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Add Image Processors (#19796)

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* Add CLIP image processor

* Crop size as dict too

* Update warning

* Actually use logger this time

* Normalize doesn't change dtype of input

* Add perceiver image processor

* Tidy up

* Add DPT image processor

* Add Vilt image processor

* Tidy up

* Add poolformer image processor

* Tidy up

* Add LayoutLM v2 and v3 imsge processors

* Tidy up

* Add Flava image processor

* Tidy up

* Add deit image processor

* Tidy up

* Add ConvNext image processor

* Tidy up

* Add levit image processor

* Add segformer image processor

* Add in post processing

* Fix up

* Add ImageGPT image processor

* Fixup

* Add mobilevit image processor

* Tidy up

* Add postprocessing

* Fixup

* Add VideoMAE image processor

* Tidy up

* Add ImageGPT image processor

* Fixup

* Add ViT image processor

* Tidy up

* Add beit image processor

* Add mobilevit image processor

* Tidy up

* Add postprocessing

* Fixup

* Fix up

* Fix flava and remove tree module

* Fix image classification pipeline failing tests

* Update feature extractor in trainer scripts

* Update pad_if_smaller to accept tuple and int size

* Update for image segmentation pipeline

* Update src/transformers/models/perceiver/image_processing_perceiver.py

Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>

* Update src/transformers/image_processing_utils.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/beit/image_processing_beit.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* PR comments - docstrings; remove accidentally added resize; var names

* Update docstrings

* Add exception if size is not in the right format

* Fix exception check

* Fix up

* Use shortest_edge in tuple in script

Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
This commit is contained in:
amyeroberts
2022-11-02 11:57:36 +00:00
committed by GitHub
parent 2e3452af0f
commit a6b7759880
65 changed files with 7060 additions and 3590 deletions
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10
examples/pytorch/image-classification/run_image_classification.py
View File

@@ -291,10 +291,14 @@ def main():
)
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in feature_extractor.size:
size = feature_extractor.size["shortest_edge"]
else:
size = (feature_extractor.size["height"], feature_extractor.size["width"])
normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
_train_transforms = Compose(
[
RandomResizedCrop(feature_extractor.size),
RandomResizedCrop(size),
RandomHorizontalFlip(),
ToTensor(),
normalize,
@@ -302,8 +306,8 @@ def main():
)
_val_transforms = Compose(
[
Resize(feature_extractor.size),
CenterCrop(feature_extractor.size),
Resize(size),
CenterCrop(size),
ToTensor(),
normalize,
]

10
examples/pytorch/image-classification/run_image_classification_no_trainer.py
View File

@@ -315,10 +315,14 @@ def main():
# Preprocessing the datasets
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in feature_extractor.size:
size = feature_extractor.size["shortest_edge"]
else:
size = (feature_extractor.size["height"], feature_extractor.size["width"])
normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
train_transforms = Compose(
[
RandomResizedCrop(feature_extractor.size),
RandomResizedCrop(size),
RandomHorizontalFlip(),
ToTensor(),
normalize,
@@ -326,8 +330,8 @@ def main():
)
val_transforms = Compose(
[
Resize(feature_extractor.size),
CenterCrop(feature_extractor.size),
Resize(size),
CenterCrop(size),
ToTensor(),
normalize,
]