chore: bump open-clip-torch to v2.8.0 #883
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jemmyshin
suggested changes
Dec 26, 2022
server/clip_server/model/model.py
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| @@ -111,6 +120,8 @@ class CLIPVisionCfg: | |||
| patch_size: int = 16 | |||
| image_size: Union[Tuple[int, int], int] = 224 | |||
| ls_init_value: Optional[float] = None # layer scale initial value | |||
| patch_dropout: float = 0.0 # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results | |||
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why we need dropout during inference? It's not training process
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Then we can remove this args if we don't need it
Codecov Report
@@ Coverage Diff @@
## main #883 +/- ##
===========================================
- Coverage 83.06% 71.78% -11.28%
===========================================
Files 22 22
Lines 1529 1531 +2
===========================================
- Hits 1270 1099 -171
- Misses 259 432 +173
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jemmyshin
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Dec 26, 2022
| @@ -12,7 +12,7 @@ def test_server_download(tmpdir): | |||
| download_model( | |||
| url='https://docarray.jina.ai/_static/favicon.png', | |||
| target_folder=tmpdir, | |||
| md5sum='a084999188f4290e2654aec43207ff2e', | |||
| md5sum='66ea4817d73514888dcf6c7d2b00016d', | |||
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is there any method we can auto-calculate the md5 in order not to block by this image chaning?
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This PR adapts recent changes introduced in open-clip-torch v2.8+