877990220.4272) loss_rpn_box_reg: 0.0875 (21256269764.1927) time: 0.3816 (0.4187) data: 0.0053 (0.0064) lr: 0.020000 max mem: 4106
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2019-02-26 17:32:30,248 maskrcnn_benchmark.trainer INFO: eta: 10:04:17 iter: 3320 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3922 (240939701181.6317) loss_rpn_box_reg: 0.0687 (21000170128.4810) time: 0.3746 (0.4183) data: 0.0055 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:32:37,809 maskrcnn_benchmark.trainer INFO: eta: 10:03:48 iter: 3340 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3958 (239496948479.9476) loss_rpn_box_reg: 0.0817 (20874420606.7542) time: 0.3769 (0.4180) data: 0.0054 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:32:45,460 maskrcnn_benchmark.trainer INFO: eta: 10:03:21 iter: 3360 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4066 (238071371405.6648) loss_rpn_box_reg: 0.1282 (20750168103.1435) time: 0.3819 (0.4178) data: 0.0053 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:32:53,117 maskrcnn_benchmark.trainer INFO: eta: 10:02:55 iter: 3380 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4281 (236662665065.9890) loss_rpn_box_reg: 0.1070 (20627386043.3625) time: 0.3844 (0.4176) data: 0.0064 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:33:00,854 maskrcnn_benchmark.trainer INFO: eta: 10:02:31 iter: 3400 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4102 (235270531742.0740) loss_rpn_box_reg: 0.1318 (20506048478.4023) time: 0.3861 (0.4175) data: 0.0049 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:33:08,405 maskrcnn_benchmark.trainer INFO: eta: 10:02:02 iter: 3420 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3610 (233894680679.2570) loss_rpn_box_reg: 0.0657 (20386130066.2484) time: 0.3708 (0.4172) data: 0.0057 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:33:15,946 maskrcnn_benchmark.trainer INFO: eta: 10:01:34 iter: 3440 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3631 (232534827884.6124) loss_rpn_box_reg: 0.0687 (20267606054.2358) time: 0.3757 (0.4170) data: 0.0061 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:33:23,505 maskrcnn_benchmark.trainer INFO: eta: 10:01:06 iter: 3460 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3793 (231190695931.5244) loss_rpn_box_reg: 0.0617 (20150452262.0154) time: 0.3738 (0.4168) data: 0.0058 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:33:31,197 maskrcnn_benchmark.trainer INFO: eta: 10:00:41 iter: 3480 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4030 (229862013771.0008) loss_rpn_box_reg: 0.0936 (20034645065.1079) time: 0.3812 (0.4166) data: 0.0057 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:33:38,834 maskrcnn_benchmark.trainer INFO: eta: 10:00:16 iter: 3500 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3281 (228548516549.4544) loss_rpn_box_reg: 0.0767 (19920161379.0222) time: 0.3819 (0.4164) data: 0.0055 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:33:46,576 maskrcnn_benchmark.trainer INFO: eta: 9:59:53 iter: 3520 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4172 (227249945432.6985) loss_rpn_box_reg: 0.1132 (19806978643.9148) time: 0.3860 (0.4162) data: 0.0058 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:33:54,233 maskrcnn_benchmark.trainer INFO: eta: 9:59:29 iter: 3540 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4127 (225966047435.9061) loss_rpn_box_reg: 0.1205 (19695074809.7692) time: 0.3837 (0.4160) data: 0.0055 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:34:01,811 maskrcnn_benchmark.trainer INFO: eta: 9:59:02 iter: 3560 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3658 (224696575259.3020) loss_rpn_box_reg: 0.0798 (19584428322.0745) time: 0.3780 (0.4158) data: 0.0058 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:34:09,433 maskrcnn_benchmark.trainer INFO: eta: 9:58:37 iter: 3580 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4528 (223441287129.3643) loss_rpn_box_reg: 0.1134 (19475018107.9854) time: 0.3794 (0.4156) data: 0.0058 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:34:17,008 maskrcnn_benchmark.trainer INFO: eta: 9:58:11 iter: 3600 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3201 (222199946645.3144) loss_rpn_box_reg: 0.0768 (19366823562.9417) time: 0.3751 (0.4154) data: 0.0055 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:34:24,429 maskrcnn_benchmark.trainer INFO: eta: 9:57:42 iter: 3620 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3743 (220972322630.7017) loss_rpn_box_reg: 0.0871 (19259824537.7328) time: 