Image Segmentation
Transformers
PyTorch
English
garment-mask-generation
image-inpainting
fashion
garment-mask
densepose
human-parsing
Instructions to use Ekliipce/wearit-garment-mask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ekliipce/wearit-garment-mask with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Ekliipce/wearit-garment-mask")# Load model directly from transformers import GarmentMaskPipeline model = GarmentMaskPipeline.from_pretrained("Ekliipce/wearit-garment-mask", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,056 Bytes
436df5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | # Copyright (c) Facebook, Inc. and its affiliates.
import cloudpickle
class PicklableWrapper:
"""
Wrap an object to make it more picklable, note that it uses
heavy weight serialization libraries that are slower than pickle.
It's best to use it only on closures (which are usually not picklable).
This is a simplified version of
https://github.com/joblib/joblib/blob/master/joblib/externals/loky/cloudpickle_wrapper.py
"""
def __init__(self, obj):
while isinstance(obj, PicklableWrapper):
# Wrapping an object twice is no-op
obj = obj._obj
self._obj = obj
def __reduce__(self):
s = cloudpickle.dumps(self._obj)
return cloudpickle.loads, (s,)
def __call__(self, *args, **kwargs):
return self._obj(*args, **kwargs)
def __getattr__(self, attr):
# Ensure that the wrapped object can be used seamlessly as the previous object.
if attr not in ["_obj"]:
return getattr(self._obj, attr)
return getattr(self, attr)
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