CIFAR stuff
- Extract coarse labels for the CIFAR dataset - Add simple resnet that branches lower layers based on coarse labels - Some other cleanup
This commit is contained in:
James Betker
parent
80d4404367
commit
fb405d9ef1
176
codes/data/cifar.py
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176
codes/data/cifar.py
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# A copy of the cifar dataset from torch which also returns coarse labels.
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from PIL import Image
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import os
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import os.path
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import numpy as np
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import pickle
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from typing import Any, Callable, Optional, Tuple
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from torchvision.datasets import VisionDataset
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from torchvision.datasets.utils import check_integrity, download_and_extract_archive
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class CIFAR10(VisionDataset):
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"""`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
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Args:
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root (string): Root directory of dataset where directory
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``cifar-10-batches-py`` exists or will be saved to if download is set to True.
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train (bool, optional): If True, creates dataset from training set, otherwise
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creates from test set.
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transform (callable, optional): A function/transform that takes in an PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.
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download (bool, optional): If true, downloads the dataset from the internet and
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puts it in root directory. If dataset is already downloaded, it is not
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downloaded again.
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"""
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base_folder = 'cifar-10-batches-py'
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url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
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filename = "cifar-10-python.tar.gz"
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tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
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train_list = [
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['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
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['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
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['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
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['data_batch_4', '634d18415352ddfa80567beed471001a'],
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['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
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]
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test_list = [
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['test_batch', '40351d587109b95175f43aff81a1287e'],
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]
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meta = {
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'filename': 'batches.meta',
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'key': 'label_names',
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'md5': '5ff9c542aee3614f3951f8cda6e48888',
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}
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def __init__(
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self,
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root: str,
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train: bool = True,
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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download: bool = False,
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) -> None:
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super(CIFAR10, self).__init__(root, transform=transform,
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target_transform=target_transform)
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self.train = train # training set or test set
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if download:
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self.download()
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if not self._check_integrity():
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raise RuntimeError('Dataset not found or corrupted.' +
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' You can use download=True to download it')
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if self.train:
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downloaded_list = self.train_list
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else:
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downloaded_list = self.test_list
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self.data: Any = []
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self.targets = []
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self.coarse_targets = []
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# now load the picked numpy arrays
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for file_name, checksum in downloaded_list:
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file_path = os.path.join(self.root, self.base_folder, file_name)
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with open(file_path, 'rb') as f:
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entry = pickle.load(f, encoding='latin1')
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self.data.append(entry['data'])
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if 'labels' in entry:
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self.targets.extend(entry['labels'])
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else:
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self.targets.extend(entry['fine_labels'])
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self.coarse_targets.extend(entry['coarse_labels'])
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self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
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self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC
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self._load_meta()
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def _load_meta(self) -> None:
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path = os.path.join(self.root, self.base_folder, self.meta['filename'])
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if not check_integrity(path, self.meta['md5']):
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raise RuntimeError('Dataset metadata file not found or corrupted.' +
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' You can use download=True to download it')
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with open(path, 'rb') as infile:
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data = pickle.load(infile, encoding='latin1')
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self.classes = data[self.meta['key']]
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self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)}
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def __getitem__(self, index: int) -> Tuple[Any, Any]:
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (image, target) where target is index of the target class.
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"""
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img, target = self.data[index], self.targets[index]
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# doing this so that it is consistent with all other datasets
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# to return a PIL Image
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img = Image.fromarray(img)
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if self.transform is not None:
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img = self.transform(img)
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if self.target_transform is not None:
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target = self.target_transform(target)
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if len(self.coarse_targets) > 0:
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return img, target, self.coarse_targets[index]
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return img, target
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def __len__(self) -> int:
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return len(self.data)
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def _check_integrity(self) -> bool:
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root = self.root
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for fentry in (self.train_list + self.test_list):
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filename, md5 = fentry[0], fentry[1]
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fpath = os.path.join(root, self.base_folder, filename)
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if not check_integrity(fpath, md5):
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return False
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return True
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def download(self) -> None:
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if self._check_integrity():
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print('Files already downloaded and verified')
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return
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download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)
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def extra_repr(self) -> str:
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return "Split: {}".format("Train" if self.train is True else "Test")
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class CIFAR100(CIFAR10):
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"""`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
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This is a subclass of the `CIFAR10` Dataset.
