116 lines
4.0 KiB
Python
116 lines
4.0 KiB
Python
from pathlib import Path
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import numpy as np
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import pytest
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import torch
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from PIL import Image
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from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure
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def reset_artifact_dir(path: Path) -> Path:
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import shutil
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if path.exists():
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shutil.rmtree(path)
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path.mkdir(parents=True, exist_ok=True)
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return path
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def infer_model_label(model: str) -> str:
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label = Path(model.rstrip("/\\")).name or "model"
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return "".join(char if char.isalnum() or char in {"-", "_"} else "_" for char in label)
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def model_output_dir(parent_dir: Path, model: str) -> Path:
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safe_model_name = model.split("/")[-1].replace(".", "_")
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path = parent_dir / safe_model_name
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path.mkdir(parents=True, exist_ok=True)
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return path
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def assert_similarity(
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*,
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model_name: str,
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vllm_image: Image.Image,
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diffusers_image: Image.Image,
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ssim_threshold: float,
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psnr_threshold: float,
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width: int | None = None,
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height: int | None = None,
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compare_mode: str = "RGB",
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) -> None:
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requested_size = (width, height) if width is not None and height is not None else None
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if requested_size is not None and diffusers_image.size != requested_size:
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pytest.skip(
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"Skipping as diffusers baseline output is corrupt and not comparable: "
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f"dimensions do not match requested size; requested={requested_size}, got={diffusers_image.size}."
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)
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assert vllm_image.size == diffusers_image.size, (
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f"Online and diffusers output sizes mismatch: online={vllm_image.size}, diffusers={diffusers_image.size}"
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)
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ssim_score, psnr_score = compute_image_ssim_psnr(
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prediction=vllm_image,
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reference=diffusers_image,
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compare_mode=compare_mode,
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)
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print(f"{model_name} similarity metrics:")
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print(f" SSIM: value={ssim_score:.6f}, threshold>={ssim_threshold:.6f}, range=[-1, 1], higher_is_better=True")
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print(
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f" PSNR: value={psnr_score:.6f} dB, threshold>={psnr_threshold:.6f} dB, range=[0, +inf), higher_is_better=True"
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)
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assert ssim_score >= ssim_threshold, (
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f"SSIM below threshold for {model_name}: got {ssim_score:.6f}, expected >= {ssim_threshold:.6f}."
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)
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assert psnr_score >= psnr_threshold, (
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f"PSNR below threshold for {model_name}: got {psnr_score:.6f}, expected >= {psnr_threshold:.6f}."
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)
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def assert_image_sequence_similarity(
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*,
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model_name: str,
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vllm_images: list[Image.Image],
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diffusers_images: list[Image.Image],
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ssim_threshold: float,
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psnr_threshold: float,
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compare_mode: str = "RGB",
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) -> None:
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assert len(vllm_images) == len(diffusers_images), (
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f"Output image count mismatch for {model_name}: online={len(vllm_images)}, diffusers={len(diffusers_images)}"
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)
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for index, (vllm_image, diffusers_image) in enumerate(zip(vllm_images, diffusers_images, strict=True), start=1):
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assert_similarity(
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model_name=f"{model_name}[layer={index}]",
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vllm_image=vllm_image,
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diffusers_image=diffusers_image,
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ssim_threshold=ssim_threshold,
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psnr_threshold=psnr_threshold,
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compare_mode=compare_mode,
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)
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def compute_image_ssim_psnr(
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*,
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prediction: Image.Image,
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reference: Image.Image,
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compare_mode: str = "RGB",
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) -> tuple[float, float]:
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pred_tensor = _pil_to_batched_tensor(prediction, compare_mode=compare_mode)
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ref_tensor = _pil_to_batched_tensor(reference, compare_mode=compare_mode)
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ssim_metric = StructuralSimilarityIndexMeasure(data_range=1.0)
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psnr_metric = PeakSignalNoiseRatio(data_range=1.0)
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ssim_value = float(ssim_metric(pred_tensor, ref_tensor).item())
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psnr_value = float(psnr_metric(pred_tensor, ref_tensor).item())
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return ssim_value, psnr_value
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def _pil_to_batched_tensor(image: Image.Image, *, compare_mode: str) -> torch.Tensor:
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array = np.asarray(image.convert(compare_mode), dtype=np.float32) / 255.0
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tensor = torch.from_numpy(array).permute(2, 0, 1).unsqueeze(0)
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return tensor
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