| import os |
| from easydict import EasyDict as edict |
| from tools.rand_sampler import RandCropper, RandPadder |
| |
| cfg = edict() |
| cfg.ROOT_DIR = os.path.join(os.path.dirname(__file__), '..') |
| |
| # training |
| cfg.TRAIN = edict() |
| cfg.TRAIN.RAND_SAMPLERS = [RandCropper(min_scale=1., max_trials=1, max_sample=1), |
| RandCropper(min_scale=.3, min_aspect_ratio=.5, max_aspect_ratio=2., min_overlap=.1), |
| RandCropper(min_scale=.3, min_aspect_ratio=.5, max_aspect_ratio=2., min_overlap=.3), |
| RandCropper(min_scale=.3, min_aspect_ratio=.5, max_aspect_ratio=2., min_overlap=.5), |
| RandCropper(min_scale=.3, min_aspect_ratio=.5, max_aspect_ratio=2., min_overlap=.7), |
| RandPadder(max_scale=2., min_aspect_ratio=.5, max_aspect_ratio=2., min_gt_scale=.05), |
| RandPadder(max_scale=3., min_aspect_ratio=.5, max_aspect_ratio=2., min_gt_scale=.05), |
| RandPadder(max_scale=4., min_aspect_ratio=.5, max_aspect_ratio=2., min_gt_scale=.05),] |
| # cfg.TRAIN.RAND_SAMPLERS = [] |
| cfg.TRAIN.RAND_MIRROR = True |
| cfg.TRAIN.INIT_SHUFFLE = True |
| cfg.TRAIN.EPOCH_SHUFFLE = True # shuffle training list after each epoch |
| cfg.TRAIN.RAND_SEED = None |
| cfg.TRAIN.RESIZE_EPOCH = 1 # save model every N epoch |
| |
| |
| # validation |
| cfg.VALID = edict() |
| cfg.VALID.RAND_SAMPLERS = [] |
| cfg.VALID.RAND_MIRROR = True |
| cfg.VALID.INIT_SHUFFLE = True |
| cfg.VALID.EPOCH_SHUFFLE = True |
| cfg.VALID.RAND_SEED = None |
