Comments (16)
husthkk
commented on July 18, 2024
8
@gittigxuy It should be because your training batch size is only 1. It is too small. We recommend using batch size >= 8.
why does batch size too small cause nan?
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bei-startdt
commented on July 18, 2024
2
hey my friend.
tears on my happy face (ku xiao bu de).
I tried for much times, google and google... but nothing worked..
so my friend say that it maybe one of GPUs error occured.
I checked it and changed GPU selected, and keep config same as repo's origin, it worked.
Thank for your helping!
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tianzhi0549
commented on July 18, 2024
1
How many times have you tried? If you always encounter NAN, we suggest that you clip the gradients with https://pytorch.org/docs/stable/_modules/torch/nn/utils/clip_grad.html . You can firstly try to set max_norm to 100. If the loss becomes NAN again, please reduce it. Thank you.
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tianzhi0549
commented on July 18, 2024
Can you post your full logs here? Maybe we can help you.
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bei-startdt
commented on July 18, 2024
2019-04-16 09:44:24,883 maskrcnn_benchmark INFO: Using 4 GPUs
2019-04-16 09:44:24,883 maskrcnn_benchmark INFO: Namespace(config_file='configs/fcos/fcos_R_50_FPN_1x.yaml', distributed=True, local_rank=0, opts=['DATALOADER.NUM_WORKERS', '2', 'OUTPUT_DIR', 'training_dir/fcos_R_50_FPN_1x'], skip_test=True)
2019-04-16 09:44:24,883 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2019-04-16 09:44:27,749 maskrcnn_benchmark INFO:
PyTorch version: 1.0.0
Is debug build: No
CUDA used to build PyTorch: 9.0.176
OS: Ubuntu 16.04.6 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609
CMake version: Could not collect
Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce GTX 1080 Ti
GPU 1: GeForce GTX 1080 Ti
GPU 2: GeForce GTX 1080 Ti
GPU 3: GeForce GTX 1080 Ti
GPU 4: GeForce GTX 1080 Ti
GPU 5: GeForce GTX 1080 Ti
GPU 6: GeForce GTX 1080 Ti
GPU 7: GeForce GTX 1080 Ti
Nvidia driver version: 415.27
cuDNN version: Could not collect
Versions of relevant libraries:
[pip] Could not collect
[conda] torch 1.0.0 pypi_0 pypi
[conda] torchvision 0.2.2.post3 pypi_0 pypi
Pillow (6.0.0)
2019-04-16 09:44:27,749 maskrcnn_benchmark INFO: Loaded configuration file configs/fcos/fcos_R_50_FPN_1x.yaml
2019-04-16 09:44:27,749 maskrcnn_benchmark INFO:
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
RPN_ONLY: True
FCOS_ON: True
BACKBONE:
CONV_BODY: "R-50-FPN-RETINANET"
RESNETS:
BACKBONE_OUT_CHANNELS: 256
RETINANET:
USE_C5: False # FCOS uses P5 instead of C5
DATASETS:
TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
TEST: ("coco_2014_minival",)
INPUT:
MIN_SIZE_TRAIN: (800,)
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.01
WEIGHT_DECAY: 0.0001
STEPS: (60000, 80000)
MAX_ITER: 90000
IMS_PER_BATCH: 16
WARMUP_METHOD: "constant"
2019-04-16 09:44:27,750 maskrcnn_benchmark INFO: Running with config:
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 2
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_RANGE_TRAIN: (-1, -1)
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-RETINANET
FREEZE_CONV_BODY_AT: 2
USE_GN: False
CLS_AGNOSTIC_BBOX_REG: False
DEVICE: cuda
FBNET:
ARCH: default
ARCH_DEF:
BN_TYPE: bn
DET_HEAD_BLOCKS: []
DET_HEAD_LAST_SCALE: 1.0
DET_HEAD_STRIDE: 0
DW_CONV_SKIP_BN: True
DW_CONV_SKIP_RELU: True
KPTS_HEAD_BLOCKS: []
KPTS_HEAD_LAST_SCALE: 0.0
KPTS_HEAD_STRIDE: 0
MASK_HEAD_BLOCKS: []
MASK_HEAD_LAST_SCALE: 0.0
MASK_HEAD_STRIDE: 0
RPN_BN_TYPE:
RPN_HEAD_BLOCKS: 0
SCALE_FACTOR: 1.0
WIDTH_DIVISOR: 1
FCOS:
FPN_STRIDES: [8, 16, 32, 64, 128]
INFERENCE_TH: 0.05
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.4
NUM_CLASSES: 81
