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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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22
examples/pytorch/semantic-segmentation/run_semantic_segmentation.py
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@@ -34,7 +34,7 @@ import transformers
from huggingface_hub import hf_hub_download
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoImageProcessor,
AutoModelForSemanticSegmentation,
HfArgumentParser,
Trainer,
@@ -240,7 +240,7 @@ 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={
@@ -358,7 +358,7 @@ def main():
references=labels,
num_labels=len(id2label),
ignore_index=0,
reduce_labels=feature_extractor.do_reduce_labels,
reduce_labels=image_processor.do_reduce_labels,
)
# add per category metrics as individual key-value pairs
per_category_accuracy = metrics.pop("per_category_accuracy").tolist()
@@ -385,8 +385,8 @@ def main():
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
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,
@@ -395,11 +395,11 @@ def main():
# Define torchvision transforms to be applied to each image + target.
# Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
# Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
if "shortest_edge" in feature_extractor.size:
if "shortest_edge" in image_processor.size:
# We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
size = (feature_extractor.size["shortest_edge"], feature_extractor.size["shortest_edge"])
size = (image_processor.size["shortest_edge"], image_processor.size["shortest_edge"])
else:
size = (feature_extractor.size["height"], feature_extractor.size["width"])
size = (image_processor.size["height"], image_processor.size["width"])
train_transforms = Compose(
[
ReduceLabels() if data_args.reduce_labels else Identity(),
@@ -407,7 +407,7 @@ def main():
RandomHorizontalFlip(flip_prob=0.5),
PILToTensor(),
ConvertImageDtype(torch.float),
Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
Normalize(mean=image_processor.image_mean, std=image_processor.image_std),
]
)
# Define torchvision transform to be applied to each image.
@@ -418,7 +418,7 @@ def main():
Resize(size=size),
PILToTensor(),
ConvertImageDtype(torch.float),
Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
Normalize(mean=image_processor.image_mean, std=image_processor.image_std),
]
)
@@ -477,7 +477,7 @@ 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=default_data_collator,
)