in mlebench/competitions/herbarium-2021-fgvc8/prepare.py [0:0]
def prepare(raw: Path, public: Path, private: Path):
"""
Splits the raw data into public and private datasets with appropriate test/train splits.
`train/metadata.json` is the "table of contents" for our data, with the following structure:
(More details at https://www.kaggle.com/competitions/herbarium-2021-fgvc8/data)
```
{
"annotations" : [annotation],
"categories" : [category],
"images" : [image],
"info" : info,
"licenses" : [license],
"institutions" : [region]
}
```
- `images` and `annotations` are both N-length lists corresponding to the N samples.
We'll need to split each of these lists into train and test.
- The other fields are dataset-wide metadata that we don't need to touch.
- test/metadata.json is the same structure as train/metadata.json, but without "annotations", "categories", "institutions"
Other notes:
- train/test splits need to occur per category (each category should be in both train and test).
- The `test/images` and `train/images` folders have nested subdirs to make it easier to browse
- `train/images` is structured as `{category_id[:3]}/{category_id[3:]}/{image_id}.jpg`
- `test/images` is structured as `{image_idx[:3]}/{image_idx}.jpg` (to not reveal the category)
- When we create the new splits, we re-assign image indices so that we don't give away labels based on the index
- train images are indexed within their own category
- test images follow a flat index after shuffling the categories
"""
dev_mode = False
dev_count = 2 # Copy over n images per category when in dev mode
# Create train, test from train split
json_path = raw / "train/metadata.json"
with open(json_path, "r", encoding="utf-8") as f:
old_train_metadata = json.load(f)
# Organize data by category so that we can split per-category later
annotations_images_by_category = {} # We'll collect both `annotations` and `images` here
for annotation, image in list(
zip(old_train_metadata["annotations"], old_train_metadata["images"])
):
assert (
annotation["image_id"] == image["id"]
), f"Mismatching image_id in annotation and image: {annotation['image_id']} vs {image['id']}"
category_id = annotation["category_id"]
if category_id not in annotations_images_by_category:
annotations_images_by_category[category_id] = []
annotations_images_by_category[category_id].append(
{
"annotation": annotation,
"image": image,
}
)
# Split train/test
train_sample_count = 0 # Useful for tqdm later
train_annotations_images_by_category = {}
test_annotations_images_by_category = {}
for category_id, annotations_images in tqdm(
annotations_images_by_category.items(), desc="Assigning train/test splits"
):
test_size = 0.2
# Create split by "category" (class): Each category needs to be in both train and test (80:20) as per original ratio
n_samples = len(annotations_images)
if n_samples == 1:
# If only one sample, put it in train
train_annotations_images = annotations_images
test_annotations_images = []
elif n_samples < 5: # Minimum 5 samples to ensure at least 1 in test
# Ensure at least 1 sample in test
test_size = max(1, int(n_samples * test_size))
train_annotations_images = annotations_images[:-test_size]
test_annotations_images = annotations_images[-test_size:]
else:
# Original split logic
train_annotations_images, test_annotations_images = train_test_split(
annotations_images, test_size=test_size, random_state=0
)
train_annotations_images_by_category[category_id] = train_annotations_images
test_annotations_images_by_category[category_id] = test_annotations_images
train_sample_count += len(train_annotations_images)
# Add to train set
new_train_metadata = (
old_train_metadata.copy()
) # Keep 'categories', 'info', 'licenses', 'institutions'
new_train_metadata.update(
{
"annotations": [],
"images": [],
}
)
with tqdm(
desc="Creating new train dataset",
total=train_sample_count,
) as pbar:
for category_id, annotations_images in train_annotations_images_by_category.items():
# Create a nested directory from category_id, e.g. 15504 -> "155/04" or 3 -> "000/03"
category_subdir = f"{category_id // 100:03d}/{category_id % 100:02d}"
(public / "train/images" / category_subdir).mkdir(exist_ok=True, parents=True)
for idx, annotation_image in enumerate(annotations_images):
new_annotation = annotation_image["annotation"].copy()
new_train_metadata["annotations"].append(new_annotation)
new_image = annotation_image["image"].copy()
new_train_metadata["images"].append(new_image)
# Copy file from raw to public
if (
not dev_mode or idx < dev_count