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"""
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base_folder = 'cifar-100-python'
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url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
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filename = "cifar-100-python.tar.gz"
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tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
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train_list = [
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['train', '16019d7e3df5f24257cddd939b257f8d'],
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]
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test_list = [
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['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
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]
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meta = {
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'filename': 'meta',
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'key': 'fine_label_names',
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'md5': '7973b15100ade9c7d40fb424638fde48',
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}
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@ -4,6 +4,7 @@ import torchvision.transforms as T
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from torchvision import datasets
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# Wrapper for basic pytorch datasets which re-wraps them into a format usable by ExtensibleTrainer.
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from data.cifar import CIFAR100, CIFAR10
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from utils.util import opt_get
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@ -12,8 +13,8 @@ class TorchDataset(Dataset):
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DATASET_MAP = {
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"mnist": datasets.MNIST,
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"fmnist": datasets.FashionMNIST,
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"cifar10": datasets.CIFAR10,
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"cifar100": datasets.CIFAR100,
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"cifar10": CIFAR10,
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"cifar100": CIFAR100,
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"imagenet": datasets.ImageNet,
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"imagefolder": datasets.ImageFolder
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}
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@ -39,8 +40,15 @@ class TorchDataset(Dataset):
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self.offset = opt_get(opt, ['offset'], 0)
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def __getitem__(self, item):
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underlying_item, lbl = self.dataset[item+self.offset]
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return {'lq': underlying_item, 'hq': underlying_item, 'labels': lbl,
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item = self.dataset[item+self.offset]
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if len(item) == 2:
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underlying_item, lbl = item
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coarselbl = None
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elif len(item) == 3:
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underlying_item, lbl, coarselbl = item
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else:
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raise NotImplementedError
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return {'lq': underlying_item, 'hq': underlying_item, 'labels': lbl, 'coarse_labels': coarselbl,
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'LQ_path': str(item), 'GT_path': str(item)}
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def __len__(self):
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0
codes/models/classifiers/__init__.py
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0
codes/models/classifiers/__init__.py
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175
codes/models/classifiers/cifar_resnet_branched.py
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codes/models/classifiers/cifar_resnet_branched.py
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"""resnet in pytorch
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[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
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Deep Residual Learning for Image Recognition
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https://arxiv.org/abs/1512.03385v1
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"""
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import torch
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import torch.nn as nn
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from trainer.networks import register_model
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class BasicBlock(nn.Module):
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"""Basic Block for resnet 18 and resnet 34
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"""
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#BasicBlock and BottleNeck block
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#have different output size
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#we use class attribute expansion
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#to distinct
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expansion = 1
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def __init__(self, in_channels, out_channels, stride=1):
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super().__init__()
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#residual function
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self.residual_function = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels * BasicBlock.expansion)
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)
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#shortcut
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self.shortcut = nn.Sequential()
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#the shortcut output dimension is not the same with residual function
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#use 1*1 convolution to match the dimension
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if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(out_channels * BasicBlock.expansion)
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)
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def forward(self, x):
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return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
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class BottleNeck(nn.Module):
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"""Residual block for resnet over 50 layers
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"""
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expansion = 4
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def __init__(self, in_channels, out_channels, stride=1):
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super().__init__()
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self.residual_function = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
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nn.BatchNorm2d(out_channels * BottleNeck.expansion),
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)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
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nn.BatchNorm2d(out_channels * BottleNeck.expansion)
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)
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def forward(self, x):
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return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
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class ResNetTail(nn.Module):
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def __init__(self, block, num_block, num_classes=100):
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super().__init__()
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self.in_channels = 128
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self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
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self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
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self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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def _make_layer(self, block, out_channels, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_channels, out_channels, stride))
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self.in_channels = out_channels * block.expansion
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return nn.Sequential(*layers)
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def forward(self, x):
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output = self.conv4_x(x)
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output = self.conv5_x(output)
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output = self.avg_pool(output)