0.3731 (0.4152) data: 0.0061 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:34:31,919 maskrcnn_benchmark.trainer INFO: eta: 9:57:14 iter: 3640 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3978 (219758188989.8757) loss_rpn_box_reg: 0.0909 (19154001325.9876) time: 0.3736 (0.4149) data: 0.0053 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:34:39,427 maskrcnn_benchmark.trainer INFO: eta: 9:56:47 iter: 3660 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4019 (218557324569.1683) loss_rpn_box_reg: 0.0900 (19049334652.0757) time: 0.3754 (0.4147) data: 0.0061 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:34:47,026 maskrcnn_benchmark.trainer INFO: eta: 9:56:22 iter: 3680 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3657 (217369513022.5988) loss_rpn_box_reg: 0.0791 (18945805659.4019) time: 0.3767 (0.4145) data: 0.0060 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:34:54,597 maskrcnn_benchmark.trainer INFO: eta: 9:55:57 iter: 3700 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3432 (216194542681.9382) loss_rpn_box_reg: 0.0739 (18843395899.0814) time: 0.3779 (0.4143) data: 0.0060 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:35:02,231 maskrcnn_benchmark.trainer INFO: eta: 9:55:34 iter: 3720 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4206 (215032206430.9622) loss_rpn_box_reg: 0.1267 (18742087318.9797) time: 0.3813 (0.4142) data: 0.0049 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:35:09,906 maskrcnn_benchmark.trainer INFO: eta: 9:55:12 iter: 3740 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.2774 (213882301583.7395) loss_rpn_box_reg: 0.0556 (18641862253.1032) time: 0.3811 (0.4140) data: 0.0052 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:35:17,406 maskrcnn_benchmark.trainer INFO: eta: 9:54:45 iter: 3760 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3127 (212744629766.8066) loss_rpn_box_reg: 0.0675 (18542703411.3318) time: 0.3745 (0.4138) data: 0.0053 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:35:25,038 maskrcnn_benchmark.trainer INFO: eta: 9:54:23 iter: 3780 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3378 (211618996805.0794) loss_rpn_box_reg: 0.0641 (18444593869.4734) time: 0.3827 (0.4136) data: 0.0057 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:35:32,679 maskrcnn_benchmark.trainer INFO: eta: 9:54:00 iter: 3800 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4255 (210505212611.3707) loss_rpn_box_reg: 0.1055 (18347517059.6348) time: 0.3813 (0.4135) data: 0.0053 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:35:40,289 maskrcnn_benchmark.trainer INFO: eta: 9:53:37 iter: 3820 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4328 (209403091079.3762) loss_rpn_box_reg: 0.1005 (18251456760.8938) time: 0.3764 (0.4133) data: 0.0069 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:35:47,805 maskrcnn_benchmark.trainer INFO: eta: 9:53:12 iter: 3840 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3726 (208312449980.0064) loss_rpn_box_reg: 0.0868 (18156397090.2648) time: 0.3742 (0.4131) data: 0.0055 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:35:55,394 maskrcnn_benchmark.trainer INFO: eta: 9:52:48 iter: 3860 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3330 (207233110860.9410) loss_rpn_box_reg: 0.0739 (18062322493.9427) time: 0.3771 (0.4129) data: 0.0049 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:36:03,062 maskrcnn_benchmark.trainer INFO: eta: 9:52:27 iter: 3880 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4016 (206164898949.2888) loss_rpn_box_reg: 0.0816 (17969217738.8198) time: 0.3832 (0.4128) data: 0.0061 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:36:10,728 maskrcnn_benchmark.trainer INFO: eta: 9:52:06 iter: 3900 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4297 (205107643057.2433) loss_rpn_box_reg: 0.1248 (17877067904.2625) time: 0.3807 (0.4126) data: 0.0061 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:36:18,251 maskrcnn_benchmark.trainer INFO: eta: 9:51:42 iter: 3920 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3443 (204061175490.6265) loss_rpn_box_reg: 0.0621 (17785858374.1392) time: 0.3767 (0.4124) data: 0.0057 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:36:25,910 maskrcnn_benchmark.trainer INFO: eta: 9:51:20 iter: 3940 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3553 (203025331960.2194) loss_rpn_box_reg: 0.0730 (17695574829.0935) time: 0.3812 (0.4123) data: 0.0058 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:36:33,531 maskrcnn_benchmark.trainer INFO: eta: 9:50:59 iter: 3960 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3868 (201999951495.7758) loss_rpn_box_reg: 0.0989 (17606203239.0482) time: 0.3779 (0.4121) data: 0.0062 (0.0063) lr: 0.020000 max mem: 4106
