Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
< 1K
License:
File size: 2,667 Bytes
0832232 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | """Inspection outputs for generated datasets."""
from __future__ import annotations
import json
from pathlib import Path
import numpy as np
from PIL import Image, ImageDraw
from .labels import CLASS_NAMES
def inspect_dataset(dataset_dir: Path, samples_per_class: int = 16) -> dict[str, object]:
npz_path = dataset_dir / "ufo_mnist_28x28.npz"
data = np.load(npz_path)
summary = {
"train_images_shape": list(data["train_images"].shape),
"test_images_shape": list(data["test_images"].shape),
"train_dtype": str(data["train_images"].dtype),
"test_dtype": str(data["test_images"].dtype),
"train_counts": _counts(data["train_labels"]),
"test_counts": _counts(data["test_labels"]),
"nearest_centroid_accuracy": _nearest_centroid_accuracy(data),
}
(dataset_dir / "summary.json").write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8")
_contact_sheet(data["train_images"], data["train_labels"], dataset_dir / "contact_sheet_train.png", samples_per_class)
_contact_sheet(data["test_images"], data["test_labels"], dataset_dir / "contact_sheet_test.png", samples_per_class)
return summary
def _counts(labels: np.ndarray) -> dict[str, int]:
return {CLASS_NAMES[index]: int((labels == index).sum()) for index in range(len(CLASS_NAMES))}
def _nearest_centroid_accuracy(data: np.lib.npyio.NpzFile) -> float:
train_x = data["train_images"].reshape(data["train_images"].shape[0], -1).astype(np.float32)
test_x = data["test_images"].reshape(data["test_images"].shape[0], -1).astype(np.float32)
train_y = data["train_labels"]
test_y = data["test_labels"]
centroids = np.stack([train_x[train_y == label].mean(axis=0) for label in range(len(CLASS_NAMES))])
dists = ((test_x[:, None, :] - centroids[None, :, :]) ** 2).mean(axis=2)
pred = dists.argmin(axis=1)
return float((pred == test_y).mean())
def _contact_sheet(images: np.ndarray, labels: np.ndarray, output: Path, samples_per_class: int) -> None:
cell = 34
label_h = 14
cols = samples_per_class
rows = len(CLASS_NAMES)
sheet = Image.new("L", (cols * cell, rows * (cell + label_h)), 20)
draw = ImageDraw.Draw(sheet)
for label, name in enumerate(CLASS_NAMES):
indices = np.where(labels == label)[0][:samples_per_class]
y0 = label * (cell + label_h)
draw.text((2, y0), f"{label} {name}", fill=230)
for col, idx in enumerate(indices):
tile = Image.fromarray(images[idx]).resize((28, 28), Image.Resampling.NEAREST)
sheet.paste(tile, (col * cell + 3, y0 + label_h + 3))
sheet.save(output)
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