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Created February 19, 2019 14:20
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Output for tools/train_net.py --config-file configs/quick_schedules/e2e_mask_rcnn_R_50_FPN_quick.yaml
Raw
SEGMASK-e2e_mask_rcnn_R_50_FPN_quick.log
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file configs/quick_schedules/e2e_mask_rcnn_R_50_FPN_quick.yaml
2019-02-19 13:46:35,385 maskrcnn_benchmark INFO: Using 2 GPUs
2019-02-19 13:46:35,385 maskrcnn_benchmark INFO: Namespace(config_file='configs/quick_schedules/e2e_mask_rcnn_R_50_FPN_quick.yaml', distributed=True, local_rank=0, opts=[], skip_test=False)
2019-02-19 13:46:35,385 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2019-02-19 13:46:37,088 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)
2019-02-19 13:46:37,088 maskrcnn_benchmark INFO: Loaded configuration file configs/quick_schedules/e2e_mask_rcnn_R_50_FPN_quick.yaml
2019-02-19 13:46:37,088 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
BATCH_SIZE_PER_IMAGE: 256
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_minival",)
TEST: ("coco_2014_minival",)
INPUT:
MIN_SIZE_TRAIN: (600,)
MAX_SIZE_TRAIN: 1000
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1000
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.005
WEIGHT_DECAY: 0.0001
STEPS: (1500,)
MAX_ITER: 2000
IMS_PER_BATCH: 4
TEST:
IMS_PER_BATCH: 2
2019-02-19 13:46:37,089 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_minival',)
INPUT:
MAX_SIZE_TEST: 1000
MAX_SIZE_TRAIN: 1000
MIN_SIZE_TEST: 800
MIN_SIZE_TRAIN: (600,)
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: 256
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: .
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: 0.005
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 2500
GAMMA: 0.1
IMS_PER_BATCH: 4
MAX_ITER: 2000
MOMENTUM: 0.9
STEPS: (1500,)
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: 2
2019-02-19 13:46:37,632 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50
2019-02-19 13:46:37,632 maskrcnn_benchmark.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
2019-02-19 13:46:37,649 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
2019-02-19 13:46:37,701 maskrcnn_benchmark.utils.c2_model_loading INFO: Remapping C2 weights
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2019-02-19 13:46:37,718 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer2.0.downsample.1.bias loaded from layer2.0.downsample.1.bias of shape (512,)
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2019-02-19 13:46:37,726 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn3.bias loaded from layer4.0.bn3.bias of shape (2048,)
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2019-02-19 13:46:37,726 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)
2019-02-19 13:46:37,726 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)
2019-02-19 13:46:37,726 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)
2019-02-19 13:46:37,726 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)
2019-02-19 13:46:37,726 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.bias loaded from layer4.0.downsample.1.bias of shape (2048,)
2019-02-19 13:46:37,726 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.weight loaded from layer4.0.downsample.1.weight of shape (2048,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.bias loaded from layer4.1.bn1.bias of shape (512,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.weight loaded from layer4.1.bn1.weight of shape (512,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.bias loaded from layer4.1.bn2.bias of shape (512,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.weight loaded from layer4.1.bn2.weight of shape (512,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.bias loaded from layer4.1.bn3.bias of shape (2048,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.weight loaded from layer4.1.bn3.weight of shape (2048,)
2019-02-19 13:46:37,727 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)
2019-02-19 13:46:37,727 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)
2019-02-19 13:46:37,727 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)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.bias loaded from layer4.2.bn1.bias of shape (512,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.weight loaded from layer4.2.bn1.weight of shape (512,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.bias loaded from layer4.2.bn2.bias of shape (512,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.weight loaded from layer4.2.bn2.weight of shape (512,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.bias loaded from layer4.2.bn3.bias of shape (2048,)
2019-02-19 13:46:37,727 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.weight loaded from layer4.2.bn3.weight of shape (2048,)
2019-02-19 13:46:37,728 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)
2019-02-19 13:46:37,728 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)
2019-02-19 13:46:37,728 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)
2019-02-19 13:46:37,728 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,)
2019-02-19 13:46:37,728 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.weight loaded from bn1.weight of shape (64,)