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2019-02-26 17:36:48,835 maskrcnn_benchmark.trainer INFO: eta: 9:50:17 iter: 4000 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3718 (199979951980.8222) loss_rpn_box_reg: 0.0979 (17430141206.6588) time: 0.3757 (0.4118) data: 0.0060 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:36:56,531 maskrcnn_benchmark.trainer INFO: eta: 9:49:57 iter: 4020 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3804 (198985026846.5912) loss_rpn_box_reg: 0.0867 (17343424086.2281) time: 0.3774 (0.4117) data: 0.0059 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:37:04,028 maskrcnn_benchmark.trainer INFO: eta: 9:49:33 iter: 4040 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3882 (197999952456.2634) loss_rpn_box_reg: 0.0757 (17257565551.1483) time: 0.3715 (0.4115) data: 0.0051 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:37:11,633 maskrcnn_benchmark.trainer INFO: eta: 9:49:12 iter: 4060 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3703 (197024583232.3428) loss_rpn_box_reg: 0.0847 (17172552912.9658) time: 0.3836 (0.4114) data: 0.0054 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:37:19,222 maskrcnn_benchmark.trainer INFO: eta: 9:48:50 iter: 4080 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3196 (196058776451.7940) loss_rpn_box_reg: 0.0633 (17088373732.0205) time: 0.3826 (0.4112) data: 0.0053 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:37:26,841 maskrcnn_benchmark.trainer INFO: eta: 9:48:29 iter: 4100 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4013 (195102392176.4214) loss_rpn_box_reg: 0.0861 (17005015811.3771) time: 0.3794 (0.4111) data: 0.0054 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:37:34,449 maskrcnn_benchmark.trainer INFO: eta: 9:48:08 iter: 4120 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3642 (194155293185.2756) loss_rpn_box_reg: 0.0581 (16922467190.9340) time: 0.3768 (0.4109) data: 0.0059 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:37:42,000 maskrcnn_benchmark.trainer INFO: eta: 9:47:46 iter: 4140 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3838 (193217344909.0202) loss_rpn_box_reg: 0.0885 (16840716141.7030) time: 0.3700 (0.4107) data: 0.0058 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:37:49,602 maskrcnn_benchmark.trainer INFO: eta: 9:47:25 iter: 4160 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3667 (192288415366.1904) loss_rpn_box_reg: 0.0909 (16759751160.2530) time: 0.3800 (0.4106) data: 0.0056 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:37:57,255 maskrcnn_benchmark.trainer INFO: eta: 9:47:05 iter: 4180 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4435 (191368375101.2824) loss_rpn_box_reg: 0.0812 (16679560963.3146) time: 0.3828 (0.4105) data: 0.0058 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:38:04,932 maskrcnn_benchmark.trainer INFO: eta: 9:46:46 iter: 4200 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3906 (190457097124.6115) loss_rpn_box_reg: 0.0740 (16600134482.5375) time: 0.3851 (0.4103) data: 0.0058 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:38:12,593 maskrcnn_benchmark.trainer INFO: eta: 9:46:27 iter: 4220 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4337 (189554456853.8807) loss_rpn_box_reg: 0.1085 (16521460859.3981) time: 0.3794 (0.4102) data: 0.0051 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:38:20,434 maskrcnn_benchmark.trainer INFO: eta: 9:46:11 iter: 4240 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4379 (188660332057.4021) loss_rpn_box_reg: 0.0738 (16443529440.2505) time: 0.3900 (0.4101) data: 0.0057 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:38:28,161 maskrcnn_benchmark.trainer INFO: eta: 9:45:54 iter: 4260 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3604 (187774602798.9186) loss_rpn_box_reg: 0.0836 (16366329771.5175) time: 0.3828 (0.4100) data: 0.0053 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:38:35,848 maskrcnn_benchmark.trainer INFO: eta: 9:45:35 iter: 4280 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3978 (186897151383.9724) loss_rpn_box_reg: 0.1027 (16289851595.0157) time: 0.3824 (0.4099) data: 0.0060 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:38:43,520 maskrcnn_benchmark.trainer INFO: eta: 9:45:17 iter: 4300 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3314 (186027862307.7696) loss_rpn_box_reg: 0.0667 (16214084843.4114) time: 0.3799 (0.4098) data: 0.0057 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:38:51,006 maskrcnn_benchmark.trainer INFO: eta: 9:44:54 iter: 4320 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3746 (185166622204.4946) loss_rpn_box_reg: 0.0766 (16139019635.8035) time: 0.3742 (0.4096) data: 0.0047 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:38:58,622 maskrcnn_benchmark.trainer INFO: eta: 9:44:35 iter: 4340 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3399 (184313319798.0240) loss_rpn_box_reg: 0.0595 (16064646273.4271) time: 0.3790 (0.4095) data: 0.0054 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:39:06,244 maskrcnn_benchmark.trainer INFO: eta: 9:44:15 iter: 4360 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3793 (183467845853.9981) loss_rpn_box_reg: 0.0924 (15990955235.4761) time: 0.3821 (0.4093) data: 0.0056 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:39:13,822 maskrcnn_benchmark.trainer INFO: eta: 9:43:55 iter: 4380 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3913 (182630093133.2054) loss_rpn_box_reg: 0.1007 (15917937175.0406) time: 0.3760 (0.4092) data: 0.0050 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:39:21,445 maskrcnn_benchmark.trainer INFO: eta: 9:43:36 iter: 4400 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3518 (181799956346.2379) loss_rpn_box_reg: 0.0685 (15845582915.1546) time: 0.3790 (0.4091) data: 0.0056 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:39:29,104 maskrcnn_benchmark.trainer INFO: eta: 9:43:18 iter: 4420 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3420 (180977332109.3790) loss_rpn_box_reg: 0.0709 (15773883444.9507) time: 0.3815 (0.4090) data: 0.0054 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:39:36,803 maskrcnn_benchmark.trainer INFO: eta: 9:43:00 iter: 4440 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4588 (180162118901.6812) loss_rpn_box_reg: 0.0934 (15702829915.9200) time: 0.3852 (0.4088) data: 0.0069 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:39:44,467 maskrcnn_benchmark.trainer INFO: eta: 9:42:42 iter: 4460 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4565 (179354217023.2004) loss_rpn_box_reg: 0.1036 (15632413638.2708) time: 0.3779 (0.4087) data: 0.0066 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:39:52,213 maskrcnn_benchmark.trainer INFO: eta: 9:42:26 iter: 4480 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4502 (178553528554.3489) loss_rpn_box_reg: 0.1243 (15562626077.3863) time: 0.3912 (0.4086) data: 0.0058 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:39:59,825 maskrcnn_benchmark.trainer INFO: eta: 9:42:07 iter: 4500 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3848 (177759957316.3314) loss_rpn_box_reg: 0.1202 (15493458850.3764) time: 0.3837 (0.4085) data: 0.0051 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:40:07,502 maskrcnn_benchmark.trainer INFO: eta: 9:41:50 iter: 4520 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4151 (176973408832.6326) loss_rpn_box_reg: 0.1037 (15424903722.7204) time: 0.3804 (0.4084) data: 0.0057 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:40:15,149 maskrcnn_benchmark.trainer INFO: eta: 9:41:32 iter: 4540 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4057 (176193790291.5215) loss_rpn_box_reg: 0.0984 (15356952604.9996) time: 0.3780 (0.4083) data: 0.0054 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:40:22,756 maskrcnn_benchmark.trainer INFO: eta: 9:41:13 iter: 4560 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3465 (175421010509.5428) loss_rpn_box_reg: 0.0818 (15289597549.7150) time: 0.3809 (0.4082) data: 0.0056 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:40:30,365 maskrcnn_benchmark.trainer INFO: eta: 9:40:55 iter: 4580 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3442 (174654979895.9658) loss_rpn_box_reg: 0.0633 (15222830748.1883) time: 0.3770 (0.4080) data: 0.0056 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:40:37,976 maskrcnn_benchmark.trainer INFO: eta: 9:40:36 iter: 4600 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4198 (173895610418.1591) loss_rpn_box_reg: 0.0970 (15156644527.5446) time: 0.3802 (0.4079) data: 0.0057 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:40:45,537 maskrcnn_benchmark.trainer INFO: eta: 9:40:17 iter: 4620 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3613 (173142815567.8658) loss_rpn_box_reg: 0.0869 (15091031347.7722) time: 0.3789 (0.4078) data: 0.0055 