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Copyright (c) 2012-2021 Gabriel Ilharco, Mitchell Wortsman, | ||
Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, | ||
John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, | ||
Ludwig Schmidt | ||
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Permission is hereby granted, free of charge, to any person obtaining | ||
a copy of this software and associated documentation files (the | ||
"Software"), to deal in the Software without restriction, including | ||
without limitation the rights to use, copy, modify, merge, publish, | ||
distribute, sublicense, and/or sell copies of the Software, and to | ||
permit persons to whom the Software is furnished to do so, subject to | ||
the following conditions: | ||
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The above copyright notice and this permission notice shall be | ||
included in all copies or substantial portions of the Software. | ||
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, | ||
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF | ||
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND | ||
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE | ||
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION | ||
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION | ||
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
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try: | ||
import open_clip | ||
except ImportError: | ||
pass | ||
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def adapt_clipconvnext(backbone): | ||
"""Adapt CLIPConvNeXt's downsampling to work with custom image size in OpenPifPaf.""" | ||
backbone.visual.trunk.stem[0].padding = (2, 2) | ||
backbone.visual.trunk.stages[1].downsample[1].padding = (1, 1) | ||
backbone.visual.trunk.stages[2].downsample[1].padding = (1, 1) | ||
backbone.visual.trunk.stages[3].downsample[1].padding = (1, 1) | ||
return backbone | ||
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def clipconvnext(model_name=None, pretraining_dataset=None): | ||
backbone, _, _ = open_clip.create_model_and_transforms(model_name, | ||
pretrained=pretraining_dataset) | ||
backbone = adapt_clipconvnext(backbone) | ||
return backbone | ||
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def clipconvnextbase(pretrained=True): | ||
model_name, pretraining_dataset = 'convnext_base_w_320', 'laion_aesthetic_s13b_b82k_augreg' | ||
out_features = 1024 | ||
if not pretrained: | ||
pretraining_dataset = None | ||
backbone = clipconvnext(model_name=model_name, pretraining_dataset=pretraining_dataset) | ||
return backbone, out_features |
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