2019-02-19 13:46:37,728 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.conv1.weight loaded from conv1.weight of shape (64, 3, 7, 7)
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
2019-02-19 13:46:37,755 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
loading annotations into memory...
loading annotations into memory...
Done (t=0.33s)
creating index...
Done (t=0.33s)
creating index...
index created!
index created!
2019-02-19 13:46:38,338 maskrcnn_benchmark.trainer INFO: Start training
2019-02-19 13:46:43,769 maskrcnn_benchmark.trainer INFO: eta: 0:08:57 iter: 20 loss: 2.3230 (2.6762) loss_box_reg: 0.0564 (0.0793) loss_classifier: 0.6713 (1.0114) loss_mask: 0.7860 (0.8789) loss_objectness: 0.5325 (0.5286) loss_rpn_box_reg: 0.0777 (0.1781) time: 0.2521 (0.2715) data: 0.0038 (0.0221) lr: 0.001793 max mem: 2263
2019-02-19 13:46:48,898 maskrcnn_benchmark.trainer INFO: eta: 0:08:37 iter: 40 loss: 2.0692 (2.3710) loss_box_reg: 0.1933 (0.1434) loss_classifier: 0.6951 (0.8538) loss_mask: 0.7071 (0.7979) loss_objectness: 0.2794 (0.4240) loss_rpn_box_reg: 0.0870 (0.1519) time: 0.2547 (0.2640) data: 0.0047 (0.0136) lr: 0.001927 max mem: 2404
2019-02-19 13:46:53,925 maskrcnn_benchmark.trainer INFO: eta: 0:08:23 iter: 60 loss: 1.7382 (2.1723) loss_box_reg: 0.1646 (0.1536) loss_classifier: 0.5379 (0.7556) loss_mask: 0.7000 (0.7673) loss_objectness: 0.2284 (0.3627) loss_rpn_box_reg: 0.0802 (0.1332) time: 0.2497 (0.2598) data: 0.0059 (0.0109) lr: 0.002060 max mem: 2404
2019-02-19 13:46:58,861 maskrcnn_benchmark.trainer INFO: eta: 0:08:12 iter: 80 loss: 1.5526 (2.0319) loss_box_reg: 0.0884 (0.1456) loss_classifier: 0.4773 (0.6837) loss_mask: 0.6942 (0.7493) loss_objectness: 0.2178 (0.3277) loss_rpn_box_reg: 0.0542 (0.1256) time: 0.2454 (0.2565) data: 0.0046 (0.0094) lr: 0.002193 max mem: 2404
2019-02-19 13:47:03,913 maskrcnn_benchmark.trainer INFO: eta: 0:08:05 iter: 100 loss: 1.6125 (1.9712) loss_box_reg: 0.1549 (0.1469) loss_classifier: 0.4495 (0.6558) loss_mask: 0.6903 (0.7370) loss_objectness: 0.1836 (0.3070) loss_rpn_box_reg: 0.1010 (0.1246) time: 0.2480 (0.2557) data: 0.0054 (0.0087) lr: 0.002327 max mem: 2404
2019-02-19 13:47:08,770 maskrcnn_benchmark.trainer INFO: eta: 0:07:56 iter: 120 loss: 1.4069 (1.8812) loss_box_reg: 0.0659 (0.1363) loss_classifier: 0.3410 (0.6114) loss_mask: 0.6817 (0.7276) loss_objectness: 0.1389 (0.2883) loss_rpn_box_reg: 0.0424 (0.1177) time: 0.2392 (0.2536) data: 0.0048 (0.0081) lr: 0.002460 max mem: 2404
2019-02-19 13:47:13,931 maskrcnn_benchmark.trainer INFO: eta: 0:07:52 iter: 140 loss: 1.7043 (1.8705) loss_box_reg: 0.2273 (0.1486) loss_classifier: 0.6021 (0.6127) loss_mask: 0.6774 (0.7210) loss_objectness: 0.1523 (0.2736) loss_rpn_box_reg: 0.0528 (0.1146) time: 0.2601 (0.2542) data: 0.0052 (0.0077) lr: 0.002593 max mem: 2404
2019-02-19 13:47:18,966 maskrcnn_benchmark.trainer INFO: eta: 0:07:47 iter: 160 loss: 1.4901 (1.8419) loss_box_reg: 0.1258 (0.1493) loss_classifier: 0.4464 (0.6026) loss_mask: 0.6765 (0.7151) loss_objectness: 0.1603 (0.2636) loss_rpn_box_reg: 0.0573 (0.1113) time: 0.2518 (0.2539) data: 0.0044 (0.0073) lr: 0.002727 max mem: 2404
2019-02-19 13:47:24,199 maskrcnn_benchmark.trainer INFO: eta: 0:07:43 iter: 180 loss: 1.7249 (1.8331) loss_box_reg: 0.2234 (0.1567) loss_classifier: 0.5916 (0.6058) loss_mask: 0.6741 (0.7108) loss_objectness: 0.1551 (0.2528) loss_rpn_box_reg: 0.0465 (0.1070) time: 0.2632 (0.2548) data: 0.0046 (0.0070) lr: 0.002860 max mem: 2404
2019-02-19 13:47:29,372 maskrcnn_benchmark.trainer INFO: eta: 0:07:39 iter: 200 loss: 1.7550 (1.8232) loss_box_reg: 0.1838 (0.1611) loss_classifier: 0.5842 (0.6016) loss_mask: 0.6667 (0.7062) loss_objectness: 0.1593 (0.2477) loss_rpn_box_reg: 0.0933 (0.1067) time: 0.2571 (0.2552) data: 0.0045 (0.0068) lr: 0.002993 max mem: 2404
2019-02-19 13:47:34,562 maskrcnn_benchmark.trainer INFO: eta: 0:07:34 iter: 220 loss: 1.5903 (1.8122) loss_box_reg: 0.2015 (0.1654) loss_classifier: 0.4981 (0.5987) loss_mask: 0.6621 (0.7019) loss_objectness: 0.1520 (0.2419) loss_rpn_box_reg: 0.0467 (0.1043) time: 0.2587 (0.2556) data: 0.0042 (0.0066) lr: 0.003127 max mem: 2404
2019-02-19 13:47:39,972 maskrcnn_benchmark.trainer INFO: eta: 0:07:31 iter: 240 loss: 1.7864 (1.8061) loss_box_reg: 0.2674 (0.1735) loss_classifier: 0.5995 (0.5990) loss_mask: 0.6463 (0.6975) loss_objectness: 0.1241 (0.2348) loss_rpn_box_reg: 0.0339 (0.1011) time: 0.2733 (0.2568) data: 0.0040 (0.0065) lr: 0.003260 max mem: 2404
2019-02-19 13:47:45,404 maskrcnn_benchmark.trainer INFO: eta: 0:07:28 iter: 260 loss: 1.7108 (1.8015) loss_box_reg: 0.2011 (0.1780) loss_classifier: 0.5880 (0.5980) loss_mask: 0.6441 (0.6934) loss_objectness: 0.1623 (0.2314) loss_rpn_box_reg: 0.0783 (0.1008) time: 0.2685 (0.2579) data: 0.0050 (0.0064) lr: 0.003393 max mem: 2549
2019-02-19 13:47:50,867 maskrcnn_benchmark.trainer INFO: eta: 0:07:25 iter: 280 loss: 1.8120 (1.8010) loss_box_reg: 0.2384 (0.1833) loss_classifier: 0.6293 (0.6021) loss_mask: 0.6481 (0.6896) loss_objectness: 0.1642 (0.2260) loss_rpn_box_reg: 0.0707 (0.0999) time: 0.2727 (0.2590) data: 0.0044 (0.0063) lr: 0.003527 max mem: 2549
2019-02-19 13:47:56,304 maskrcnn_benchmark.trainer INFO: eta: 0:07:21 iter: 300 loss: 1.7089 (1.8055) loss_box_reg: 0.2853 (0.1899) loss_classifier: 0.5995 (0.6060) loss_mask: 0.6182 (0.6849) loss_objectness: 0.1702 (0.2222) loss_rpn_box_reg: 0.1168 (0.1025) time: 0.2709 (0.2599) data: 0.0050 (0.0063) lr: 0.003660 max mem: 2549
