parent
f87c05308b
commit
b732fae265
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@ -1,6 +1,6 @@
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repos:
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- repo: https://gitlab.com/pycqa/flake8.git
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rev: 3.8.3
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rev: 5.0.4
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hooks:
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- id: flake8
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- repo: https://github.com/PyCQA/isort
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@ -8,11 +8,11 @@ repos:
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hooks:
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- id: isort
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- repo: https://github.com/pre-commit/mirrors-yapf
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rev: v0.30.0
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rev: v0.32.0
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hooks:
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- id: yapf
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v3.1.0
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rev: v4.3.0
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hooks:
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- id: trailing-whitespace
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- id: check-yaml
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@ -34,7 +34,7 @@ repos:
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- mdformat_frontmatter
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- linkify-it-py
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- repo: https://github.com/codespell-project/codespell
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rev: v2.1.0
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rev: v2.2.1
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hooks:
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- id: codespell
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- repo: https://github.com/myint/docformatter
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@ -53,7 +53,7 @@ repos:
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files: ^configs/.*\.md$
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require_serial: true
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- repo: https://github.com/asottile/pyupgrade
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rev: v2.32.1
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rev: v3.0.0
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hooks:
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- id: pyupgrade
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args: ["--py36-plus"]
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@ -43,7 +43,7 @@ We recommend that users follow our best practices to install MMSegmentation. How
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```shell
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pip install -U openmim
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mim install 'mmcv>=2.0.0rc1'
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mim install mmmenigne
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mim install mmenigne
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```
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**Step 1.** Install MMSegmentation.
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@ -95,7 +95,7 @@ wget https://user-images.githubusercontent.com/24582831/189833109-eddad58f-f777-
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wget https://user-images.githubusercontent.com/24582831/189833143-15f60f8a-4d1e-4cbb-a6e7-5e2233869fac.png --output-document aachen_000000_000019_gtFine_labelTrainIds.png
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```
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Then you can find their local path and use the scrips below to visualize:
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Then you can find their local path and use the scripts below to visualize:
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```python
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import mmcv
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@ -335,7 +335,7 @@ class VisionTransformer(BaseModule):
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constant_init(m, val=1.0, bias=0.)
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def _pos_embeding(self, patched_img, hw_shape, pos_embed):
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"""Positiong embeding method.
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"""Positioning embeding method.
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Resize the pos_embed, if the input image size doesn't match
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the training size.
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@ -78,7 +78,7 @@ def sigmoid_focal_loss(pred,
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valid_mask=None,
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reduction='mean',
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avg_factor=None):
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r"""A warpper of cuda version `Focal Loss
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r"""A wrapper of cuda version `Focal Loss
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<https://arxiv.org/abs/1708.02002>`_.
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Args:
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pred (torch.Tensor): The prediction with shape (N, C), C is the number
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Loading…
Reference in New Issue