Donnerstag 17 Dezember 2020 um 12:43
Multi-Scale Training with PyTorch Image Folder
Von Eric Antoine ScuccimarraI've had good luck with multi-scale training for image detection so I wanted to try it for classification of images that were of different sizes with objects at differing scales. I found some base code here , but this is based on PyTorch datasets, not on ImageFolders. I wrote some code to extend it to ImageFolders, which is in the below gist :
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| """Based on https://github.com/CaoWGG/multi-scale-training""" | |
| from torch.utils.data import Sampler,RandomSampler,SequentialSampler | |
| import numpy as np | |
| class BatchSampler(object): | |
| def __init__(self, sampler, batch_size, drop_last,multiscale_step=None,img_sizes = None): | |
| if not isinstance(sampler, Sampler): | |
| raise ValueError("sampler should be an instance of " | |
| "torch.utils.data.Sampler, but got sampler={}" | |
| .format(sampler)) | |
| if not isinstance(drop_last, bool): | |
| raise ValueError("drop_last should be a boolean value, but got " | |
| "drop_last={}".format(drop_last)) | |
| self.sampler = sampler | |
| self.batch_size = batch_size | |
| self.drop_last = drop_last | |
| if multiscale_step is not None and multiscale_step < 1 : | |
| raise ValueError("multiscale_step should be > 0, but got " | |
| "multiscale_step={}".format(multiscale_step)) | |
| if multiscale_step is not None and img_sizes is None: | |
| raise ValueError("img_sizes must a list, but got img_sizes={} ".format(img_sizes)) | |
| self.multiscale_step = multiscale_step | |
| self.img_sizes = img_sizes | |
| def __iter__(self): | |
| num_batch = 0 | |
| batch = [] | |
| size = 416 | |
| for idx in self.sampler: | |
| batch.append([idx,size]) | |
| if len(batch) == self.batch_size: | |
| yield batch | |
| num_batch+=1 | |
| batch = [] | |
| if self.multiscale_step and num_batch % self.multiscale_step == 0 : | |
| size = np.random.choice(self.img_sizes) | |
| if len(batch) > 0 and not self.drop_last: | |
| yield batch | |
| def __len__(self): | |
| if self.drop_last: | |
| return len(self.sampler) // self.batch_size | |
| else: | |
| return (len(self.sampler) + self.batch_size - 1) // self.batch_size | |
| class MultiscaleDataSet(torchvision.datasets.ImageFolder): | |
| """Multiscale ImageFolder dataset""" | |
| def __getitem__(self, index): | |
| if isinstance(index, (tuple, list)): | |
| index, input_size = index | |
| else: | |
| # set the default image size here | |
| input_size = 448 | |
| path, target = self.samples[index] | |
| sample = self.loader(path) | |
| # resize the image | |
| sample = sample.resize((input_size, input_size)) | |
| # return the image and label | |
| return sample, target | |
| transforms = | |
| # create the dataset and loader | |
| train_dataset = MultiscaleDataSet( | |
| root="data/train", | |
| transform=transform | |
| ) | |
| train_loader = torch.utils.data.DataLoader( | |
| train_dataset, | |
| batch_sampler=BatchSampler(RandomSampler(train_dataset), | |
| batch_size=batch_size, | |
| multiscale_step=1, | |
| drop_last=True, | |
| img_sizes=[320, 384, 448, 512, 576, 640]), | |
| num_workers=7, | |
| ) |
Etiketten: machine_learning, pytorch