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:40:53,098 maskrcnn_benchmark.trainer INFO: eta: 9:39:58 iter: 4640 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3682 (172396510328.3507) loss_rpn_box_reg: 0.0783 (15025983798.8598) time: 0.3752 (0.4077) data: 0.0056 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:41:00,853 maskrcnn_benchmark.trainer INFO: eta: 9:39:42 iter: 4660 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3997 (171656611142.3940) loss_rpn_box_reg: 0.0718 (14961494598.0069) time: 0.3863 (0.4076) data: 0.0061 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:41:08,525 maskrcnn_benchmark.trainer INFO: eta: 9:39:26 iter: 4680 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4187 (170923035881.1035) loss_rpn_box_reg: 0.0824 (14897556586.9048) time: 0.3853 (0.4075) data: 0.0054 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:41:15,994 maskrcnn_benchmark.trainer INFO: eta: 9:39:05 iter: 4700 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3813 (170195703813.5259) loss_rpn_box_reg: 0.0770 (14834162729.0887) time: 0.3705 (0.4073) data: 0.0053 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:41:23,658 maskrcnn_benchmark.trainer INFO: eta: 9:38:48 iter: 4720 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4369 (169474535577.0298) loss_rpn_box_reg: 0.0948 (14771306107.3557) time: 0.3827 (0.4072) data: 0.0059 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:41:31,370 maskrcnn_benchmark.trainer INFO: eta: 9:38:32 iter: 4740 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4002 (168759453148.4364) loss_rpn_box_reg: 0.0978 (14708979921.2493) time: 0.3849 (0.4071) data: 0.0060 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:41:38,897 maskrcnn_benchmark.trainer INFO: eta: 9:38:13 iter: 4760 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3455 (168050379815.8814) loss_rpn_box_reg: 0.0679 (14647177484.6058) time: 0.3792 (0.4070) data: 0.0053 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:41:46,569 maskrcnn_benchmark.trainer INFO: eta: 9:37:57 iter: 4780 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3255 (167347240151.3815) loss_rpn_box_reg: 0.0724 (14585892223.1644) time: 0.3798 (0.4069) data: 0.0052 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:41:54,100 maskrcnn_benchmark.trainer INFO: eta: 9:37:38 iter: 4800 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3456 (166649959984.0857) loss_rpn_box_reg: 0.0573 (14525117672.2349) time: 0.3712 (0.4068) data: 0.0062 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:42:01,724 maskrcnn_benchmark.trainer INFO: eta: 9:37:21 iter: 4820 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3989 (165958466374.1950) loss_rpn_box_reg: 0.1123 (14464847474.4253) time: 0.3810 (0.4067) data: 0.0048 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:42:09,235 maskrcnn_benchmark.trainer INFO: eta: 9:37:01 iter: 4840 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3307 (165272687587.5263) loss_rpn_box_reg: 0.0699 (14405075377.4240) time: 0.3776 (0.4066) data: 0.0056 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:42:16,953 maskrcnn_benchmark.trainer INFO: eta: 9:36:46 iter: 4860 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4317 (164592553070.7070) loss_rpn_box_reg: 0.0819 (14345795231.8384) time: 0.3897 (0.4065) data: 0.0057 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:42:24,561 maskrcnn_benchmark.trainer INFO: eta: 9:36:29 iter: 4880 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4230 (163917993426.9763) loss_rpn_box_reg: 0.0900 (14287000989.0855) time: 0.3799 (0.4064) data: 0.0051 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:42:32,241 maskrcnn_benchmark.trainer INFO: eta: 9:36:13 iter: 4900 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3926 (163248940392.5822) loss_rpn_box_reg: 0.0846 (14228686699.3346) time: 0.3793 (0.4063) data: 0.0058 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:42:39,877 maskrcnn_benchmark.trainer INFO: eta: 9:35:56 iter: 4920 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3481 (162585326813.7522) loss_rpn_box_reg: 0.0736 (14170846509.5004) time: 0.3812 (0.4062) data: 0.0053 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:42:47,468 maskrcnn_benchmark.trainer INFO: eta: 9:35:39 iter: 4940 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.2957 (161927086624.2242) loss_rpn_box_reg: 0.0581 (14113474661.2842) time: 0.3788 (0.4061) data: 0.0054 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:42:55,131 maskrcnn_benchmark.trainer INFO: eta: 9:35:23 iter: 4960 