| """ |
| Copyright (c) 2022 Samsung Electronics Co., Ltd. |
| |
| Author: |
| Abhijith Punnappurath (abhijith.p@samsung.com) |
| |
| Licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License, (the "License"); |
| you may not use this file except in compliance with the License. |
| You may obtain a copy of the License at https://creativecommons.org/licenses/by-nc/4.0 |
| Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an |
| "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and limitations under the License. |
| For conditions of distribution and use, see the accompanying LICENSE.md file. |
| |
| """ |
|
|
| |
|
|
| import os |
| import torch |
| import torchvision |
| from torchvision import transforms |
| import utils.data_generator as dg |
|
|
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
|
|
|
|
| class DatasetRAW(object): |
| def __init__(self, root, batch_size, patch_size, stride, wb_illum, on_cuda, which_input): |
|
|
| print('Inside raw data generator') |
| rawimgs = dg.datagenerator_raw(data_dir=os.path.join(root, which_input), |
| meta_dir=os.path.join(root, 'metadata_raw'), |
| wb_illum=wb_illum, |
| batch_size=batch_size, patch_size=patch_size, stride=stride) |
| rawimgs = torch.from_numpy(rawimgs) |
| rawimgs = rawimgs.permute(0, 3, 1, 2) |
|
|
| rawimgs = rawimgs ** (1 / 2.2) |
| if on_cuda: |
| rawimgs = rawimgs.to(device) |
|
|
| print('Number of patches '+str(rawimgs.shape[0])) |
| print('Outside raw data generator \n') |
| self.rawimgs = rawimgs |
|
|
| print('Inside sRGB data generator') |
| srgbimgs = dg.datagenerator_sRGB(data_dir=os.path.join(root, 'clean'), batch_size=batch_size, patch_size=patch_size, |
| stride=stride) |
| print('Number of patches '+str(srgbimgs.shape[0])) |
| print('Outside sRGB data generator \n') |
| srgbimgs = torch.from_numpy(srgbimgs) |
| srgbimgs = srgbimgs.permute(0, 3, 1, 2) |
| srgbimgs = srgbimgs / 255.0 |
| if on_cuda: |
| srgbimgs = srgbimgs.to(device) |
| self.srgbimgs = srgbimgs |
|
|
| def __getitem__(self, idx): |
| |
|
|
| img = self.rawimgs[idx] |
| target = self.srgbimgs[idx] |
|
|
| return img, target |
|
|
| def __len__(self): |
| return len(self.rawimgs) |
|
|
|
|
| if __name__ == '__main__': |
| data_dir = 'synth_night' |
| batch_size, patch_size, stride = 4, 480, 480 |
| wb_illum = 'avg' |
| on_cuda = False |
| which_input = 'noisy_raw' |
|
|
| image_datasets = { |
| x: DatasetRAW(os.path.join(data_dir, x), batch_size, patch_size, stride, wb_illum, |
| on_cuda, which_input) |
| for x in ['val']} |
|
|
| dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, |
| shuffle=True, num_workers=0) |
| for x in ['val']} |
|
|
| dataset_sizes = {x: len(image_datasets[x]) for x in ['val']} |
|
|
| |
| inputs, targets = next(iter(dataloaders['val'])) |
|
|
| |
| targets_grid = torchvision.utils.make_grid(targets) |
| inputs_grid = torchvision.utils.make_grid(inputs) |
|
|
| targets_grid = transforms.ToPILImage()(targets_grid.cpu()) |
| inputs_grid = transforms.ToPILImage()(inputs_grid.cpu()) |
|
|
| targets_grid.save("debug_targets_grid.png") |
| inputs_grid.save("debug_inputs_grid.png") |
|
|
| print('Done!') |
|
|
