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layout_overlap.py
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from typing import Dict, List, Tuple, TypedDict, Union
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import datasets as ds
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import evaluate
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import numpy as np
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import numpy.typing as npt
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_DESCRIPTION = """\
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Some overlap metrics that are different to each other in previous works.
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"""
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_CITATION = """\
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@inproceedings{li2018layoutgan,
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title={LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators},
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author={Li, Jianan and Yang, Jimei and Hertzmann, Aaron and Zhang, Jianming and Xu, Tingfa},
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booktitle={International Conference on Learning Representations},
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year={2019}
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}
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@article{li2020attribute,
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title={Attribute-conditioned layout gan for automatic graphic design},
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author={Li, Jianan and Yang, Jimei and Zhang, Jianming and Liu, Chang and Wang, Christina and Xu, Tingfa},
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journal={IEEE Transactions on Visualization and Computer Graphics},
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volume={27},
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number={10},
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pages={4039--4048},
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year={2020},
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publisher={IEEE}
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}
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@inproceedings{kikuchi2021constrained,
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title={Constrained graphic layout generation via latent optimization},
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author={Kikuchi, Kotaro and Simo-Serra, Edgar and Otani, Mayu and Yamaguchi, Kota},
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booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
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pages={88--96},
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year={2021}
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}
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"""
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def convert_xywh_to_ltrb(
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batch_bbox: npt.NDArray[np.float64],
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) -> Tuple[
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npt.NDArray[np.float64],
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npt.NDArray[np.float64],
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npt.NDArray[np.float64],
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npt.NDArray[np.float64],
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]:
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xc, yc, w, h = batch_bbox
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x1 = xc - w / 2
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y1 = yc - h / 2
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x2 = xc + w / 2
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y2 = yc + h / 2
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return (x1, y1, x2, y2)
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class A(TypedDict):
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a1: npt.NDArray[np.float64]
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ai: npt.NDArray[np.float64]
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class LayoutOverlap(evaluate.Metric):
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def _info(self) -> evaluate.EvaluationModuleInfo:
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return evaluate.MetricInfo(
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description=_DESCRIPTION,
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citation=_CITATION,
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features=ds.Features(
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{
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"bbox": ds.Sequence(ds.Sequence(ds.Value("float64"))),
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"mask": ds.Sequence(ds.Value("bool")),
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}
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),
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codebase_urls=[
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"https://github.com/ktrk115/const_layout/blob/master/metric.py#L138-L164",
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"https://github.com/CyberAgentAILab/layout-dm/blob/main/src/trainer/trainer/helpers/metric.py#L150-L203",
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],
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)
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def __calculate_a1_ai(self, batch_bbox: npt.NDArray[np.float64]) -> A:
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l1, t1, r1, b1 = convert_xywh_to_ltrb(batch_bbox[:, :, :, None])
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l2, t2, r2, b2 = convert_xywh_to_ltrb(batch_bbox[:, :, None, :])
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a1 = (r1 - l1) * (b1 - t1)
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# shape: (B, S, S)
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l_max = np.maximum(l1, l2)
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r_min = np.minimum(r1, r2)
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t_max = np.maximum(t1, t2)
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b_min = np.minimum(b1, b2)
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cond = (l_max < r_min) & (t_max < b_min)
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ai = np.where(cond, (r_min - l_max) * (b_min - t_max), 0.0)
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return {"a1": a1, "ai": ai}
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def _compute_ac_layout_gan(
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self,
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S: int,
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ai: npt.NDArray[np.float64],
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a1: npt.NDArray[np.float64],
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batch_mask: npt.NDArray[np.bool_],
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) -> npt.NDArray[np.float64]:
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# shape: (B, S) -> (B, S, S)
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batch_mask = ~batch_mask[:, None, :] | ~batch_mask[:, :, None]
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indices = np.arange(S)
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batch_mask[:, indices, indices] = True
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ai[batch_mask] = 0.0
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# shape: (B, S, S)
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ar = np.nan_to_num(ai / a1)
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score = ar.sum(axis=(1, 2))
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return score
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def _compute_layout_gan_pp(
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self,
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score_ac_layout_gan: npt.NDArray[np.float64],
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batch_mask: npt.NDArray[np.bool_],
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) -> npt.NDArray[np.float64]:
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# shape: (B, S) -> (B,)
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batch_mask = batch_mask.sum(axis=1)
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# shape: (B,)
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score_normalized = score_ac_layout_gan / batch_mask
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score_normalized[np.isnan(score_normalized)] = 0.0
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return score_normalized
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def _compute_layout_gan(
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self, S: int, B: int, ai: npt.NDArray[np.float64]
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) -> npt.NDArray[np.float64]:
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indices = np.arange(S)
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ii, jj = np.meshgrid(indices, indices, indexing="ij")
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# shape: ii (S, S) -> (1, S, S), jj (S, S) -> (1, S, S)
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# shape: (1, S, S) -> (B, S, S)
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ai[np.repeat((ii[None, :] >= jj[None, :]), axis=0, repeats=B)] = 0.0
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# shape: (B, S, S) -> (B,)
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score = ai.sum(axis=(1, 2))
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return score
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def _compute(
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self,
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*,
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bbox: Union[npt.NDArray[np.float64], List[List[int]]],
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mask: Union[npt.NDArray[np.bool_], List[List[bool]]],
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) -> Dict[str, npt.NDArray[np.float64]]:
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# shape: (B, model_max_length, C)
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bbox = np.array(bbox)
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# shape: (B, model_max_length)
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mask = np.array(mask)
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assert bbox.ndim == 3
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assert mask.ndim == 2
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# S: model_max_length
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B, S, C = bbox.shape
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# shape: batch_bbox (B, S, C), batch_mask (B, S) -> (B, S, 1) -> (B, S, C)
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bbox[np.repeat(~mask[:, :, None], axis=2, repeats=C)] = 0.0
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# shape: (C, B, S)
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bbox = bbox.transpose(2, 0, 1)
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A = self.__calculate_a1_ai(bbox)
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# shape: (B,)
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score_ac_layout_gan = self._compute_ac_layout_gan(S=S, batch_mask=mask, **A)
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# shape: (B,)
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score_layout_gan_pp = self._compute_layout_gan_pp(
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score_ac_layout_gan=score_ac_layout_gan, batch_mask=mask
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)
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# shape: (B,)
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score_layout_gan = self._compute_layout_gan(B=B, S=S, ai=A["ai"])
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return {
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"overlap-ACLayoutGAN": score_ac_layout_gan,
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"overlap-LayoutGAN++": score_layout_gan_pp,
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"overlap-LayoutGAN": score_layout_gan,
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}
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