NUM_CONVS: 4
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
FCOS_ON: True
FPN:
USE_GN: False
USE_RELU: False
GROUP_NORM:
DIM_PER_GP: -1
EPSILON: 1e-05
NUM_GROUPS: 32
KEYPOINT_ON: False
MASK_ON: False
META_ARCHITECTURE: GeneralizedRCNN
RESNETS:
BACKBONE_OUT_CHANNELS: 256
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: False
RETINANET_ON: False
ROI_BOX_HEAD:
CONV_HEAD_DIM: 256
DILATION: 1
FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 81
NUM_STACKED_CONVS: 4
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
PREDICTOR: FastRCNNPredictor
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: False
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: ResNet50Conv5ROIFeatureExtractor
MLP_HEAD_DIM: 1024
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
POSTPROCESS_MASKS: False
POSTPROCESS_MASKS_THRESHOLD: 0.5
PREDICTOR: MaskRCNNC4Predictor
RESOLUTION: 14
SHARE_BOX_FEATURE_EXTRACTOR: True
USE_GN: False
RPN:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDE: (16,)
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: 2000
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: 6000
PRE_NMS_TOP_N_TRAIN: 12000
RPN_HEAD: SingleConvRPNHead
STRADDLE_THRESH: 0
USE_FPN: False
RPN_ONLY: True
WEIGHT: catalog://ImageNetPretrained/MSRA/R-50
OUTPUT_DIR: training_dir/fcos_R_50_FPN_1x
PATHS_CATALOG: /data/bei/FCOS/maskrcnn_benchmark/config/paths_catalog.py
SOLVER:
BASE_LR: 0.01
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: constant
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
TEST:
DETECTIONS_PER_IMG: 100
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 8
2019-04-16 09:44:28,652 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50
2019-04-16 09:44:28,652 maskrcnn_benchmark.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
2019-04-16 09:44:28,681 maskrcnn_benchmark.utils.checkpoint INFO: url https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl cached in /home/wangjiangben/.torch/models/R-50.pkl
2019-04-16 09:44:28,769 maskrcnn_benchmark.utils.c2_model_loading INFO: Remapping C2 weights
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2019-04-16 09:44:28,810 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn2.bias loaded from layer4.0.bn2.bias of shape (512,)
2019-04-16 09:44:28,810 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn2.weight loaded from layer4.0.bn2.weight of shape (512,)
2019-04-16 09:44:28,810 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn3.bias loaded from layer4.0.bn3.bias of shape (2048,)
2019-04-16 09:44:28,810 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.bn3.weight loaded from layer4.0.bn3.weight of shape (2048,)
2019-04-16 09:44:28,810 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)
2019-04-16 09:44:28,810 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)
2019-04-16 09:44:28,810 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)
2019-04-16 09:44:28,810 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)
2019-04-16 09:44:28,810 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.bias loaded from layer4.0.downsample.1.bias of shape (2048,)
2019-04-16 09:44:28,811 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.weight loaded from layer4.0.downsample.1.weight of shape (2048,)
2019-04-16 09:44:28,811 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.bias loaded from layer4.1.bn1.bias of shape (512,)
2019-04-16 09:44:28,811 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.weight loaded from layer4.1.bn1.weight of shape (512,)
2019-04-16 09:44:28,811 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.bias loaded from layer4.1.bn2.bias of shape (512,)
2019-04-16 09:44:28,811 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.weight loaded from layer4.1.bn2.weight of shape (512,)
