CIFAR stuff

- Extract coarse labels for the CIFAR dataset
- Add simple resnet that branches lower layers based on coarse labels
- Some other cleanup

codes/data/cifar.py

@@ -0,0 +1,176 @@
+# A copy of the cifar dataset from torch which also returns coarse labels.
+
+from PIL import Image
+import os
+import os.path
+import numpy as np
+import pickle
+from typing import Any, Callable, Optional, Tuple
+
+from torchvision.datasets import VisionDataset
+from torchvision.datasets.utils import check_integrity, download_and_extract_archive
+
+
+class CIFAR10(VisionDataset):
+    """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
+
+    Args:
+        root (string): Root directory of dataset where directory
+            ``cifar-10-batches-py`` exists or will be saved to if download is set to True.
+        train (bool, optional): If True, creates dataset from training set, otherwise
+            creates from test set.
+        transform (callable, optional): A function/transform that takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+
+    """
+    base_folder = 'cifar-10-batches-py'
+    url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+    filename = "cifar-10-python.tar.gz"
+    tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
+    train_list = [
+        ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
+        ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
+        ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
+        ['data_batch_4', '634d18415352ddfa80567beed471001a'],
+        ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
+    ]
+
+    test_list = [
+        ['test_batch', '40351d587109b95175f43aff81a1287e'],
+    ]
+    meta = {
+        'filename': 'batches.meta',
+        'key': 'label_names',
+        'md5': '5ff9c542aee3614f3951f8cda6e48888',
+    }
+
+    def __init__(
+            self,
+            root: str,
+            train: bool = True,
+            transform: Optional[Callable] = None,
+            target_transform: Optional[Callable] = None,
+            download: bool = False,
+    ) -> None:
+
+        super(CIFAR10, self).__init__(root, transform=transform,
+                                      target_transform=target_transform)
+
+        self.train = train  # training set or test set
+
+        if download:
+            self.download()
+
+        if not self._check_integrity():
+            raise RuntimeError('Dataset not found or corrupted.' +
+                               ' You can use download=True to download it')
+
+        if self.train:
+            downloaded_list = self.train_list
+        else:
+            downloaded_list = self.test_list
+
+        self.data: Any = []
+        self.targets = []
+        self.coarse_targets = []
+
+        # now load the picked numpy arrays
+        for file_name, checksum in downloaded_list:
+            file_path = os.path.join(self.root, self.base_folder, file_name)
+            with open(file_path, 'rb') as f:
+                entry = pickle.load(f, encoding='latin1')
+                self.data.append(entry['data'])
+                if 'labels' in entry:
+                    self.targets.extend(entry['labels'])
+                else:
+                    self.targets.extend(entry['fine_labels'])
+                    self.coarse_targets.extend(entry['coarse_labels'])
+
+        self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
+        self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC
+
+        self._load_meta()
+
+    def _load_meta(self) -> None:
+        path = os.path.join(self.root, self.base_folder, self.meta['filename'])
+        if not check_integrity(path, self.meta['md5']):
+            raise RuntimeError('Dataset metadata file not found or corrupted.' +
+                               ' You can use download=True to download it')
+        with open(path, 'rb') as infile:
+            data = pickle.load(infile, encoding='latin1')
+            self.classes = data[self.meta['key']]
+        self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)}
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.targets[index]
+
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+        img = Image.fromarray(img)
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        if len(self.coarse_targets) > 0:
+            return img, target, self.coarse_targets[index]
+
+        return img, target
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def _check_integrity(self) -> bool:
+        root = self.root
+        for fentry in (self.train_list + self.test_list):
+            filename, md5 = fentry[0], fentry[1]
+            fpath = os.path.join(root, self.base_folder, filename)
+            if not check_integrity(fpath, md5):
+                return False
+        return True
+
+    def download(self) -> None:
+        if self._check_integrity():
+            print('Files already downloaded and verified')
+            return
+        download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)
+
+    def extra_repr(self) -> str:
+        return "Split: {}".format("Train" if self.train is True else "Test")
+
+
+class CIFAR100(CIFAR10):
+    """`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
+
+    This is a subclass of the `CIFAR10` Dataset.
+    """
+    base_folder = 'cifar-100-python'
+    url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
+    filename = "cifar-100-python.tar.gz"
+    tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
+    train_list = [
+        ['train', '16019d7e3df5f24257cddd939b257f8d'],
+    ]
+
+    test_list = [
+        ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
+    ]
+    meta = {
+        'filename': 'meta',
+        'key': 'fine_label_names',
+        'md5': '7973b15100ade9c7d40fb424638fde48',
+    }

codes/data/torch_dataset.py

@@ -4,6 +4,7 @@ import torchvision.transforms as T
 from torchvision import datasets
 
 # Wrapper for basic pytorch datasets which re-wraps them into a format usable by ExtensibleTrainer.
+from data.cifar import CIFAR100, CIFAR10
 from utils.util import opt_get
 