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output = output.view(output.size(0), -1)
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output = self.fc(output)
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return output
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class ResNet(nn.Module):
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def __init__(self, block, num_block, num_classes=100, num_tails=20):
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super().__init__()
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self.in_channels = 64
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self.conv1 = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True))
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self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
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self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
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self.tails = nn.ModuleList([ResNetTail(block, num_block, num_classes) for _ in range(num_tails)])
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def _make_layer(self, block, out_channels, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_channels, out_channels, stride))
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self.in_channels = out_channels * block.expansion
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return nn.Sequential(*layers)
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def forward(self, x, coarse_label):
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output = self.conv1(x)
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output = self.conv2_x(output)
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output = self.conv3_x(output)
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bs = output.shape[0]
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tailouts = []
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for t in self.tails:
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tailouts.append(t(output))
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tailouts = torch.stack(tailouts, dim=0)
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return (tailouts[coarse_label] * torch.eye(n=bs).view(bs,bs,1)).sum(dim=1)
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@register_model
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def register_cifar_resnet18(opt_net, opt):
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""" return a ResNet 18 object
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"""
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return ResNet(BasicBlock, [2, 2, 2, 2])
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def resnet34():
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""" return a ResNet 34 object
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"""
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return ResNet(BasicBlock, [3, 4, 6, 3])
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def resnet50():
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""" return a ResNet 50 object
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"""
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return ResNet(BottleNeck, [3, 4, 6, 3])
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def resnet101():
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""" return a ResNet 101 object
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"""
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return ResNet(BottleNeck, [3, 4, 23, 3])
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def resnet152():
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""" return a ResNet 152 object
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"""
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return ResNet(BottleNeck, [3, 8, 36, 3])
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if __name__ == '__main__':
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model = ResNet(BasicBlock, [2,2,2,2])
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print(model(torch.randn(2,3,32,32), torch.LongTensor([4,19])).shape)
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@ -4,22 +4,16 @@ import shutil
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision
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from PIL import Image
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from torch.utils.data import DataLoader
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from torchvision.transforms import ToTensor, Resize
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from torchvision.transforms import ToTensor
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from tqdm import tqdm
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import numpy as np
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import utils
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from data.image_folder_dataset import ImageFolderDataset
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from models.resnet_with_checkpointing import resnet50
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from models.spinenet_arch import SpineNet
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from models.classifiers.resnet_with_checkpointing import resnet50
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# Computes the structural euclidean distance between [x,y]. "Structural" here means the [h,w] dimensions are preserved
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# and the distance is computed across the channel dimension.
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from utils import util
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from utils.kmeans import kmeans, kmeans_predict
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from utils.options import dict_to_nonedict
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@ -1,5 +1,4 @@
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import os
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import shutil
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import torch
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import torch.nn as nn
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@ -7,20 +6,14 @@ import torch.nn.functional as F
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import torchvision
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from PIL import Image
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from torch.utils.data import DataLoader
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from torchvision.transforms import ToTensor, Resize, Normalize
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from torchvision.transforms import ToTensor, Normalize
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from tqdm import tqdm
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import numpy as np
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import utils
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from data.image_folder_dataset import ImageFolderDataset
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from models.resnet_with_checkpointing import resnet50
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from models.segformer.segformer import Segformer
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from models.spinenet_arch import SpineNet
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# Computes the structural euclidean distance between [x,y]. "Structural" here means the [h,w] dimensions are preserved
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# and the distance is computed across the channel dimension.
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from utils import util
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from utils.kmeans import kmeans, kmeans_predict
|
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from utils.options import dict_to_nonedict
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
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import os
|
||||
import shutil
|
||||
from random import shuffle
|
||||
|
||||
import matplotlib.cm as cm
|
||||
|
|
@ -7,26 +6,15 @@ import torch
|
|||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchvision
|
||||
from PIL import Image
|
||||
from torch.utils.data import DataLoader
|
||||
from torchvision.models.resnet import Bottleneck
|
||||
from torchvision.transforms import ToTensor, Resize
|
||||
from tqdm import tqdm
|
||||
import numpy as np
|
||||
|
||||
import utils
|
||||
from data.image_folder_dataset import ImageFolderDataset
|
||||
from models.pixel_level_contrastive_learning.resnet_unet import UResNet50
|
||||
from models.pixel_level_contrastive_learning.resnet_unet_2 import UResNet50_2
|
||||
from models.pixel_level_contrastive_learning.resnet_unet_3 import UResNet50_3
|
||||
from models.resnet_with_checkpointing import resnet50
|
||||
from models.spinenet_arch import SpineNet
|
||||
|
||||
|
||||
# Computes the structural euclidean distance between [x,y]. "Structural" here means the [h,w] dimensions are preserved
|
||||
# and the distance is computed across the channel dimension.
|
||||
from scripts.byol.byol_spinenet_playground import find_similar_latents, create_latent_database
|
||||
from utils import util
|
||||
from utils.kmeans import kmeans, kmeans_predict
|
||||
from utils.options import dict_to_nonedict
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user