2019-02-19 13:48:01,758 maskrcnn_benchmark.trainer INFO: eta: 0:07:17 iter: 320 loss: 1.8144 (1.8083) loss_box_reg: 0.2063 (0.1930) loss_classifier: 0.7122 (0.6082) loss_mask: 0.6378 (0.6825) loss_objectness: 0.1854 (0.2209) loss_rpn_box_reg: 0.0635 (0.1037) time: 0.2712 (0.2607) data: 0.0048 (0.0062) lr: 0.003793 max mem: 2549
2019-02-19 13:48:07,140 maskrcnn_benchmark.trainer INFO: eta: 0:07:13 iter: 340 loss: 1.7668 (1.8015) loss_box_reg: 0.2336 (0.1949) loss_classifier: 0.6030 (0.6064) loss_mask: 0.6177 (0.6780) loss_objectness: 0.1351 (0.2181) loss_rpn_box_reg: 0.0903 (0.1040) time: 0.2689 (0.2612) data: 0.0051 (0.0062) lr: 0.003927 max mem: 2549
2019-02-19 13:48:12,715 maskrcnn_benchmark.trainer INFO: eta: 0:07:09 iter: 360 loss: 1.6516 (1.7983) loss_box_reg: 0.2637 (0.1989) loss_classifier: 0.6032 (0.6086) loss_mask: 0.6149 (0.6750) loss_objectness: 0.1161 (0.2132) loss_rpn_box_reg: 0.0473 (0.1027) time: 0.2791 (0.2622) data: 0.0044 (0.0061) lr: 0.004060 max mem: 2549
2019-02-19 13:48:18,294 maskrcnn_benchmark.trainer INFO: eta: 0:07:06 iter: 380 loss: 1.6798 (1.7963) loss_box_reg: 0.2893 (0.2026) loss_classifier: 0.6357 (0.6114) loss_mask: 0.6101 (0.6716) loss_objectness: 0.1130 (0.2087) loss_rpn_box_reg: 0.0658 (0.1020) time: 0.2814 (0.2630) data: 0.0045 (0.0061) lr: 0.004193 max mem: 2549
2019-02-19 13:48:23,890 maskrcnn_benchmark.trainer INFO: eta: 0:07:02 iter: 400 loss: 1.8709 (1.7984) loss_box_reg: 0.2620 (0.2074) loss_classifier: 0.7494 (0.6167) loss_mask: 0.6227 (0.6689) loss_objectness: 0.1187 (0.2045) loss_rpn_box_reg: 0.0466 (0.1009) time: 0.2797 (0.2639) data: 0.0043 (0.0060) lr: 0.004327 max mem: 2549
2019-02-19 13:48:29,511 maskrcnn_benchmark.trainer INFO: eta: 0:06:58 iter: 420 loss: 1.7225 (1.7948) loss_box_reg: 0.2678 (0.2103) loss_classifier: 0.5540 (0.6175) loss_mask: 0.6065 (0.6656) loss_objectness: 0.1301 (0.2012) loss_rpn_box_reg: 0.0737 (0.1003) time: 0.2824 (0.2647) data: 0.0044 (0.0060) lr: 0.004460 max mem: 2549
2019-02-19 13:48:35,139 maskrcnn_benchmark.trainer INFO: eta: 0:06:54 iter: 440 loss: 1.8233 (1.7963) loss_box_reg: 0.2745 (0.2133) loss_classifier: 0.6522 (0.6211) loss_mask: 0.5945 (0.6624) loss_objectness: 0.1518 (0.1994) loss_rpn_box_reg: 0.0807 (0.1000) time: 0.2798 (0.2655) data: 0.0055 (0.0059) lr: 0.004593 max mem: 2549
2019-02-19 13:48:40,800 maskrcnn_benchmark.trainer INFO: eta: 0:06:49 iter: 460 loss: 1.7511 (1.7946) loss_box_reg: 0.2615 (0.2156) loss_classifier: 0.6927 (0.6248) loss_mask: 0.5747 (0.6590) loss_objectness: 0.1219 (0.1964) loss_rpn_box_reg: 0.0511 (0.0988) time: 0.2813 (0.2662) data: 0.0047 (0.0059) lr: 0.004727 max mem: 2549
2019-02-19 13:48:46,511 maskrcnn_benchmark.trainer INFO: eta: 0:06:45 iter: 480 loss: 1.9411 (1.8022) loss_box_reg: 0.3235 (0.2198) loss_classifier: 0.8518 (0.6337) loss_mask: 0.5958 (0.6566) loss_objectness: 0.1406 (0.1943) loss_rpn_box_reg: 0.0498 (0.0979) time: 0.2821 (0.2670) data: 0.0044 (0.0059) lr: 0.004860 max mem: 2549
2019-02-19 13:48:52,047 maskrcnn_benchmark.trainer INFO: eta: 0:06:41 iter: 500 loss: 1.8887 (1.8062) loss_box_reg: 0.2885 (0.2226) loss_classifier: 0.7751 (0.6393) loss_mask: 0.6013 (0.6546) loss_objectness: 0.1265 (0.1923) loss_rpn_box_reg: 0.0734 (0.0975) time: 0.2774 (0.2674) data: 0.0056 (0.0059) lr: 0.004993 max mem: 2549
2019-02-19 13:48:57,722 maskrcnn_benchmark.trainer INFO: eta: 0:06:36 iter: 520 loss: 1.6907 (1.8049) loss_box_reg: 0.2375 (0.2239) loss_classifier: 0.6553 (0.6401) loss_mask: 0.5697 (0.6510) loss_objectness: 0.1227 (0.1919) loss_rpn_box_reg: 0.0703 (0.0979) time: 0.2851 (0.2680) data: 0.0046 (0.0059) lr: 0.005000 max mem: 2549
2019-02-19 13:49:03,414 maskrcnn_benchmark.trainer INFO: eta: 0:06:32 iter: 540 loss: 1.7308 (1.8073) loss_box_reg: 0.3126 (0.2264) loss_classifier: 0.6859 (0.6424) loss_mask: 0.5618 (0.6483) loss_objectness: 0.1292 (0.1909) loss_rpn_box_reg: 0.0603 (0.0993) time: 0.2810 (0.2687) data: 0.0054 (0.0059) lr: 0.005000 max mem: 2549
2019-02-19 13:49:09,017 maskrcnn_benchmark.trainer INFO: eta: 0:06:27 iter: 560 loss: 1.6803 (1.8064) loss_box_reg: 0.2642 (0.2281) loss_classifier: 0.6360 (0.6437) loss_mask: 0.5714 (0.6461) loss_objectness: 0.1136 (0.1885) loss_rpn_box_reg: 0.0510 (0.1000) time: 0.2794 (0.2691) data: 0.0046 (0.0058) lr: 0.005000 max mem: 2549
2019-02-19 13:49:14,655 maskrcnn_benchmark.trainer INFO: eta: 0:06:22 iter: 580 loss: 1.6922 (1.8049) loss_box_reg: 0.2963 (0.2303) loss_classifier: 0.6837 (0.6457) loss_mask: 0.5813 (0.6437) loss_objectness: 0.1117 (0.1861) loss_rpn_box_reg: 0.0578 (0.0990) time: 0.2776 (0.2695) data: 0.0044 (0.0058) lr: 0.005000 max mem: 2549
2019-02-19 13:49:20,249 maskrcnn_benchmark.trainer INFO: eta: 0:06:17 iter: 600 loss: 1.6669 (1.8036) loss_box_reg: 0.2800 (0.2324) loss_classifier: 0.5814 (0.6463) loss_mask: 0.5423 (0.6404) loss_objectness: 0.1409 (0.1849) loss_rpn_box_reg: 0.0629 (0.0996) time: 0.2783 (0.2698) data: 0.0051 (0.0058) lr: 0.005000 max mem: 2549
2019-02-19 13:49:26,016 maskrcnn_benchmark.trainer INFO: eta: 0:06:13 iter: 620 loss: 1.6720 (1.8014) loss_box_reg: 0.2867 (0.2339) loss_classifier: 0.6639 (0.6474) loss_mask: 0.5348 (0.6370) loss_objectness: 0.1085 (0.1837) loss_rpn_box_reg: 0.0501 (0.0994) time: 0.2872 (0.2704) data: 0.0047 (0.0058) lr: 0.005000 max mem: 2549
