code/studiogan/src/utils/resize.py

"""
MIT License

Copyright (c) 2021 Gaurav Parmar

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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SOFTWARE.
"""

### On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation
### (https://www.cs.cmu.edu/~clean-fid/)
### Gaurav Parmar, Richard Zhang, Jun-Yan Zhu
### https://github.com/GaParmar/clean-fid/blob/main/cleanfid/resize.py


import os

from PIL import Image
import torch
import torch.nn.functional as F
import numpy as np


dict_name_to_filter = {
    "PIL": {
        "bicubic": Image.BICUBIC,
        "bilinear": Image.BILINEAR,
        "nearest": Image.NEAREST,
        "lanczos": Image.LANCZOS,
        "box": Image.BOX
    }
}


def build_resizer(resizer, backbone, size):
    if resizer == "friendly":
        if backbone == "InceptionV3_tf":
            return make_resizer("PIL", "bilinear", (size, size))
        elif backbone == "InceptionV3_torch":
            return make_resizer("PIL", "lanczos", (size, size))
        elif backbone == "ResNet50_torch":
            return make_resizer("PIL", "bilinear", (size, size))
        elif backbone == "SwAV_torch":
            return make_resizer("PIL", "bilinear", (size, size))
        elif backbone == "DINO_torch":
            return make_resizer("PIL", "bilinear", (size, size))
        elif backbone == "Swin-T_torch":
            return make_resizer("PIL", "bicubic", (size, size))
        else:
            raise ValueError(f"Invalid resizer {resizer} specified")
    elif resizer == "clean":
        return make_resizer("PIL", "bicubic", (size, size))
    elif resizer == "legacy":
        return make_resizer("PyTorch", "bilinear", (size, size))


def make_resizer(library, filter, output_size):
    if library == "PIL":
        s1, s2 = output_size
        def resize_single_channel(x_np):
            img = Image.fromarray(x_np.astype(np.float32), mode='F')
            img = img.resize(output_size, resample=dict_name_to_filter[library][filter])
            return np.asarray(img).reshape(s1, s2, 1)
        def func(x):
            x = [resize_single_channel(x[:, :, idx]) for idx in range(3)]
            x = np.concatenate(x, axis=2).astype(np.float32)
            return x
    elif library == "PyTorch":
        import warnings
        # ignore the numpy warnings
        warnings.filterwarnings("ignore")
        def func(x):
            x = torch.Tensor(x.transpose((2, 0, 1)))[None, ...]
            x = F.interpolate(x, size=output_size, mode=filter, align_corners=False)
            x = x[0, ...].cpu().data.numpy().transpose((1, 2, 0)).clip(0, 255)
            return x
    else:
        raise NotImplementedError('library [%s] is not include' % library)
    return func