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Update examples with image processors (#21155)

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* Update examples to use image processors

* Small fixes

* Resolve conflicts
This commit is contained in:
amyeroberts
2023-01-19 15:14:58 +00:00
committed by GitHub
parent fc8a93507c
commit 4bc18e7a83
12 changed files with 124 additions and 137 deletions
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18
examples/pytorch/image-classification/run_image_classification.py
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@@ -38,7 +38,7 @@ import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
@@ -141,7 +141,7 @@ class ModelArguments:
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
feature_extractor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
use_auth_token: bool = field(
default=False,
metadata={
@@ -283,19 +283,19 @@ def main():
use_auth_token=True if model_args.use_auth_token else None,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)
feature_extractor = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path,
image_processor = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in feature_extractor.size:
size = feature_extractor.size["shortest_edge"]
if "shortest_edge" in image_processor.size:
size = image_processor.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)
size = (image_processor.size["height"], image_processor.size["width"])
normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
_train_transforms = Compose(
[
RandomResizedCrop(size),
@@ -352,7 +352,7 @@ def main():
train_dataset=dataset["train"] if training_args.do_train else None,
eval_dataset=dataset["validation"] if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=feature_extractor,
tokenizer=image_processor,
data_collator=collate_fn,
)