2019-04-16 09:44:28,811 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.bias loaded from layer4.1.bn3.bias of shape (2048,)
2019-04-16 09:44:28,811 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.weight loaded from layer4.1.bn3.weight of shape (2048,)
2019-04-16 09:44:28,811 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)
2019-04-16 09:44:28,811 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)
2019-04-16 09:44:28,811 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)
2019-04-16 09:44:28,811 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.bias loaded from layer4.2.bn1.bias of shape (512,)
2019-04-16 09:44:28,811 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.weight loaded from layer4.2.bn1.weight of shape (512,)
2019-04-16 09:44:28,812 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.bias loaded from layer4.2.bn2.bias of shape (512,)
2019-04-16 09:44:28,812 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.weight loaded from layer4.2.bn2.weight of shape (512,)
2019-04-16 09:44:28,812 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.bias loaded from layer4.2.bn3.bias of shape (2048,)
2019-04-16 09:44:28,812 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.weight loaded from layer4.2.bn3.weight of shape (2048,)
2019-04-16 09:44:28,812 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)
2019-04-16 09:44:28,812 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)
2019-04-16 09:44:28,812 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)
2019-04-16 09:44:28,812 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,)
2019-04-16 09:44:28,812 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.weight loaded from bn1.weight of shape (64,)
2019-04-16 09:44:28,812 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
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
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
2019-04-16 09:44:28,848 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...
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loading annotations into memory...
loading annotations into memory...
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2019-04-16 09:44:48,874 maskrcnn_benchmark.trainer INFO: Start training
2019-04-16 09:45:04,999 maskrcnn_benchmark.trainer INFO: eta: 20:09:01 iter: 20 loss: 4.2540 (4.8868) loss_centerness: 0.6721 (0.6827) loss_cls: 1.0426 (1.0449) loss_reg: 2.5388 (3.1591) time: 0.6950 (0.8062) data: 0.0225 (0.0672) lr: 0.003333 max mem: 7051
2019-04-16 09:45:19,617 maskrcnn_benchmark.trainer INFO: eta: 19:12:17 iter: 40 loss: 3.1588 (4.0368) loss_centerness: 0.6638 (0.6733) loss_cls: 0.8076 (0.9305) loss_reg: 1.6586 (2.4331) time: 0.6950 (0.7685) data: 0.0278 (0.0478) lr: 0.003333 max mem: 7051
2019-04-16 09:45:33,480 maskrcnn_benchmark.trainer INFO: eta: 18:34:23 iter: 60 loss: 2.9134 (3.6434) loss_centerness: 0.6611 (0.6692) loss_cls: 0.7650 (0.8791) loss_reg: 1.4770 (2.0951) time: 0.6922 (0.7434) data: 0.0308 (0.0417) lr: 0.003333 max mem: 7051
2019-04-16 09:45:47,101 maskrcnn_benchmark.trainer INFO: eta: 18:10:46 iter: 80 loss: nan (nan) loss_centerness: 0.6573 (nan) loss_cls: 0.7112 (nan) loss_reg: 0.9977 (nan) time: 0.6785 (0.7278) data: 0.0283 (0.0387) lr: 0.003333 max mem: 7051
2019-04-16 09:46:00,404 maskrcnn_benchmark.trainer INFO: eta: 17:51:44 iter: 100 loss: nan (nan) loss_centerness: nan (nan) loss_cls: nan (nan) loss_reg: nan (nan) time: 0.6631 (0.7153) data: 0.0248 (0.0361) lr: 0.003333 max mem: 7051
Thank u for your helping
from fcos.
tianzhi0549
commented on July 18, 2024
Can you try to use python2.7? Our code is only tested with python2.7.
from fcos.
bei-startdt
commented on July 18, 2024
oh... It might be some different 3.6(I use) and 2.7. I will try with 2.7 and post my result after a later.
from fcos.