2019-02-19 13:49:31,835 maskrcnn_benchmark.trainer INFO: eta: 0:06:08 iter: 640 loss: 1.8837 (1.8052) loss_box_reg: 0.3011 (0.2365) loss_classifier: 0.7695 (0.6517) loss_mask: 0.5785 (0.6353) loss_objectness: 0.1354 (0.1825) loss_rpn_box_reg: 0.0722 (0.0992) time: 0.2907 (0.2711) data: 0.0047 (0.0058) lr: 0.005000 max mem: 2549
2019-02-19 13:49:37,487 maskrcnn_benchmark.trainer INFO: eta: 0:06:03 iter: 660 loss: 1.5010 (1.8000) loss_box_reg: 0.2161 (0.2365) loss_classifier: 0.5212 (0.6497) loss_mask: 0.5601 (0.6329) loss_objectness: 0.1339 (0.1814) loss_rpn_box_reg: 0.0681 (0.0996) time: 0.2795 (0.2714) data: 0.0043 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:49:43,297 maskrcnn_benchmark.trainer INFO: eta: 0:05:59 iter: 680 loss: 2.0809 (1.8032) loss_box_reg: 0.3477 (0.2395) loss_classifier: 0.7688 (0.6529) loss_mask: 0.5559 (0.6304) loss_objectness: 0.1532 (0.1806) loss_rpn_box_reg: 0.0748 (0.0998) time: 0.2882 (0.2720) data: 0.0051 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:49:49,038 maskrcnn_benchmark.trainer INFO: eta: 0:05:54 iter: 700 loss: 1.8863 (1.8051) loss_box_reg: 0.3183 (0.2415) loss_classifier: 0.6817 (0.6541) loss_mask: 0.5439 (0.6279) loss_objectness: 0.1519 (0.1805) loss_rpn_box_reg: 0.1370 (0.1011) time: 0.2844 (0.2724) data: 0.0051 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:49:54,895 maskrcnn_benchmark.trainer INFO: eta: 0:05:49 iter: 720 loss: 1.7167 (1.8045) loss_box_reg: 0.3074 (0.2427) loss_classifier: 0.7149 (0.6553) loss_mask: 0.5233 (0.6253) loss_objectness: 0.1298 (0.1796) loss_rpn_box_reg: 0.0618 (0.1016) time: 0.2949 (0.2730) data: 0.0044 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:50:00,582 maskrcnn_benchmark.trainer INFO: eta: 0:05:44 iter: 740 loss: 1.6174 (1.7998) loss_box_reg: 0.2602 (0.2436) loss_classifier: 0.6336 (0.6552) loss_mask: 0.5181 (0.6222) loss_objectness: 0.1132 (0.1779) loss_rpn_box_reg: 0.0717 (0.1010) time: 0.2837 (0.2733) data: 0.0048 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:50:06,291 maskrcnn_benchmark.trainer INFO: eta: 0:05:39 iter: 760 loss: 1.8020 (1.7993) loss_box_reg: 0.3000 (0.2454) loss_classifier: 0.6735 (0.6570) loss_mask: 0.5185 (0.6196) loss_objectness: 0.1286 (0.1766) loss_rpn_box_reg: 0.0568 (0.1007) time: 0.2860 (0.2736) data: 0.0053 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:50:12,005 maskrcnn_benchmark.trainer INFO: eta: 0:05:34 iter: 780 loss: 1.7782 (1.7968) loss_box_reg: 0.2953 (0.2464) loss_classifier: 0.6201 (0.6566) loss_mask: 0.5130 (0.6168) loss_objectness: 0.1445 (0.1760) loss_rpn_box_reg: 0.1144 (0.1010) time: 0.2843 (0.2739) data: 0.0052 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:50:17,759 maskrcnn_benchmark.trainer INFO: eta: 0:05:29 iter: 800 loss: 1.6865 (1.7947) loss_box_reg: 0.2773 (0.2474) loss_classifier: 0.6048 (0.6563) loss_mask: 0.4922 (0.6139) loss_objectness: 0.1290 (0.1755) loss_rpn_box_reg: 0.0735 (0.1015) time: 0.2855 (0.2743) data: 0.0054 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:50:23,419 maskrcnn_benchmark.trainer INFO: eta: 0:05:23 iter: 820 loss: 1.8226 (1.7950) loss_box_reg: 0.3247 (0.2489) loss_classifier: 0.7368 (0.6575) loss_mask: 0.5268 (0.6120) loss_objectness: 0.1306 (0.1754) loss_rpn_box_reg: 0.0600 (0.1012) time: 0.2827 (0.2745) data: 0.0060 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:50:29,120 maskrcnn_benchmark.trainer INFO: eta: 0:05:18 iter: 840 loss: 1.7553 (1.7959) loss_box_reg: 0.3070 (0.2503) loss_classifier: 0.7573 (0.6594) loss_mask: 0.5033 (0.6098) loss_objectness: 0.1259 (0.1750) loss_rpn_box_reg: 0.1241 (0.1014) time: 0.2836 (0.2747) data: 0.0046 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:50:34,905 maskrcnn_benchmark.trainer INFO: eta: 0:05:13 iter: 860 loss: 1.8332 (1.7960) loss_box_reg: 0.2933 (0.2514) loss_classifier: 0.7678 (0.6608) loss_mask: 0.5084 (0.6076) loss_objectness: 0.1463 (0.1746) loss_rpn_box_reg: 0.0901 (0.1015) time: 0.2880 (0.2751) data: 0.0053 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:50:40,577 maskrcnn_benchmark.trainer INFO: eta: 0:05:08 iter: 880 loss: 1.6811 (1.7949) loss_box_reg: 0.2827 (0.2526) loss_classifier: 0.6872 (0.6628) loss_mask: 0.5179 (0.6056) loss_objectness: 0.0918 (0.1731) loss_rpn_box_reg: 0.0450 (0.1008) time: 0.2825 (0.2753) data: 0.0046 (0.0057) lr: 0.005000 max mem: 2549
2019-02-19 13:50:46,295 maskrcnn_benchmark.trainer INFO: eta: 0:05:03 iter: 900 loss: 1.5759 (1.7903) loss_box_reg: 0.2551 (0.2530) loss_classifier: 0.6447 (0.6625) loss_mask: 0.4672 (0.6028) loss_objectness: 0.1054 (0.1717) loss_rpn_box_reg: 0.0572 (0.1003) time: 0.2899 (0.2755) data: 0.0039 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:50:52,027 maskrcnn_benchmark.trainer INFO: eta: 0:04:57 iter: 920 loss: 1.4994 (1.7886) loss_box_reg: 0.2624 (0.2541) loss_classifier: 0.5896 (0.6627) loss_mask: 0.5145 (0.6008) loss_objectness: 0.1234 (0.1709) loss_rpn_box_reg: 0.0652 (0.1002) time: 0.2821 (0.2757) data: 0.0042 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:50:57,813 maskrcnn_benchmark.trainer INFO: eta: 0:04:52 iter: 940 loss: 1.7080 (1.7872) loss_box_reg: 0.3238 (0.2554) loss_classifier: 0.7043 (0.6635) loss_mask: 0.5008 (0.5985) loss_objectness: 0.1067 (0.1698) loss_rpn_box_reg: 0.0891 (0.1000) time: 0.2916 (0.2760) data: 0.0043 