GitLab

Add missing spaces in adjacent strings

checkpoint path

* docs: add space to docs

* docs: remove reduntant space

* Revert frozen training arguments

* TODO

* Make training args fully immutable

* Working tests, PyTorch

* In test_trainer

* during testing

* Use proper dataclass way

* Fix test

* Another one

* Fix tf

* Lingering slow

* Exception

* Clean

* add missing tokens

* fix

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Load else

* New approach

* Propagate

* pytorch examples

* pytorch mim no trainer

* cookiecutter

* flax examples

* missed line in pytorch run_glue

* tensorflow examples

* tensorflow run_clip

* tensorflow run_mlm

* tensorflow run_ner

* tensorflow run_clm

* pytorch example from_configs

* pytorch no trainer examples

* Revert "tensorflow run_clip"

This reverts commit 261f86ac1f1c9e05dd3fd0291e1a1f8e573781d5.

* fix: duplicated argument

* fix

* fix

* fix

* fix

* fix

* fix

* fix

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* pytorch examples

* tensorflow examples

* flax examples

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Fix .push_to_hub and cleanup get_full_repo_name usage

* Do not rely on Python bool conversion magic

* request changes

Change logic

Fix step bugs in no trainer + load checkpoint + grad acc

Act on deprecation

* Add run_mim_no_trainer.py draft from #20412

Add parse_args method and copy over other dependencies

Add Method call for sending telemetry

Initialize Accelerator

Make one log on every process

Set seed and Handle repository creation

Initialize dataset and Set validation split

Create Config

Adapt Config

Update Config

Create Feature Extractor

Create model

Set column names

Create transforms

Create mask generator

Create method to preprocess images

Shuffle datasets if needed and set transforms

Create Dataloaders

Add optimizer

Add learning rate scheduler

Prepare everything with our accelerator

Tie weights for TPU training

Recalculate training steps and training epochs

Set accelerator checkpointing steps

Initialize trackers and store configuration

Set total batch size

Fix typo: mlm -> mim

Log info at the start of training

Load in the weights and states from previous save

update the progress_bar if load from checkpoint

Define train loop

Add evaluation loop to training

Add to parse_args method

Push repo to hub

Save accelerator state

End training and save model and feature extractor

Remove unused imports

Fix trailing whitespace

* Update code based on comments, Rename feature_extractor to image_processor

* Fix linting

* Add argument for learning rate

* Add argument for setting number of training epochs

* Remove incorrect logger argument

* Convert max_train_steps to int for tqdm

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Co-authored-by: Saad Mahmud <shuvro.mahmud79@gmail.com>

* Respect documentation on passive log level

* Fix test and set log level in examples

* Add doc

Update run_mae.py

* Update examples to use image processors

* Small fixes

* Resolve conflicts

* 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>