Instructions to use mccaly/test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mccaly/test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mccaly/test2")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("mccaly/test2") model = UperNetForSemanticSegmentation.from_pretrained("mccaly/test2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| import os.path as osp | |
| import mmcv | |
| from mmseg.apis import inference_segmentor, init_segmentor | |
| def test_test_time_augmentation_on_cpu(): | |
| config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py' | |
| config = mmcv.Config.fromfile(config_file) | |
| # Remove pretrain model download for testing | |
| config.model.pretrained = None | |
| # Replace SyncBN with BN to inference on CPU | |
| norm_cfg = dict(type='BN', requires_grad=True) | |
| config.model.backbone.norm_cfg = norm_cfg | |
| config.model.decode_head.norm_cfg = norm_cfg | |
| config.model.auxiliary_head.norm_cfg = norm_cfg | |
| # Enable test time augmentation | |
| config.data.test.pipeline[1].flip = True | |
| checkpoint_file = None | |
| model = init_segmentor(config, checkpoint_file, device='cpu') | |
| img = mmcv.imread( | |
| osp.join(osp.dirname(__file__), 'data/color.jpg'), 'color') | |
| result = inference_segmentor(model, img) | |
| assert result[0].shape == (288, 512) | |