 
@@ -12,8 +13,8 @@ class TorchDataset(Dataset):
         DATASET_MAP = {
             "mnist": datasets.MNIST,
             "fmnist": datasets.FashionMNIST,
-            "cifar10": datasets.CIFAR10,
+            "cifar10": CIFAR10,
-            "cifar100": datasets.CIFAR100,
+            "cifar100": CIFAR100,
             "imagenet": datasets.ImageNet,
             "imagefolder": datasets.ImageFolder
         }
@@ -39,8 +40,15 @@ class TorchDataset(Dataset):
         self.offset = opt_get(opt, ['offset'], 0)
 
     def __getitem__(self, item):
-        underlying_item, lbl = self.dataset[item+self.offset]
+        item = self.dataset[item+self.offset]
-        return {'lq': underlying_item, 'hq': underlying_item, 'labels': lbl,
+        if len(item) == 2:
+            underlying_item, lbl = item
+            coarselbl = None
+        elif len(item) == 3:
+            underlying_item, lbl, coarselbl = item
+        else:
+            raise NotImplementedError
+        return {'lq': underlying_item, 'hq': underlying_item, 'labels': lbl, 'coarse_labels': coarselbl,
                 'LQ_path': str(item), 'GT_path': str(item)}
 
     def __len__(self):

codes/models/classifiers/cifar_resnet_branched.py

@@ -0,0 +1,175 @@
+"""resnet in pytorch
+
+
+
+[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
+
+    Deep Residual Learning for Image Recognition
+    https://arxiv.org/abs/1512.03385v1
+"""
+
+import torch
+import torch.nn as nn
+
+from trainer.networks import register_model
+
+
+class BasicBlock(nn.Module):
+    """Basic Block for resnet 18 and resnet 34
+
+    """
+
+    #BasicBlock and BottleNeck block
+    #have different output size
+    #we use class attribute expansion
+    #to distinct
+    expansion = 1
+
+    def __init__(self, in_channels, out_channels, stride=1):
+        super().__init__()
+
+        #residual function
+        self.residual_function = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(out_channels * BasicBlock.expansion)
+        )
+
+        #shortcut
+        self.shortcut = nn.Sequential()
+
+        #the shortcut output dimension is not the same with residual function
+        #use 1*1 convolution to match the dimension
+        if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
+            )
+
+    def forward(self, x):
+        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
+
+class BottleNeck(nn.Module):
+    """Residual block for resnet over 50 layers
+
+    """
+    expansion = 4
+    def __init__(self, in_channels, out_channels, stride=1):
+        super().__init__()
+        self.residual_function = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
+            nn.BatchNorm2d(out_channels * BottleNeck.expansion),
+        )
+
+        self.shortcut = nn.Sequential()
+
+        if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
+                nn.BatchNorm2d(out_channels * BottleNeck.expansion)
+            )
+
+    def forward(self, x):
+        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
+
+
+class ResNetTail(nn.Module):
+    def __init__(self, block, num_block, num_classes=100):
+        super().__init__()
+
+        self.in_channels = 128
+        self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
+        self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
+        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+    def _make_layer(self, block, out_channels, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_channels, out_channels, stride))
+            self.in_channels = out_channels * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        output = self.conv4_x(x)
+        output = self.conv5_x(output)
+        output = self.avg_pool(output)
+        output = output.view(output.size(0), -1)
+        output = self.fc(output)
+
+        return output
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, num_block, num_classes=100, num_tails=20):
+        super().__init__()
+        self.in_channels = 64
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True))
+
+        self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
+        self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
+        self.tails = nn.ModuleList([ResNetTail(block, num_block, num_classes) for _ in range(num_tails)])
+
+    def _make_layer(self, block, out_channels, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_channels, out_channels, stride))
+            self.in_channels = out_channels * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x, coarse_label):
+        output = self.conv1(x)
+        output = self.conv2_x(output)
+        output = self.conv3_x(output)
+        bs = output.shape[0]
+        tailouts = []
+        for t in self.tails:
+            tailouts.append(t(output))
+        tailouts = torch.stack(tailouts, dim=0)
+        return (tailouts[coarse_label] * torch.eye(n=bs).view(bs,bs,1)).sum(dim=1)
+
+@register_model
+def register_cifar_resnet18(opt_net, opt):
+    """ return a ResNet 18 object
+    """
+    return ResNet(BasicBlock, [2, 2, 2, 2])
+
+def resnet34():
+    """ return a ResNet 34 object
+    """
+    return ResNet(BasicBlock, [3, 4, 6, 3])
+
+def resnet50():
+    """ return a ResNet 50 object
+    """
+    return ResNet(BottleNeck, [3, 4, 6, 3])
+
+def resnet101():
+    """ return a ResNet 101 object
+    """
+    return ResNet(BottleNeck, [3, 4, 23, 3])
+
+def resnet152():
+    """ return a ResNet 152 object
+    """
+    return ResNet(BottleNeck, [3, 8, 36, 3])
+
+
+if __name__ == '__main__':
+    model = ResNet(BasicBlock, [2,2,2,2])
+    print(model(torch.randn(2,3,32,32), torch.LongTensor([4,19])).shape)
+

codes/scripts/byol/byol_resnet_playground.py

@@ -4,22 +4,16 @@ import shutil
 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.transforms import ToTensor, Resize
+from torchvision.transforms import ToTensor
 from tqdm import tqdm
-import numpy as np
 
-import utils
 from data.image_folder_dataset import ImageFolderDataset
-from models.resnet_with_checkpointing import resnet50
+from models.classifiers.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 utils import util
 from utils.kmeans import kmeans, kmeans_predict
 from utils.options import dict_to_nonedict
 

codes/scripts/byol/byol_segformer_playground.py

@@ -1,5 +1,4 @@
 import os
-import shutil
 
 import torch
 import torch.nn as nn
@@ -7,20 +6,14 @@ import torch.nn.functional as F
 import torchvision
 from PIL import Image
 from torch.utils.data import DataLoader
-from torchvision.transforms import ToTensor, Resize, Normalize
+from torchvision.transforms import ToTensor, Normalize
 from tqdm import tqdm
-import numpy as np
 
-import utils
 from data.image_folder_dataset import ImageFolderDataset
-from models.resnet_with_checkpointing import resnet50
 from models.segformer.segformer import Segformer
-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 utils import util
 from utils.kmeans import kmeans, kmeans_predict
 from utils.options import dict_to_nonedict
 

codes/scripts/byol/byol_uresnet_playground.py

@@ -1,5 +1,4 @@
 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