| |
| import logging |
| import numpy as np |
| import pycocotools.mask as mask_util |
| import torch |
| from fvcore.common.file_io import PathManager |
| from PIL import Image |
|
|
|
|
| from detectron2.data import transforms as T |
| from .transforms.custom_augmentation_impl import EfficientDetResizeCrop |
|
|
| def build_custom_augmentation(cfg, is_train, scale=None, size=None, \ |
| min_size=None, max_size=None): |
| """ |
| Create a list of default :class:`Augmentation` from config. |
| Now it includes resizing and flipping. |
| |
| Returns: |
| list[Augmentation] |
| """ |
| if cfg.INPUT.CUSTOM_AUG == 'ResizeShortestEdge': |
| if is_train: |
| min_size = cfg.INPUT.MIN_SIZE_TRAIN if min_size is None else min_size |
| max_size = cfg.INPUT.MAX_SIZE_TRAIN if max_size is None else max_size |
| sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING |
| else: |
| min_size = cfg.INPUT.MIN_SIZE_TEST |
| max_size = cfg.INPUT.MAX_SIZE_TEST |
| sample_style = "choice" |
| augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)] |
| elif cfg.INPUT.CUSTOM_AUG == 'EfficientDetResizeCrop': |
| if is_train: |
| scale = cfg.INPUT.SCALE_RANGE if scale is None else scale |
| size = cfg.INPUT.TRAIN_SIZE if size is None else size |
| else: |
| scale = (1, 1) |
| size = cfg.INPUT.TEST_SIZE |
| augmentation = [EfficientDetResizeCrop(size, scale)] |
| else: |
| assert 0, cfg.INPUT.CUSTOM_AUG |
|
|
| if is_train: |
| augmentation.append(T.RandomFlip()) |
| return augmentation |
|
|
|
|
| build_custom_transform_gen = build_custom_augmentation |
| """ |
| Alias for backward-compatibility. |
| """ |
