Multi-Scale Training with PyTorch Image Folder
jeudi 17 décembre 2020I'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 :
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| """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, | |
| ) |
Libellés:
machine_learning,
pytorch
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