bei-startdt
commented on July 18, 2024
No handlers could be found for logger "maskrcnn_benchmark"
No handlers could be found for logger "maskrcnn_benchmark"
2019-04-16 10:33:09,935 maskrcnn_benchmark INFO: Using 4 GPUs
No handlers could be found for logger "maskrcnn_benchmark"
2019-04-16 10:33:09,935 maskrcnn_benchmark INFO: Namespace(config_file='configs/fcos/fcos_R_50_FPN_1x.yaml', distributed=True, local_rank=0, opts=['DATALOADER.NUM_WORKERS', '2', 'OUTPUT_DIR', 'training_dir/fcos_R_50_FPN_1x'], skip_test=True)
2019-04-16 10:33:09,936 maskrcnn_benchmark INFO: Collecting env info (might take some time)
2019-04-16 10:33:12,610 maskrcnn_benchmark INFO:
PyTorch version: 1.0.0
Is debug build: No
CUDA used to build PyTorch: 9.0.176
OS: Ubuntu 16.04.6 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609
CMake version: Could not collect
Python version: 2.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce GTX 1080 Ti
GPU 1: GeForce GTX 1080 Ti
GPU 2: GeForce GTX 1080 Ti
GPU 3: GeForce GTX 1080 Ti
GPU 4: GeForce GTX 1080 Ti
GPU 5: GeForce GTX 1080 Ti
GPU 6: GeForce GTX 1080 Ti
GPU 7: GeForce GTX 1080 Ti
Nvidia driver version: 415.27
cuDNN version: Could not collect
Versions of relevant libraries:
[pip] Could not collect
[conda] torch 1.0.0 pypi_0 pypi
[conda] torchvision 0.2.2.post3 pypi_0 pypi
Pillow (6.0.0)
2019-04-16 10:33:12,610 maskrcnn_benchmark INFO: Loaded configuration file configs/fcos/fcos_R_50_FPN_1x.yaml
2019-04-16 10:33:12,610 maskrcnn_benchmark INFO:
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
RPN_ONLY: True
FCOS_ON: True
BACKBONE:
CONV_BODY: "R-50-FPN-RETINANET"
RESNETS:
BACKBONE_OUT_CHANNELS: 256
RETINANET:
USE_C5: False # FCOS uses P5 instead of C5
DATASETS:
TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
TEST: ("coco_2014_minival",)
INPUT:
MIN_SIZE_TRAIN: (800,)
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.01
WEIGHT_DECAY: 0.0001
STEPS: (60000, 80000)
MAX_ITER: 90000
IMS_PER_BATCH: 16
WARMUP_METHOD: "constant"
2019-04-16 10:33:12,611 maskrcnn_benchmark INFO: Running with config:
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 2
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_RANGE_TRAIN: (-1, -1)
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-RETINANET
FREEZE_CONV_BODY_AT: 2
USE_GN: False
CLS_AGNOSTIC_BBOX_REG: False
DEVICE: cuda
FBNET:
ARCH: default
ARCH_DEF:
BN_TYPE: bn
DET_HEAD_BLOCKS: []
DET_HEAD_LAST_SCALE: 1.0
DET_HEAD_STRIDE: 0
DW_CONV_SKIP_BN: True
DW_CONV_SKIP_RELU: True
KPTS_HEAD_BLOCKS: []
KPTS_HEAD_LAST_SCALE: 0.0
KPTS_HEAD_STRIDE: 0
MASK_HEAD_BLOCKS: []
MASK_HEAD_LAST_SCALE: 0.0
MASK_HEAD_STRIDE: 0
RPN_BN_TYPE:
RPN_HEAD_BLOCKS: 0
SCALE_FACTOR: 1.0
WIDTH_DIVISOR: 1
FCOS:
FPN_STRIDES: [8, 16, 32, 64, 128]
INFERENCE_TH: 0.05
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.4
NUM_CLASSES: 81
NUM_CONVS: 4
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
FCOS_ON: True
FPN:
USE_GN: False
USE_RELU: False
GROUP_NORM:
DIM_PER_GP: -1
EPSILON: 1e-05
NUM_GROUPS: 32
KEYPOINT_ON: False
MASK_ON: False
META_ARCHITECTURE: GeneralizedRCNN
RESNETS:
BACKBONE_OUT_CHANNELS: 256
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: False
RETINANET_ON: False
ROI_BOX_HEAD:
CONV_HEAD_DIM: 256
DILATION: 1
FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 81
NUM_STACKED_CONVS: 4
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
PREDICTOR: FastRCNNPredictor
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: False
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: ResNet50Conv5ROIFeatureExtractor
MLP_HEAD_DIM: 1024
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
POSTPROCESS_MASKS: False
POSTPROCESS_MASKS_THRESHOLD: 0.5
PREDICTOR: MaskRCNNC4Predictor
RESOLUTION: 14
SHARE_BOX_FEATURE_EXTRACTOR: True
USE_GN: False
RPN:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDE: (16,)
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: 2000
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: 6000
PRE_NMS_TOP_N_TRAIN: 12000
RPN_HEAD: SingleConvRPNHead
STRADDLE_THRESH: 0
USE_FPN: False
