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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
View File

@@ -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,
)

26
examples/pytorch/image-classification/run_image_classification_no_trainer.py
View File

@@ -41,13 +41,7 @@ from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository, create_repo
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForImageClassification,
SchedulerType,
get_scheduler,
)
from transformers import AutoConfig, AutoImageProcessor, AutoModelForImageClassification, SchedulerType, get_scheduler
from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
from transformers.utils.versions import require_version
@@ -294,7 +288,7 @@ def main():
label2id = {label: str(i) for i, label in enumerate(labels)}
id2label = {str(i): label for i, label in enumerate(labels)}
# Load pretrained model and feature extractor
# Load pretrained model and image processor
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
@@ -305,7 +299,7 @@ def main():
label2id=label2id,
finetuning_task="image-classification",
)
feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_name_or_path)
image_processor = AutoImageProcessor.from_pretrained(args.model_name_or_path)
model = AutoModelForImageClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
@@ -316,11 +310,11 @@ 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"]
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),
@@ -505,7 +499,7 @@ def main():
save_function=accelerator.save,
)
if accelerator.is_main_process:
feature_extractor.save_pretrained(args.output_dir)
image_processor.save_pretrained(args.output_dir)
repo.push_to_hub(
commit_message=f"Training in progress {completed_steps} steps",
blocking=False,
@@ -547,7 +541,7 @@ def main():
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
feature_extractor.save_pretrained(args.output_dir)
image_processor.save_pretrained(args.output_dir)
repo.push_to_hub(
commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
)
@@ -568,7 +562,7 @@ def main():
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
feature_extractor.save_pretrained(args.output_dir)
image_processor.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)