# Score threshold for visualization __C.VIS_TH = 0.9 # Expected results should take the form of a list of expectations, each # specified by four elements (dataset, task, metric, expected value). For # example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387]] __C.EXPECTED_RESULTS = [] # Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS __C.EXPECTED_RESULTS_RTOL = 0.1 __C.EXPECTED_RESULTS_ATOL = 0.005 # Set to send email in case of an EXPECTED_RESULTS failure __C.EXPECTED_RESULTS_EMAIL = '' # ------------------------------ # Data directory __C.DATA_DIR = '/home/space/wwt/ECCV2020/data' # [Deprecate] __C.POOLING_MODE = 'crop' # [Deprecate] Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 __C.CROP_RESIZE_WITH_MAX_POOL = True # [Infered value] __C.CUDA = False __C.DEBUG = False # [Infered value]
# Score threshold for visualization __C.VIS_TH = 0.9 # Expected results should take the form of a list of expectations, each # specified by four elements (dataset, task, metric, expected value). For # example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387]] __C.EXPECTED_RESULTS = [] # Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS __C.EXPECTED_RESULTS_RTOL = 0.1 __C.EXPECTED_RESULTS_ATOL = 0.005 # Set to send email in case of an EXPECTED_RESULTS failure __C.EXPECTED_RESULTS_EMAIL = '' # ------------------------------ # Data directory __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) # [Deprecate] __C.POOLING_MODE = 'crop' # [Deprecate] Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 __C.CROP_RESIZE_WITH_MAX_POOL = True # [Infered value] __C.CUDA = False __C.DEBUG = False # [Infered value]
__C.VIS_TH = 0.9 # Expected results should take the form of a list of expectations, each # specified by four elements (dataset, task, metric, expected value). For # example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387]] __C.EXPECTED_RESULTS = [] # Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS __C.EXPECTED_RESULTS_RTOL = 0.1 __C.EXPECTED_RESULTS_ATOL = 0.005 # Set to send email in case of an EXPECTED_RESULTS failure __C.EXPECTED_RESULTS_EMAIL = '' # ------------------------------ # Data directory # __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) __C.DATA_DIR = '/data1' # [Deprecate] __C.POOLING_MODE = 'crop' # [Deprecate] Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 __C.CROP_RESIZE_WITH_MAX_POOL = True # [Infered value] __C.CUDA = False __C.DEBUG = False # [Infered value]
# Score threshold for visualization
__C.VIS_TH = 0.9
# Expected results should take the form of a list of expectations, each
# specified by four elements (dataset, task, metric, expected value). For
# example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387]]
__C.EXPECTED_RESULTS = []
# Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS
__C.EXPECTED_RESULTS_RTOL = 0.1
__C.EXPECTED_RESULTS_ATOL = 0.005
# Set to send email in case of an EXPECTED_RESULTS failure
__C.EXPECTED_RESULTS_EMAIL = ''
# ------------------------------
# Data directory
__C.DATA_DIR = osp.abspath(
osp.join(__C.ROOT_DIR, '..', '..', 'data', 'mask_rcnn'))
# [Deprecate]
__C.POOLING_MODE = 'crop'
# [Deprecate] Size of the pooled region after RoI pooling
__C.POOLING_SIZE = 7
__C.CROP_RESIZE_WITH_MAX_POOL = True
# [Infered value]
__C.CUDA = False
__C.DEBUG = False
# [Infered value]
__C.VIS_TH = 0.9 # Expected results should take the form of a list of expectations, each # specified by four elements (dataset, task, metric, expected value). For # example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387]] __C.EXPECTED_RESULTS = [] # Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS __C.EXPECTED_RESULTS_RTOL = 0.1 __C.EXPECTED_RESULTS_ATOL = 0.005 # Set to send email in case of an EXPECTED_RESULTS failure __C.EXPECTED_RESULTS_EMAIL = '' # ------------------------------ # Data directory #__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) __C.DATA_DIR = '/media/wrc/8EF06A4CF06A3A9B/kitti/training' # [Deprecate] __C.POOLING_MODE = 'crop' # [Deprecate] Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 __C.CROP_RESIZE_WITH_MAX_POOL = True # [Infered value] __C.CUDA = False __C.DEBUG = False # [Infered value]
# Score threshold for visualization __C.VIS_TH = 0.5 # Expected results should take the form of a list of expectations, each # specified by four elements (dataset, task, metric, expected value). For # example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387]] __C.EXPECTED_RESULTS = [] # Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS __C.EXPECTED_RESULTS_RTOL = 0.1 __C.EXPECTED_RESULTS_ATOL = 0.005 # Set to send email in case of an EXPECTED_RESULTS failure __C.EXPECTED_RESULTS_EMAIL = '' # ------------------------------ # Data directory __C.DATA_DIR = osp.join(__C.ROOT_DIR, 'data') # [Deprecate] __C.POOLING_MODE = 'crop' # [Deprecate] Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 __C.CROP_RESIZE_WITH_MAX_POOL = True # [Infered value] __C.CUDA = False __C.DEBUG = False # [Infered value]
# Score threshold for visualization __C.VIS_TH = 0.9 # Expected results should take the form of a list of expectations, each # specified by four elements (dataset, task, metric, expected value). For # example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387]] __C.EXPECTED_RESULTS = [] # Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS __C.EXPECTED_RESULTS_RTOL = 0.1 __C.EXPECTED_RESULTS_ATOL = 0.005 # Set to send email in case of an EXPECTED_RESULTS failure __C.EXPECTED_RESULTS_EMAIL = '' # ------------------------------ # Data directory __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) # [Deprecate] __C.POOLING_MODE = 'crop' # [Deprecate] Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 __C.CROP_RESIZE_WITH_MAX_POOL = True # [Infered value] __C.CUDA = False __C.DEBUG = False # [Infered value]
# Score threshold for visualization
__C.VIS_TH = 0.6 #0.9
# Expected results should take the form of a list of expectations, each
# specified by four elements (dataset, task, metric, expected value). For
# example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387]]
__C.EXPECTED_RESULTS = []
# Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS
__C.EXPECTED_RESULTS_RTOL = 0.1
__C.EXPECTED_RESULTS_ATOL = 0.005
# Set to send email in case of an EXPECTED_RESULTS failure
__C.EXPECTED_RESULTS_EMAIL = ''
# ------------------------------
# Data directory
__C.DATA_DIR = osp.abspath(
osp.join(__C.ROOT_DIR, 'preprocess/dataset/iSAID_patches'))
# [Deprecate]
__C.POOLING_MODE = 'crop'
# [Deprecate] Size of the pooled region after RoI pooling
__C.POOLING_SIZE = 7
__C.CROP_RESIZE_WITH_MAX_POOL = True
# [Infered value]
__C.CUDA = True
__C.DEBUG = False
# [Infered value]
import logging import numpy as np import os import os.path as osp import yaml from utils.io import cache_url logger = logging.getLogger(__name__) __C = AttrDict() # Consumers can get config by: # from core.config import cfg cfg = __C __C.DATA_DIR = b'datasets/large_scale_VRD' __C.OUTPUT_DIR = b'checkpoints' # ---------------------------------------------------------------------------- # # Misc options # ---------------------------------------------------------------------------- # # Number of GPUs to use # __C.NUM_GPUS = 1 # Use NCCL for all reduce, otherwise use muji # NCCL seems to work ok for 2 GPUs, but become prone to deadlocks when using # 4 or 8 __C.USE_NCCL = False # The mapping from image coordinates to feature map coordinates might cause # some boxes that are distinct in image space to become identical in feature