RPN_ONLY: True
WEIGHT: catalog://ImageNetPretrained/MSRA/R-50
OUTPUT_DIR: training_dir/fcos_R_50_FPN_1x
PATHS_CATALOG: /data/bei/FCOS/maskrcnn_benchmark/config/paths_catalog.py
SOLVER:
BASE_LR: 0.01
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: 0.333333333333
WARMUP_ITERS: 500
WARMUP_METHOD: constant
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
TEST:
DETECTIONS_PER_IMG: 100
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 8
2019-04-16 10:33:13,403 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50
2019-04-16 10:33:13,404 maskrcnn_benchmark.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
2019-04-16 10:33:13,442 maskrcnn_benchmark.utils.checkpoint INFO: url https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl cached in /home/wangjiangben/.torch/models/R-50.pkl
2019-04-16 10:33:13,495 maskrcnn_benchmark.utils.c2_model_loading INFO: Remapping C2 weights
2019-04-16 10:33:13,496 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: conv1_b mapped name: conv1.bias
2019-04-16 10:33:13,496 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: conv1_w mapped name: conv1.weight
2019-04-16 10:33:13,496 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: fc1000_b mapped name: fc1000.bias
2019-04-16 10:33:13,496 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: fc1000_w mapped name: fc1000.weight
2019-04-16 10:33:13,496 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_b mapped name: layer1.0.downsample.0.bias
2019-04-16 10:33:13,496 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_b mapped name: layer1.0.downsample.1.bias
2019-04-16 10:33:13,496 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_s mapped name: layer1.0.downsample.1.weight
2019-04-16 10:33:13,496 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_w mapped name: layer1.0.downsample.0.weight
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_b mapped name: layer1.0.conv1.bias
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_b mapped name: layer1.0.bn1.bias
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_s mapped name: layer1.0.bn1.weight
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_w mapped name: layer1.0.conv1.weight
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_b mapped name: layer1.0.conv2.bias
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_b mapped name: layer1.0.bn2.bias
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_s mapped name: layer1.0.bn2.weight
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_w mapped name: layer1.0.conv2.weight
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_b mapped name: layer1.0.conv3.bias
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_bn_b mapped name: layer1.0.bn3.bias
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_bn_s mapped name: layer1.0.bn3.weight
2019-04-16 10:33:13,497 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_w mapped name: layer1.0.conv3.weight
2019-04-16 10:33:13,498 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_b mapped name: layer1.1.conv1.bias
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2019-04-16 10:33:13,553 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.2.bn1.bias loaded from layer3.2.bn1.bias of shape (256,)
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2019-04-16 10:33:13,554 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.layer3.3.bn1.bias loaded from layer3.3.bn1.bias of shape (256,)
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2019-04-16 10:33:13,555 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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2019-04-16 10:33:13,555 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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2019-04-16 10:33:13,611 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...
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Done (t=6.93s)
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2019-04-16 10:33:47,672 maskrcnn_benchmark.trainer INFO: Start training
index created!
index created!
