| """ |
| 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 |
| in the Software without restriction, including without limitation the rights |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
| copies of the Software, and to permit persons to whom the Software is |
| furnished to do so, subject to the following conditions: |
| |
| The above copyright notice and this permission notice shall be included in all |
| copies or substantial portions of the Software. |
| |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 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 |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| SOFTWARE. |
| """ |
|
|
| |
| |
| |
| |
|
|
|
|
| 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 |
| |
| 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 |
|
|