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:51:03,618 maskrcnn_benchmark.trainer INFO: eta: 0:04:47 iter: 960 loss: 1.5720 (1.7842) loss_box_reg: 0.2755 (0.2562) loss_classifier: 0.5974 (0.6632) loss_mask: 0.5261 (0.5969) loss_objectness: 0.0991 (0.1685) loss_rpn_box_reg: 0.0626 (0.0994) time: 0.2863 (0.2763) data: 0.0048 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:51:09,427 maskrcnn_benchmark.trainer INFO: eta: 0:04:42 iter: 980 loss: 1.7891 (1.7852) loss_box_reg: 0.3461 (0.2578) loss_classifier: 0.7637 (0.6653) loss_mask: 0.5064 (0.5949) loss_objectness: 0.1037 (0.1676) loss_rpn_box_reg: 0.0720 (0.0996) time: 0.2889 (0.2766) data: 0.0056 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:51:15,098 maskrcnn_benchmark.trainer INFO: eta: 0:04:36 iter: 1000 loss: 1.5234 (1.7830) loss_box_reg: 0.2861 (0.2584) loss_classifier: 0.5799 (0.6653) loss_mask: 0.4504 (0.5929) loss_objectness: 0.1120 (0.1669) loss_rpn_box_reg: 0.0812 (0.0994) time: 0.2808 (0.2768) data: 0.0053 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:51:20,799 maskrcnn_benchmark.trainer INFO: eta: 0:04:31 iter: 1020 loss: 1.5327 (1.7811) loss_box_reg: 0.3058 (0.2596) loss_classifier: 0.6813 (0.6660) loss_mask: 0.4634 (0.5905) loss_objectness: 0.0910 (0.1658) loss_rpn_box_reg: 0.0601 (0.0993) time: 0.2848 (0.2769) data: 0.0047 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:51:26,410 maskrcnn_benchmark.trainer INFO: eta: 0:04:25 iter: 1040 loss: 1.6353 (1.7769) loss_box_reg: 0.2733 (0.2601) loss_classifier: 0.6270 (0.6655) loss_mask: 0.4628 (0.5878) loss_objectness: 0.0822 (0.1649) loss_rpn_box_reg: 0.0313 (0.0987) time: 0.2791 (0.2770) data: 0.0045 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:51:32,021 maskrcnn_benchmark.trainer INFO: eta: 0:04:20 iter: 1060 loss: 1.5023 (1.7729) loss_box_reg: 0.2753 (0.2604) loss_classifier: 0.5837 (0.6642) loss_mask: 0.4579 (0.5855) loss_objectness: 0.0990 (0.1641) loss_rpn_box_reg: 0.0472 (0.0987) time: 0.2804 (0.2771) data: 0.0040 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:51:37,728 maskrcnn_benchmark.trainer INFO: eta: 0:04:15 iter: 1080 loss: 1.6153 (1.7697) loss_box_reg: 0.3147 (0.2610) loss_classifier: 0.6166 (0.6637) loss_mask: 0.4740 (0.5831) loss_objectness: 0.1210 (0.1635) loss_rpn_box_reg: 0.0647 (0.0985) time: 0.2842 (0.2772) data: 0.0050 (0.0056) lr: 0.005000 max mem: 2549
2019-02-19 13:51:43,378 maskrcnn_benchmark.trainer INFO: eta: 0:04:09 iter: 1100 loss: 1.4403 (1.7668) loss_box_reg: 0.2846 (0.2612) loss_classifier: 0.6026 (0.6631) loss_mask: 0.4671 (0.5810) loss_objectness: 0.1144 (0.1631) loss_rpn_box_reg: 0.0745 (0.0984) time: 0.2847 (0.2773) data: 0.0045 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:51:48,933 maskrcnn_benchmark.trainer INFO: eta: 0:04:04 iter: 1120 loss: 1.6501 (1.7655) loss_box_reg: 0.3092 (0.2621) loss_classifier: 0.6625 (0.6633) loss_mask: 0.4899 (0.5793) loss_objectness: 0.1072 (0.1625) loss_rpn_box_reg: 0.0812 (0.0983) time: 0.2760 (0.2773) data: 0.0050 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:51:54,652 maskrcnn_benchmark.trainer INFO: eta: 0:03:58 iter: 1140 loss: 1.8234 (1.7659) loss_box_reg: 0.3451 (0.2630) loss_classifier: 0.7490 (0.6642) loss_mask: 0.4825 (0.5777) loss_objectness: 0.1268 (0.1623) loss_rpn_box_reg: 0.0916 (0.0987) time: 0.2870 (0.2775) data: 0.0052 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:00,349 maskrcnn_benchmark.trainer INFO: eta: 0:03:53 iter: 1160 loss: 1.8024 (1.7666) loss_box_reg: 0.3198 (0.2640) loss_classifier: 0.7356 (0.6660) loss_mask: 0.4870 (0.5762) loss_objectness: 0.1149 (0.1615) loss_rpn_box_reg: 0.0899 (0.0989) time: 0.2874 (0.2776) data: 0.0041 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:06,004 maskrcnn_benchmark.trainer INFO: eta: 0:03:47 iter: 1180 loss: 1.4873 (1.7633) loss_box_reg: 0.2499 (0.2639) loss_classifier: 0.5679 (0.6649) loss_mask: 0.4816 (0.5746) loss_objectness: 0.1214 (0.1614) loss_rpn_box_reg: 0.0401 (0.0985) time: 0.2786 (0.2777) data: 0.0045 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:11,733 maskrcnn_benchmark.trainer INFO: eta: 0:03:42 iter: 1200 loss: 1.6941 (1.7622) loss_box_reg: 0.2841 (0.2641) loss_classifier: 0.6078 (0.6647) loss_mask: 0.4984 (0.5733) loss_objectness: 0.1411 (0.1612) loss_rpn_box_reg: 0.0752 (0.0988) time: 0.2842 (0.2778) data: 0.0050 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:17,389 maskrcnn_benchmark.trainer INFO: eta: 0:03:36 iter: 1220 loss: 1.7102 (1.7605) loss_box_reg: 0.3012 (0.2645) loss_classifier: 0.6433 (0.6643) loss_mask: 0.5117 (0.5723) loss_objectness: 0.1153 (0.1605) loss_rpn_box_reg: 0.0713 (0.0988) time: 0.2824 (0.2779) data: 0.0053 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:22,874 maskrcnn_benchmark.trainer INFO: eta: 0:03:31 iter: 1240 loss: 1.4190 (1.7573) loss_box_reg: 0.2572 (0.2647) loss_classifier: 0.5792 (0.6639) loss_mask: 0.5033 (0.5710) loss_objectness: 0.0700 (0.1596) loss_rpn_box_reg: 0.0311 (0.0982) time: 0.2765 (0.2779) data: 0.0045 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:28,592 maskrcnn_benchmark.trainer INFO: eta: 0:03:25 iter: 1260 loss: 1.5475 (1.7550) loss_box_reg: 0.2952 (0.2650) loss_classifier: 0.6485 (0.6635) loss_mask: 0.4785 (0.5696) loss_objectness: 0.0917 (0.1588) loss_rpn_box_reg: 0.0549 (0.0980) time: 0.2900 (0.2780) data: 0.0048 