2019-04-16 10:34:05,565 maskrcnn_benchmark.trainer INFO: eta: 22:21:28 iter: 20 loss: 4.3718 (5.2479) loss_cls: 1.0199 (1.0036) loss_centerness: 0.6694 (0.6730) loss_reg: 2.6008 (3.5713) time: 0.7003 (0.8945) data: 0.0219 (0.0851) lr: 0.003333 max mem: 7051
2019-04-16 10:34:20,310 maskrcnn_benchmark.trainer INFO: eta: 20:23:18 iter: 40 loss: 3.6584 (nan) loss_cls: 0.8155 (nan) loss_centerness: 0.6613 (nan) loss_reg: 2.1678 (nan) time: 0.6955 (0.8159) data: 0.0235 (0.0542) lr: 0.003333 max mem: 7051
2019-04-16 10:34:33,628 maskrcnn_benchmark.trainer INFO: eta: 19:08:05 iter: 60 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6679 (0.7659) data: 0.0231 (0.0439) lr: 0.003333 max mem: 7051
2019-04-16 10:34:46,920 maskrcnn_benchmark.trainer INFO: eta: 18:29:52 iter: 80 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6659 (0.7406) data: 0.0225 (0.0390) lr: 0.003333 max mem: 7051
2019-04-16 10:35:00,301 maskrcnn_benchmark.trainer INFO: eta: 18:08:11 iter: 100 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6678 (0.7263) data: 0.0223 (0.0358) lr: 0.003333 max mem: 7051
2019-04-16 10:35:13,445 maskrcnn_benchmark.trainer INFO: eta: 17:50:42 iter: 120 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6566 (0.7148) data: 0.0222 (0.0340) lr: 0.003333 max mem: 7051
2019-04-16 10:35:26,752 maskrcnn_benchmark.trainer INFO: eta: 17:39:53 iter: 140 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6618 (0.7077) data: 0.0255 (0.0331) lr: 0.003333 max mem: 7051
2019-04-16 10:35:40,196 maskrcnn_benchmark.trainer INFO: eta: 17:33:01 iter: 160 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6735 (0.7033) data: 0.0239 (0.0320) lr: 0.003333 max mem: 7051
2019-04-16 10:35:53,716 maskrcnn_benchmark.trainer INFO: eta: 17:28:14 iter: 180 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6763 (0.7002) data: 0.0236 (0.0312) lr: 0.003333 max mem: 7051
2019-04-16 10:36:07,115 maskrcnn_benchmark.trainer INFO: eta: 17:23:28 iter: 200 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6638 (0.6972) data: 0.0252 (0.0307) lr: 0.003333 max mem: 7051
2019-04-16 10:36:20,502 maskrcnn_benchmark.trainer INFO: eta: 17:19:27 iter: 220 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6729 (0.6947) data: 0.0224 (0.0301) lr: 0.003333 max mem: 7055
2019-04-16 10:36:33,843 maskrcnn_benchmark.trainer INFO: eta: 17:15:47 iter: 240 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6620 (0.6924) data: 0.0225 (0.0296) lr: 0.003333 max mem: 7055
2019-04-16 10:36:47,305 maskrcnn_benchmark.trainer INFO: eta: 17:13:20 iter: 260 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6750 (0.6909) data: 0.0247 (0.0293) lr: 0.003333 max mem: 7055
2019-04-16 10:37:00,747 maskrcnn_benchmark.trainer INFO: eta: 17:11:06 iter: 280 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6702 (0.6895) data: 0.0248 (0.0290) lr: 0.003333 max mem: 7055
2019-04-16 10:37:14,164 maskrcnn_benchmark.trainer INFO: eta: 17:09:00 iter: 300 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6720 (0.6883) data: 0.0256 (0.0288) lr: 0.003333 max mem: 7055
2019-04-16 10:37:27,489 maskrcnn_benchmark.trainer INFO: eta: 17:06:42 iter: 320 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6652 (0.6869) data: 0.0250 (0.0286) lr: 0.003333 max mem: 7055
2019-04-16 10:37:40,791 maskrcnn_benchmark.trainer INFO: eta: 17:04:34 iter: 340 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6599 (0.6856) data: 0.0223 (0.0283) lr: 0.003333 max mem: 7055
2019-04-16 10:37:54,031 maskrcnn_benchmark.trainer INFO: eta: 17:02:22 iter: 360 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6606 (0.6843) data: 0.0233 (0.0281) lr: 0.003333 max mem: 7055
