Comments (7)
Here is my log file:
OS: Ubuntu 16.04.5 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: 10.0.130
GPU models and configuration:
GPU 0: GeForce RTX 2080 Ti
GPU 1: GeForce RTX 2080 Ti
Nvidia driver version: 410.48
cuDNN version: Probably one of the following:
/usr/local/cuda-10.0/targets/x86_64-linux/lib/libcudnn.so.7.4.1
/usr/local/cuda-10.0/targets/x86_64-linux/lib/libcudnn_static.a
Versions of relevant libraries:
[pip] Could not collect
[conda] torch 1.0.0
[conda] torchvision 0.2.2.post3
Pillow (6.0.0)
2019-05-04 00:17:09,221 maskrcnn_benchmark INFO: Loaded configuration file /home/abc/code/FCOS/configs/fcos/fcos_R_101_FPN_2x.yaml
2019-05-04 00:17:09,221 maskrcnn_benchmark INFO:
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-101"
RPN_ONLY: True
FCOS_ON: True
BACKBONE:
CONV_BODY: "R-101-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",)
TRAIN: ("coco_citypersons_train", )
TEST: ("coco_citypersons_val",)
INPUT:
MIN_SIZE_RANGE_TRAIN: (640, 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: (120000, 160000)
MAX_ITER: 180000
IMS_PER_BATCH: 16
WARMUP_METHOD: "constant"
2019-05-04 00:17:09,223 maskrcnn_benchmark INFO: Running with config:
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 4
SIZE_DIVISIBILITY: 32
DATASETS:
TEST: ('coco_citypersons_val',)
TRAIN: ('coco_citypersons_train',)
INPUT:
MAX_SIZE_TEST: 1333
MAX_SIZE_TRAIN: 1333
MIN_SIZE_RANGE_TRAIN: (640, 800)
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-101-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: 2
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: 2
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: 2
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: 1000
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-101
OUTPUT_DIR: training_dir/FCOS_0503
PATHS_CATALOG: /home/abc/code/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: 180000
MOMENTUM: 0.9
STEPS: (120000, 160000)
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
from fcos.
@gittigxuy It seems that you are using another dataset. Did R-101 work normally with COCO?
from fcos.
no,it encounter same problem when I run coco2014 with resnet101,could you please tell me how to fix the code?
from fcos.
@gittigxuy Are you using the latest version? I have tested it with R-101 and it works normally with coco.
from fcos.
yes,I git from your April 12 th version,I could not deal with the problem.I also git the newest code but the same problem.what should I do?thanks
from fcos.
@gittigxuy We are sorry. It was a bug and has been fixed. Please use the latest code.
from fcos.
Thanks,fix the bug
from fcos.
Related Issues (20)
- 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 HOT 1
- use my dataset to train FCOS net,why the loss is nan? HOT 2
- Can’t calculate the Params and FLOPs of Backbone
- Testing with test dataset while training
- ValueError: Unknown CUDA arch (8.6) or GPU not supported HOT 2
- weird time consuming of FCOS head when changing the backbone
- Problem with the last line of Testing-only installation HOT 1
- Deepcopy returns TypeError: 'int' object is not callable HOT 1
- 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
- Segmentation fault (core dumped)
- gempy to load csv data
- ImportError: cannot import name '_C'
- Why regression head wasn't normalized ?
- ValueError: num_samples should be a positive integer value, but got num_samples=0
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from fcos.