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:34,255 maskrcnn_benchmark.trainer INFO: eta: 0:03:20 iter: 1280 loss: 1.5376 (1.7517) loss_box_reg: 0.2672 (0.2653) loss_classifier: 0.6040 (0.6624) loss_mask: 0.4878 (0.5683) loss_objectness: 0.0868 (0.1581) loss_rpn_box_reg: 0.0533 (0.0976) time: 0.2833 (0.2781) data: 0.0050 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:39,919 maskrcnn_benchmark.trainer INFO: eta: 0:03:14 iter: 1300 loss: 1.6382 (1.7498) loss_box_reg: 0.2892 (0.2657) loss_classifier: 0.6499 (0.6625) loss_mask: 0.4468 (0.5666) loss_objectness: 0.0915 (0.1571) loss_rpn_box_reg: 0.0652 (0.0979) time: 0.2805 (0.2781) data: 0.0046 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:45,666 maskrcnn_benchmark.trainer INFO: eta: 0:03:09 iter: 1320 loss: 1.7022 (1.7490) loss_box_reg: 0.3046 (0.2663) loss_classifier: 0.6650 (0.6628) loss_mask: 0.4485 (0.5653) loss_objectness: 0.1072 (0.1565) loss_rpn_box_reg: 0.0991 (0.0982) time: 0.2836 (0.2783) data: 0.0058 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:51,378 maskrcnn_benchmark.trainer INFO: eta: 0:03:03 iter: 1340 loss: 1.5889 (1.7472) loss_box_reg: 0.3261 (0.2668) loss_classifier: 0.6230 (0.6629) loss_mask: 0.4452 (0.5636) loss_objectness: 0.0909 (0.1559) loss_rpn_box_reg: 0.0618 (0.0980) time: 0.2836 (0.2784) data: 0.0049 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:52:57,143 maskrcnn_benchmark.trainer INFO: eta: 0:02:58 iter: 1360 loss: 1.5897 (1.7454) loss_box_reg: 0.3068 (0.2672) loss_classifier: 0.6699 (0.6628) loss_mask: 0.4647 (0.5623) loss_objectness: 0.0968 (0.1552) loss_rpn_box_reg: 0.0597 (0.0980) time: 0.2895 (0.2785) data: 0.0051 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:53:02,867 maskrcnn_benchmark.trainer INFO: eta: 0:02:52 iter: 1380 loss: 1.7422 (1.7448) loss_box_reg: 0.3011 (0.2679) loss_classifier: 0.6472 (0.6632) loss_mask: 0.4555 (0.5609) loss_objectness: 0.1109 (0.1547) loss_rpn_box_reg: 0.0799 (0.0981) time: 0.2819 (0.2786) data: 0.0052 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:53:08,520 maskrcnn_benchmark.trainer INFO: eta: 0:02:47 iter: 1400 loss: 1.4731 (1.7408) loss_box_reg: 0.2675 (0.2678) loss_classifier: 0.5735 (0.6619) loss_mask: 0.4554 (0.5592) loss_objectness: 0.0916 (0.1538) loss_rpn_box_reg: 0.0644 (0.0980) time: 0.2789 (0.2787) data: 0.0047 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:53:14,257 maskrcnn_benchmark.trainer INFO: eta: 0:02:41 iter: 1420 loss: 1.4790 (1.7381) loss_box_reg: 0.3092 (0.2683) loss_classifier: 0.5662 (0.6614) loss_mask: 0.4518 (0.5577) loss_objectness: 0.0773 (0.1531) loss_rpn_box_reg: 0.0452 (0.0977) time: 0.2836 (0.2788) data: 0.0047 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:53:19,912 maskrcnn_benchmark.trainer INFO: eta: 0:02:36 iter: 1440 loss: 1.5411 (1.7346) loss_box_reg: 0.2378 (0.2682) loss_classifier: 0.5743 (0.6605) loss_mask: 0.4582 (0.5563) loss_objectness: 0.1102 (0.1525) loss_rpn_box_reg: 0.0498 (0.0972) time: 0.2838 (0.2789) data: 0.0046 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:53:25,645 maskrcnn_benchmark.trainer INFO: eta: 0:02:30 iter: 1460 loss: 1.6366 (1.7330) loss_box_reg: 0.2975 (0.2685) loss_classifier: 0.6179 (0.6600) loss_mask: 0.4625 (0.5550) loss_objectness: 0.1108 (0.1523) loss_rpn_box_reg: 0.0582 (0.0972) time: 0.2846 (0.2790) data: 0.0059 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:53:31,312 maskrcnn_benchmark.trainer INFO: eta: 0:02:25 iter: 1480 loss: 1.5455 (1.7308) loss_box_reg: 0.2849 (0.2687) loss_classifier: 0.6360 (0.6598) loss_mask: 0.4529 (0.5538) loss_objectness: 0.1093 (0.1518) loss_rpn_box_reg: 0.0506 (0.0968) time: 0.2780 (0.2790) data: 0.0049 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:53:37,029 maskrcnn_benchmark.trainer INFO: eta: 0:02:19 iter: 1500 loss: 1.5361 (1.7285) loss_box_reg: 0.2770 (0.2689) loss_classifier: 0.5660 (0.6590) loss_mask: 0.4529 (0.5526) loss_objectness: 0.1034 (0.1513) loss_rpn_box_reg: 0.0734 (0.0966) time: 0.2874 (0.2791) data: 0.0047 (0.0055) lr: 0.005000 max mem: 2549
2019-02-19 13:53:42,652 maskrcnn_benchmark.trainer INFO: eta: 0:02:13 iter: 1520 loss: 1.5752 (1.7263) loss_box_reg: 0.3060 (0.2692) loss_classifier: 0.6182 (0.6584) loss_mask: 0.4474 (0.5512) loss_objectness: 0.1123 (0.1509) loss_rpn_box_reg: 0.0857 (0.0966) time: 0.2812 (0.2792) data: 0.0047 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:53:48,401 maskrcnn_benchmark.trainer INFO: eta: 0:02:08 iter: 1540 loss: 1.5351 (1.7233) loss_box_reg: 0.3014 (0.2695) loss_classifier: 0.5566 (0.6574) loss_mask: 0.4416 (0.5498) loss_objectness: 0.0785 (0.1502) loss_rpn_box_reg: 0.0435 (0.0963) time: 0.2845 (0.2793) data: 0.0049 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:53:54,144 maskrcnn_benchmark.trainer INFO: eta: 0:02:02 iter: 1560 loss: 1.4174 (1.7196) loss_box_reg: 0.2805 (0.2696) loss_classifier: 0.5110 (0.6564) loss_mask: 0.4137 (0.5482) loss_objectness: 0.0792 (0.1493) loss_rpn_box_reg: 0.0602 (0.0961) time: 0.2890 (0.2794) data: 0.0044 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:53:59,800 maskrcnn_benchmark.trainer INFO: eta: 0:01:57 iter: 1580 loss: 1.3599 (1.7150) loss_box_reg: 0.2501 (0.2695) loss_classifier: 0.5123 (0.6543) loss_mask: 0.4045 (0.5464) loss_objectness: 0.0784 (0.1488) loss_rpn_box_reg: 0.0526 (0.0960) time: 0.2853 (0.2794) data: 