2019-04-16 10:38:07,475 maskrcnn_benchmark.trainer INFO: eta: 17:01:12 iter: 380 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6730 (0.6837) data: 0.0253 (0.0281) lr: 0.003333 max mem: 7055
2019-04-16 10:38:20,803 maskrcnn_benchmark.trainer INFO: eta: 16:59:40 iter: 400 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6687 (0.6828) data: 0.0227 (0.0279) lr: 0.003333 max mem: 7055
2019-04-16 10:38:34,255 maskrcnn_benchmark.trainer INFO: eta: 16:58:43 iter: 420 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6746 (0.6823) data: 0.0239 (0.0277) lr: 0.003333 max mem: 7055
2019-04-16 10:38:47,661 maskrcnn_benchmark.trainer INFO: eta: 16:57:40 iter: 440 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6706 (0.6818) data: 0.0223 (0.0275) lr: 0.003333 max mem: 7055
2019-04-16 10:39:00,960 maskrcnn_benchmark.trainer INFO: eta: 16:56:21 iter: 460 loss: nan (nan) loss_cls: nan (nan) loss_centerness: nan (nan) loss_reg: nan (nan) time: 0.6630 (0.6811) data: 0.0243 (0.0274) lr: 0.003333 max mem: 7055
I try with python2.7, but loss nan again...
from fcos.
YanShuo1992
commented on July 18, 2024
@tianzhi0549 The python version should not be the cause since I tested the code with python3.7.
from fcos.
tianzhi0549
commented on July 18, 2024
@YanShuo1992 OK. Thank you for pointing that out. Maybe some dependencies are different and cause the NAN. Just try to clip gradients. I think it can prevent the loss from exploding.
from fcos.
tianzhi0549
commented on July 18, 2024
@bei-startdt Happy to know that you have solved it:-).
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gittigxuy
commented on July 18, 2024
@tianzhi0549,@bei-startdt ,the same problem,how could I deal with the loss nan detaillly?I don't understand how to clip_gradient,I encounter the same issue when I train coco2014 dataset
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tianzhi0549
commented on July 18, 2024
@gittigxuy Did the loss become NAN many times?
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tianzhi0549
commented on July 18, 2024
@gittigxuy It should be because your training batch size is only 1. It is too small. We recommend using batch size >= 8.
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gittigxuy
commented on July 18, 2024
Thanks,I have solved the problem,you are right,just config the batch_size>=8
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yonghongl
commented on July 18, 2024
@gittigxuy It should be because your training batch size is only 1. It is too small. We recommend using batch size >= 8.
why does batch size too small cause nan?
It's because when calculate sigmoid_focal_loss, torch.log() will output inf result, i.e., torch.log(torch.tensor(1e-49)), then the loss will be nan.
You can modify the following line with p = torch.clamp(torch.sigmoid(logits), min=eps, max= 1 - eps) and make eps as 1e-7, this works for ANY batch size.
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Related Issues (20)
- questions
- About exporting ONNX model
- error python setup.py build develop --no-deps
- some issues on reproducing result of Figure 7 HOT 3
- Questions about details of inference speed.
- Can I use it on CPU?
- Can we use this FCOS algorithm for counting the objects in images??
- May I ask you for a train log file?
- Which is the accuracy index in the paper？
- Error pip install git+https://github.com/tianzhi0549/FCOS.git
- use my dataset to train FCOS net，why the loss is nan? HOT 2
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- Testing with test dataset while training
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- assert len(proposal_losses) == 1 and proposal_losses["zero"] == 0 # loss_dict should be empty dict
- tools/train_net.py FAILED HOT 1
- Convert onnx
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