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3585 (161274154823.3217) loss_rpn_box_reg: 0.0606 (14056565489.2633) time: 0.3837 (0.4060) data: 0.0053 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:43:02,910 maskrcnn_benchmark.trainer INFO: eta: 9:35:09 iter: 4980 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3810 (160626467454.5551) loss_rpn_box_reg: 0.1098 (14000113419.0259) time: 0.3866 (0.4059) data: 0.0057 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:43:10,490 maskrcnn_benchmark.trainer INFO: eta: 9:34:52 iter: 5000 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3999 (159983961584.7383) loss_rpn_box_reg: 0.1055 (13944112965.3503) time: 0.3766 (0.4058) data: 0.0055 (0.0063) lr: 0.020000 max mem: 4106
2019-02-26 17:43:10,491 maskrcnn_benchmark.utils.checkpoint INFO: Saving checkpoint to runs/COCO_2014/model_0005000.pth
2019-02-26 17:43:18,303 maskrcnn_benchmark.trainer INFO: eta: 9:34:39 iter: 5020 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3756 (159346575283.6053) loss_rpn_box_reg: 0.0730 (13888558730.4289) time: 0.3786 (0.4057) data: 0.0057 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:43:25,973 maskrcnn_benchmark.trainer INFO: eta: 9:34:23 iter: 5040 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3480 (158714247603.9099) loss_rpn_box_reg: 0.0720 (13833445402.1340) time: 0.3816 (0.4056) data: 0.0054 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:43:33,630 maskrcnn_benchmark.trainer INFO: eta: 9:34:07 iter: 5060 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4051 (158086918561.9990) loss_rpn_box_reg: 0.0917 (13778767752.3236) time: 0.3829 (0.4056) data: 0.0061 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:43:41,308 maskrcnn_benchmark.trainer INFO: eta: 9:33:52 iter: 5080 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3423 (157464529118.8431) loss_rpn_box_reg: 0.0839 (13724520635.1890) time: 0.3836 (0.4055) data: 0.0056 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:43:48,809 maskrcnn_benchmark.trainer INFO: eta: 9:33:34 iter: 5100 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3775 (156847021161.5158) loss_rpn_box_reg: 0.1042 (13670698985.6396) time: 0.3746 (0.4053) data: 0.0053 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:43:56,488 maskrcnn_benchmark.trainer INFO: eta: 9:33:19 iter: 5120 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4174 (156234337485.1053) loss_rpn_box_reg: 0.0771 (13617297817.7275) time: 0.3811 (0.4053) data: 0.0057 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:44:04,111 maskrcnn_benchmark.trainer INFO: eta: 9:33:02 iter: 5140 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3452 (155626421775.0479) loss_rpn_box_reg: 0.0925 (13564312223.1064) time: 0.3797 (0.4052) data: 0.0055 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:44:11,737 maskrcnn_benchmark.trainer INFO: eta: 9:32:46 iter: 5160 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3913 (155023218589.8749) loss_rpn_box_reg: 0.0664 (13511737369.5289) time: 0.3778 (0.4051) data: 0.0060 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:44:19,274 maskrcnn_benchmark.trainer INFO: eta: 9:32:29 iter: 5180 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.4099 (154424673344.3557) loss_rpn_box_reg: 0.0873 (13459568499.3767) time: 0.3748 (0.4050) data: 0.0062 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:44:26,836 maskrcnn_benchmark.trainer INFO: eta: 9:32:12 iter: 5200 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3696 (153830732293.0329) loss_rpn_box_reg: 0.0845 (13407800928.2258) time: 0.3818 (0.4049) data: 0.0063 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:44:34,568 maskrcnn_benchmark.trainer INFO: eta: 9:31:58 iter: 5220 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3952 (153241342514.1341) loss_rpn_box_reg: 0.0988 (13356430043.4439) time: 0.3820 (0.4048) data: 0.0059 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:44:42,211 maskrcnn_benchmark.trainer INFO: eta: 9:31:43 iter: 5240 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3826 (152656451893.8527) loss_rpn_box_reg: 0.0772 (13305451302.8204) time: 0.3824 (0.4047) data: 0.0059 (0.0064) lr: 0.020000 max mem: 4106
2019-02-26 17:44:49,846 maskrcnn_benchmark.trainer INFO: eta: 9:31:27 iter: 5260 loss: nan (nan) loss_box_reg: nan (nan) loss_classifier: nan (nan) loss_mask: nan (nan) loss_objectness: 0.3880 (152076009110.9877) loss_rpn_box_reg: 0.0981 (13254860233.2284) time: 0.3816 (0.4046) data: 0.0064 (0.0064) lr: 0.020000 max mem: 4106
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