0.0046 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:05,526 maskrcnn_benchmark.trainer INFO: eta: 0:01:51 iter: 1600 loss: 1.4356 (1.7115) loss_box_reg: 0.2750 (0.2695) loss_classifier: 0.5116 (0.6531) loss_mask: 0.4367 (0.5451) loss_objectness: 0.0872 (0.1481) loss_rpn_box_reg: 0.0451 (0.0956) time: 0.2851 (0.2795) data: 0.0056 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:11,165 maskrcnn_benchmark.trainer INFO: eta: 0:01:46 iter: 1620 loss: 1.4541 (1.7082) loss_box_reg: 0.2708 (0.2696) loss_classifier: 0.4989 (0.6521) loss_mask: 0.4218 (0.5437) loss_objectness: 0.0711 (0.1473) loss_rpn_box_reg: 0.0586 (0.0955) time: 0.2827 (0.2795) data: 0.0047 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:16,908 maskrcnn_benchmark.trainer INFO: eta: 0:01:40 iter: 1640 loss: 1.4389 (1.7047) loss_box_reg: 0.2583 (0.2696) loss_classifier: 0.5119 (0.6504) loss_mask: 0.4345 (0.5424) loss_objectness: 0.1025 (0.1469) loss_rpn_box_reg: 0.0575 (0.0953) time: 0.2852 (0.2796) data: 0.0048 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:22,593 maskrcnn_benchmark.trainer INFO: eta: 0:01:35 iter: 1660 loss: 1.2746 (1.7002) loss_box_reg: 0.2433 (0.2695) loss_classifier: 0.4917 (0.6486) loss_mask: 0.4112 (0.5410) loss_objectness: 0.0807 (0.1462) loss_rpn_box_reg: 0.0439 (0.0949) time: 0.2845 (0.2797) data: 0.0047 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:28,295 maskrcnn_benchmark.trainer INFO: eta: 0:01:29 iter: 1680 loss: 1.5318 (1.6978) loss_box_reg: 0.2799 (0.2697) loss_classifier: 0.5902 (0.6477) loss_mask: 0.4470 (0.5400) loss_objectness: 0.0791 (0.1458) loss_rpn_box_reg: 0.0522 (0.0946) time: 0.2845 (0.2797) data: 0.0043 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:34,038 maskrcnn_benchmark.trainer INFO: eta: 0:01:23 iter: 1700 loss: 1.5763 (1.6955) loss_box_reg: 0.2897 (0.2699) loss_classifier: 0.6013 (0.6469) loss_mask: 0.4485 (0.5388) loss_objectness: 0.0892 (0.1454) loss_rpn_box_reg: 0.0453 (0.0945) time: 0.2872 (0.2798) data: 0.0050 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:39,824 maskrcnn_benchmark.trainer INFO: eta: 0:01:18 iter: 1720 loss: 1.4729 (1.6931) loss_box_reg: 0.2799 (0.2701) loss_classifier: 0.5826 (0.6462) loss_mask: 0.4350 (0.5377) loss_objectness: 0.0967 (0.1449) loss_rpn_box_reg: 0.0515 (0.0943) time: 0.2892 (0.2799) data: 0.0055 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:45,463 maskrcnn_benchmark.trainer INFO: eta: 0:01:12 iter: 1740 loss: 1.4299 (1.6896) loss_box_reg: 0.2583 (0.2699) loss_classifier: 0.5416 (0.6447) loss_mask: 0.4487 (0.5366) loss_objectness: 0.0806 (0.1443) loss_rpn_box_reg: 0.0558 (0.0940) time: 0.2824 (0.2800) data: 0.0044 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:51,157 maskrcnn_benchmark.trainer INFO: eta: 0:01:07 iter: 1760 loss: 1.4343 (1.6864) loss_box_reg: 0.2660 (0.2699) loss_classifier: 0.5453 (0.6435) loss_mask: 0.4375 (0.5354) loss_objectness: 0.0916 (0.1438) loss_rpn_box_reg: 0.0544 (0.0938) time: 0.2858 (0.2800) data: 0.0047 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:54:56,801 maskrcnn_benchmark.trainer INFO: eta: 0:01:01 iter: 1780 loss: 1.4848 (1.6842) loss_box_reg: 0.3083 (0.2702) loss_classifier: 0.5933 (0.6429) loss_mask: 0.4528 (0.5344) loss_objectness: 0.0842 (0.1432) loss_rpn_box_reg: 0.0413 (0.0936) time: 0.2820 (0.2800) data: 0.0044 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:02,542 maskrcnn_benchmark.trainer INFO: eta: 0:00:56 iter: 1800 loss: 1.4202 (1.6820) loss_box_reg: 0.2883 (0.2704) loss_classifier: 0.5393 (0.6417) loss_mask: 0.4530 (0.5333) loss_objectness: 0.1140 (0.1430) loss_rpn_box_reg: 0.0749 (0.0936) time: 0.2881 (0.2801) data: 0.0048 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:08,310 maskrcnn_benchmark.trainer INFO: eta: 0:00:50 iter: 1820 loss: 1.3939 (1.6788) loss_box_reg: 0.2682 (0.2704) loss_classifier: 0.5598 (0.6406) loss_mask: 0.4284 (0.5322) loss_objectness: 0.0626 (0.1423) loss_rpn_box_reg: 0.0517 (0.0933) time: 0.2834 (0.2802) data: 0.0046 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:14,019 maskrcnn_benchmark.trainer INFO: eta: 0:00:44 iter: 1840 loss: 1.3820 (1.6756) loss_box_reg: 0.2804 (0.2705) loss_classifier: 0.5402 (0.6395) loss_mask: 0.4079 (0.5308) loss_objectness: 0.0817 (0.1417) loss_rpn_box_reg: 0.0674 (0.0931) time: 0.2837 (0.2803) data: 0.0047 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:19,750 maskrcnn_benchmark.trainer INFO: eta: 0:00:39 iter: 1860 loss: 1.4310 (1.6731) loss_box_reg: 0.2899 (0.2707) loss_classifier: 0.5778 (0.6386) loss_mask: 0.4188 (0.5296) loss_objectness: 0.0707 (0.1411) loss_rpn_box_reg: 0.0681 (0.0930) time: 0.2895 (0.2803) data: 0.0055 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:25,511 maskrcnn_benchmark.trainer INFO: eta: 0:00:33 iter: 1880 loss: 1.3401 (1.6701) loss_box_reg: 0.2902 (0.2707) loss_classifier: 0.5013 (0.6374) loss_mask: 0.4177 (0.5285) loss_objectness: 0.0796 (0.1406) loss_rpn_box_reg: 0.0346 (0.0929) time: 0.2905 (0.2804) data: 0.0044 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:31,197 maskrcnn_benchmark.trainer INFO: eta: 0:00:28 iter: 1900 loss: 1.4876 (1.6680) loss_box_reg: 0.2582 (0.2707) loss_classifier: 0.5557 (0.6366) loss_mask: 0.4660 (0.5278) loss_objectness: 0.0991 (0.1401) loss_rpn_box_reg: 0.0417 (0.0927) time: 0.2818 (0.2805) data: 0.0048 