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 | |
| import pytest | |
| from mmcv.utils import build_from_cfg | |
| from mmseg.datasets.builder import PIPELINES | |
| def test_multi_scale_flip_aug(): | |
| # test assertion if img_scale=None, img_ratios=1 (not float). | |
| with pytest.raises(AssertionError): | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=None, | |
| img_ratios=1, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| build_from_cfg(tta_transform, PIPELINES) | |
| # test assertion if img_scale=None, img_ratios=None. | |
| with pytest.raises(AssertionError): | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=None, | |
| img_ratios=None, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| build_from_cfg(tta_transform, PIPELINES) | |
| # test assertion if img_scale=(512, 512), img_ratios=1 (not float). | |
| with pytest.raises(AssertionError): | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(512, 512), | |
| img_ratios=1, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| build_from_cfg(tta_transform, PIPELINES) | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(512, 512), | |
| img_ratios=[0.5, 1.0, 2.0], | |
| flip=False, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| tta_module = build_from_cfg(tta_transform, PIPELINES) | |
| results = dict() | |
| # (288, 512, 3) | |
| img = mmcv.imread( | |
| osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color') | |
| results['img'] = img | |
| results['img_shape'] = img.shape | |
| results['ori_shape'] = img.shape | |
| # Set initial values for default meta_keys | |
| results['pad_shape'] = img.shape | |
| results['scale_factor'] = 1.0 | |
| tta_results = tta_module(results.copy()) | |
| assert tta_results['scale'] == [(256, 256), (512, 512), (1024, 1024)] | |
| assert tta_results['flip'] == [False, False, False] | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(512, 512), | |
| img_ratios=[0.5, 1.0, 2.0], | |
| flip=True, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| tta_module = build_from_cfg(tta_transform, PIPELINES) | |
| tta_results = tta_module(results.copy()) | |
| assert tta_results['scale'] == [(256, 256), (256, 256), (512, 512), | |
| (512, 512), (1024, 1024), (1024, 1024)] | |
| assert tta_results['flip'] == [False, True, False, True, False, True] | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(512, 512), | |
| img_ratios=1.0, | |
| flip=False, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| tta_module = build_from_cfg(tta_transform, PIPELINES) | |
| tta_results = tta_module(results.copy()) | |
| assert tta_results['scale'] == [(512, 512)] | |
| assert tta_results['flip'] == [False] | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(512, 512), | |
| img_ratios=1.0, | |
| flip=True, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| tta_module = build_from_cfg(tta_transform, PIPELINES) | |
| tta_results = tta_module(results.copy()) | |
| assert tta_results['scale'] == [(512, 512), (512, 512)] | |
| assert tta_results['flip'] == [False, True] | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=None, | |
| img_ratios=[0.5, 1.0, 2.0], | |
| flip=False, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| tta_module = build_from_cfg(tta_transform, PIPELINES) | |
| tta_results = tta_module(results.copy()) | |
| assert tta_results['scale'] == [(256, 144), (512, 288), (1024, 576)] | |
| assert tta_results['flip'] == [False, False, False] | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=None, | |
| img_ratios=[0.5, 1.0, 2.0], | |
| flip=True, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| tta_module = build_from_cfg(tta_transform, PIPELINES) | |
| tta_results = tta_module(results.copy()) | |
| assert tta_results['scale'] == [(256, 144), (256, 144), (512, 288), | |
| (512, 288), (1024, 576), (1024, 576)] | |
| assert tta_results['flip'] == [False, True, False, True, False, True] | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=[(256, 256), (512, 512), (1024, 1024)], | |
| img_ratios=None, | |
| flip=False, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| tta_module = build_from_cfg(tta_transform, PIPELINES) | |
| tta_results = tta_module(results.copy()) | |
| assert tta_results['scale'] == [(256, 256), (512, 512), (1024, 1024)] | |
| assert tta_results['flip'] == [False, False, False] | |
| tta_transform = dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=[(256, 256), (512, 512), (1024, 1024)], | |
| img_ratios=None, | |
| flip=True, | |
| transforms=[dict(type='Resize', keep_ratio=False)], | |
| ) | |
| tta_module = build_from_cfg(tta_transform, PIPELINES) | |
| tta_results = tta_module(results.copy()) | |
| assert tta_results['scale'] == [(256, 256), (256, 256), (512, 512), | |
| (512, 512), (1024, 1024), (1024, 1024)] | |
| assert tta_results['flip'] == [False, True, False, True, False, True] | |