): # if dev_mode, only copy the first dev_count images
src_path = raw / "train" / annotation_image["image"]["file_name"]
dst_path = public / "train" / annotation_image["image"]["file_name"]
shutil.copyfile(src=src_path, dst=dst_path)
pbar.update(1)
with open(public / "train/metadata.json", "w") as f:
json.dump(new_train_metadata, f, indent=4, sort_keys=True)
if not dev_mode:
assert len(list((public / "train/images").glob("**/*.jpg"))) == len(
new_train_metadata["images"]
), f"Mismatching number of images in train_images, got {len(list((public / 'train/images').glob('**/*.jpg')))}"
assert len(new_train_metadata["annotations"]) == len(
new_train_metadata["images"]
), f"Mismatching number of annotations in train_metadata, got {len(new_train_metadata['annotations'])}"
# Add to test set
new_test_metadata = old_train_metadata.copy()
del new_test_metadata["categories"]
del new_test_metadata["institutions"]
new_test_metadata.update(
{
"annotations": [],
"images": [],
}
)
# Flatten and shuffle test set so that we don't have all the same categories in a row
test_annotations_images = [
item for sublist in test_annotations_images_by_category.values() for item in sublist
]
random.Random(0).shuffle(test_annotations_images)
for idx, annotation_image in tqdm(
enumerate(test_annotations_images),
desc="Creating new test dataset",
total=len(test_annotations_images),
):
# Make new image id, for test set this is just the index
new_image_id = str(idx)
# Make new filename from image id e.g. "000/0.jpg"
new_file_name = f"images/{idx // 1000:03d}/{idx}.jpg"
new_annotation = annotation_image["annotation"].copy()
new_annotation["image_id"] = new_image_id
new_test_metadata["annotations"].append(new_annotation)
new_image = annotation_image["image"].copy()
new_image["id"] = new_image_id
new_image["file_name"] = new_file_name
new_test_metadata["images"].append(new_image)
# Copy file from raw to public
if not dev_mode or idx < dev_count: # if dev_mode, only copy the first dev_count images
src_path = raw / "train" / annotation_image["image"]["file_name"]
dst_path = public / "test" / new_file_name
dst_path.parent.mkdir(exist_ok=True, parents=True)
shutil.copyfile(src=src_path, dst=dst_path)
# Save new test metadata
with open(public / "test/metadata.json", "w") as f:
# The public test data, of course, doesn't have annotations
public_new_test = new_test_metadata.copy()
del public_new_test["annotations"]
assert public_new_test.keys() == {
"images",
"info",
"licenses",
}, f"Public test metadata keys should be 'images', 'info', 'licenses', but found {public_new_test.keys()}"
json.dump(public_new_test, f, indent=4, sort_keys=True)
if not dev_mode:
assert len(list((public / "test/images").glob("**/*.jpg"))) == len(
new_test_metadata["images"]
), f"Mismatching number of images in test_images, got {len(list((public / 'test/images').glob('**/*.jpg')))}"
assert len(new_test_metadata["annotations"]) == len(
new_test_metadata["images"]
), f"Mismatching number of annotations in test_metadata, got {len(new_test_metadata['annotations'])}"
assert len(new_train_metadata["annotations"]) + len(
new_test_metadata["annotations"]
) == len(old_train_metadata["annotations"]), (
f"Expected {len(old_train_metadata['annotations'])} annotations in total, but found"
f"{len(new_train_metadata['annotations'])} in train and {len(new_test_metadata['annotations'])} in test"
)
# Save private test answers
answers_rows = []
for image, annotation in zip(new_test_metadata["images"], new_test_metadata["annotations"]):
assert (
image["id"] == annotation["image_id"]
), f"Mismatching image_id in image and annotation: {image['id']} vs {annotation['image_id']}"
answers_rows.append(
{
"Id": image["id"],
"Predicted": annotation["category_id"],
}
)
answers_df = pd.DataFrame(answers_rows)
answers_df.to_csv(private / "answers.csv", index=False)
# Create new sample submission that matches raw/sample_submission.csv, but for the new test set
sample_rows = []
for image in new_test_metadata["images"]:
sample_rows.append(
{
"Id": image["id"],
"Predicted": 0,
}
)
sample_df = pd.DataFrame(sample_rows)
sample_df.to_csv(public / "sample_submission.csv", index=False)
assert len(answers_df) == len(
new_test_metadata["images"]
), f"Expected {len(new_test_metadata['images'])} rows in answers, but found {len(answers_df)}"
assert len(sample_df) == len(
answers_df
), f"Expected {len(answers_df)} rows in sample submission, but found {len(sample_df)}"
assert answers_df["Id"].equals(
sample_df["Id"]
), "Mismatched 'Id' columns between answers and sample submission"