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:36,855 maskrcnn_benchmark.trainer INFO: eta: 0:00:22 iter: 1920 loss: 1.3420 (1.6645) loss_box_reg: 0.2652 (0.2706) loss_classifier: 0.5421 (0.6354) loss_mask: 0.4135 (0.5267) loss_objectness: 0.0536 (0.1395) loss_rpn_box_reg: 0.0345 (0.0923) time: 0.2817 (0.2805) data: 0.0043 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:42,494 maskrcnn_benchmark.trainer INFO: eta: 0:00:16 iter: 1940 loss: 1.4048 (1.6620) loss_box_reg: 0.2654 (0.2708) loss_classifier: 0.5629 (0.6347) loss_mask: 0.4375 (0.5257) loss_objectness: 0.0745 (0.1389) loss_rpn_box_reg: 0.0478 (0.0920) time: 0.2855 (0.2805) data: 0.0052 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:48,204 maskrcnn_benchmark.trainer INFO: eta: 0:00:11 iter: 1960 loss: 1.3833 (1.6597) loss_box_reg: 0.2607 (0.2708) loss_classifier: 0.5137 (0.6336) loss_mask: 0.4458 (0.5249) loss_objectness: 0.0725 (0.1385) loss_rpn_box_reg: 0.0394 (0.0919) time: 0.2869 (0.2805) data: 0.0048 (0.0055) lr: 0.000500 max mem: 2549
2019-02-19 13:55:53,868 maskrcnn_benchmark.trainer INFO: eta: 0:00:05 iter: 1980 loss: 1.2494 (1.6559) loss_box_reg: 0.2330 (0.2705) loss_classifier: 0.4160 (0.6319) loss_mask: 0.4170 (0.5239) loss_objectness: 0.0938 (0.1380) loss_rpn_box_reg: 0.0473 (0.0917) time: 0.2810 (0.2806) data: 0.0045 (0.0054) lr: 0.000500 max mem: 2549
2019-02-19 13:55:59,506 maskrcnn_benchmark.trainer INFO: eta: 0:00:00 iter: 2000 loss: 1.2962 (1.6527) loss_box_reg: 0.2499 (0.2704) loss_classifier: 0.4956 (0.6306) loss_mask: 0.4477 (0.5230) loss_objectness: 0.0668 (0.1374) loss_rpn_box_reg: 0.0378 (0.0913) time: 0.2825 (0.2806) data: 0.0041 (0.0054) lr: 0.000500 max mem: 2549
2019-02-19 13:55:59,507 maskrcnn_benchmark.utils.checkpoint INFO: Saving checkpoint to ./model_final.pth
loading annotations into memory...
2019-02-19 13:55:59,887 maskrcnn_benchmark.trainer INFO: Total training time: 0:09:21.548077 (0.2808 s / it)
Done (t=0.29s)
creating index...
loading annotations into memory...
index created!
0%| | 1/2500 [00:00<10:22, 4.02it/s]Done (t=0.30s)
creating index...
index created!
2019-02-19 13:56:00,454 maskrcnn_benchmark.inference INFO: Start evaluation on coco_2014_minival dataset(5000 images).
1%|█▏ | 17/2500 [00:01<03:38, 11.35it/s]Traceback (most recent call last):
File "tools/train_net.py", line 174, in <module>
main()
File "tools/train_net.py", line 170, in main
test(cfg, model, args.distributed)
File "tools/train_net.py", line 106, in test
output_folder=output_folder,
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/engine/inference.py", line 76, in inference
predictions = compute_on_dataset(model, data_loader, device)
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/engine/inference.py", line 20, in compute_on_dataset
for i, batch in enumerate(tqdm(data_loader)):
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/tqdm-4.31.1-py3.7.egg/tqdm/_tqdm.py", line 1022, in __iter__
for obj in iterable:
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 637, in __next__
return self._process_next_batch(batch)
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 658, in _process_next_batch
raise batch.exc_type(batch.exc_msg)
IndexError: Traceback (most recent call last):
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 138, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 138, in <listcomp>
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/data/datasets/coco.py", line 83, in __getitem__
masks = SegmentationMask(masks, img.size)
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/structures/segmentation_mask.py", line 206, in __init__
if not isinstance(segms[0], (list, Polygons)):
IndexError: list index out of range
1%|█▎ | 19/2500 [00:01<03:39, 11.30it/s]
1%|█▉ | 29/2500 [00:02<03:27, 11.88it/s]Traceback (most recent call last):
File "tools/train_net.py", line 174, in <module>
main()
File "tools/train_net.py", line 170, in main
test(cfg, model, args.distributed)
File "tools/train_net.py", line 106, in test
output_folder=output_folder,
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/engine/inference.py", line 76, in inference
predictions = compute_on_dataset(model, data_loader, device)
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/engine/inference.py", line 20, in compute_on_dataset
for i, batch in enumerate(tqdm(data_loader)):
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/tqdm-4.31.1-py3.7.egg/tqdm/_tqdm.py", line 1022, in __iter__
for obj in iterable:
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 637, in __next__
return self._process_next_batch(batch)
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 658, in _process_next_batch
raise batch.exc_type(batch.exc_msg)
IndexError: Traceback (most recent call last):
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 138, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 138, in <listcomp>
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/data/datasets/coco.py", line 83, in __getitem__
masks = SegmentationMask(masks, img.size)
File "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/maskrcnn_benchmark-0.1-py3.7-linux-x86_64.egg/maskrcnn_benchmark/structures/segmentation_mask.py", line 206, in __init__
if not isinstance(segms[0], (list, Polygons)):
IndexError: list index out of range
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