OpenCV implementation of Torchvision transforms, from https://github.com/YU-Zhiyang/opencv_transforms_torchvision

Why? Because opencv is faster than PIL

To use do

package_path = '../input/cvtorchvision/opencv_transforms_torchvision-master/'

sys.path.append(package_path)

from cvtorchvision import cvtransforms

Dataset Card for "movie_posters-genres-80k-torchvision-transforms"

More Information needed

CIFAR-10 Dataset with format of Pytorch Tensor.

You can directly use torch.load('---File_Path---') to load data.

The whole dataset was seperated into 3 parts: train_X, train_y, test_X. Specifically, train_X contains 50, 000 'images' and test_X contains 300, 000 'images'. To be more detailed, train_X has shape of (50000, 3, 32, 32), train_y has shape of (50000,) and test_X has shape of (300000, 3, 32, 32).

Tips: If you wanna use data augment, it's unnecessary to transform these tensors to images to do so, actually you can directly apply Torchvision Transforms (or a Compose of Transforms) on tensors, it does work :)

Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding and requires careful hyperparameter tuning. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. It consists of a set of patches optimized to generalize across different models and applied to ImageNet data after preprocessing them with affine transformations. This process enables an approximate yet faster robustness evaluation, leveraging the transferability of adversarial perturbations.

We release our dataset as a set of folders indicating the patch target label (e.g., banana), each containing 1000 subfolders as the ImageNet output classes.

An example showing how to use the dataset is shown below.

code for testing robustness of a model

import os.path

from torchvision import datasets, transforms, models import torch.utils.data

class ImageFolderWithEmptyDirs(datasets.ImageFolder): """ This is required for handling empty folders from the ImageFolder Class. """

def find_classes(self, directory):
  classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
  if not classes:
    raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")
  class_to_idx = {cls_name: i for i, cls_name in enumerate(classes) if
          len(os.listdir(os.path.join(directory, cls_name))) > 0}
  return classes, class_to_idx

extract and unzip the dataset, then write top folder here

dataset_folder = 'data/ImageNet-Patch'

available_labels = { 487: 'cellular telephone', 513: 'cornet', 546: 'electric guitar', 585: 'hair spray', 804: 'soap dispenser', 806: 'sock', 878: 'typewriter keyboard', 923: 'plate', 954: 'banana', 968: 'cup' }

select folder with specific target

target_label = 954

dataset_folder = os.path.join(dataset_folder, str(target_label)) normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) transforms = transforms.Compose([ transforms.ToTensor(), normalizer ])

dataset = ImageFolderWithEmptyDirs(dataset_folder, transform=transforms) model = models.resnet50(pretrained=True) loader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=5) model.eval()

batches = 10 correct, attack_success, total = 0, 0, 0 for batch_idx, (images, labels) in enumerate(loader): if batch_idx == batches: break pred = model(images).argmax(dim=1) correct += (pred == labels).sum() attack_success += sum(pred == target_label) total += pred.shape[0]

accuracy = correct / total attack_sr = attack_success / total

print("Robust Accuracy: ", accuracy) print("Attack Success: ", attack_sr)

Example usage. You will have to use a shape batching dataset when training in batches from datasets import load_dataset from diffusers import AutoencoderDC import torch import torchvision.transforms as transforms from PIL import Image

ds = load_dataset("SwayStar123/imagenet1k_dcae-f64-latents_train")

with torch.inference_mode(): device = "cuda" ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers", cache_dir="ae", torch_dtype=torch.bfloat16).to(device).eval()… See the full description on the dataset page: https://huggingface.co/datasets/SwayStar123/imagenet1k_dcae-f64-latents.

This dataset contains only the COCO 2017 train images (118K images) and a caption annotation JSON file, designed to fit within Google Colab's available disk space of approximately 50GB when connected to a GPU runtime.

If you're using PyTorch on Google Colab, you can easily utilize this dataset as follows:

Manually downloading and uploading the file to Colab can be time-consuming. Therefore, it's more efficient to download this data directly into Google Colab. Please ensure you have first added your Kaggle key to Google Colab. You can find more details on this process here

from google.colab import drive
import os
import torch
import torchvision.datasets as dset
import torchvision.transforms as transforms

os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')
os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')

# Download the Dataset and unzip it
!kaggle datasets download -d seungjunleeofficial/coco2017-image-caption-train
!mkdir "/content/Dataset"
!unzip "coco2017-image-caption-train" -d "/content/Dataset"


# load the dataset
cap = dset.CocoCaptions(root = '/content/Dataset/COCO2017 Image Captioning Train/train2017',
            annFile = '/content/Dataset/COCO2017 Image Captioning Train/captions_train2017.json',
            transform=transforms.PILToTensor())

You can then use the dataset in the following way:

print(f"Number of samples: {len(cap)}")
img, target = cap[3]
print(img.shape)
print(target)
# Output example: torch.Size([3, 425, 640])
# ['A zebra grazing on lush green grass in a field.', 'Zebra reaching its head down to ground where grass is.', 
# 'The zebra is eating grass in the sun.', 'A lone zebra grazing in some green grass.', 
# 'A Zebra grazing on grass in a green open field.']

使用resnet18进行训练 resnet18 干净样本数据集精准度为 89.27% 对抗样本攻击成功率99%以上 攻击方法pgd 干净样本数据集 https://github.com/fastai/imagenette

resnet18模型 使用的是 https://github.com/Harry24k/MAIR/tree/main/mair中的utils resnet18

使用fastai 读取数据集

from fastai.vision.all import * from utils import * from attacks import * import torchvision.transforms as tt

def get_x(files): return files

def get_y(files): return files.parent.name

自定义数据分割函数

def my_splitter(items): train_idx = [i for i,o in enumerate(items) if 'train' in o.parts] valid_idx = [i for i,o in enumerate(items) if 'val' in o.parts] return train_idx, valid_idx

请替换"path/to/data"为您的数据根目录的实际路径

path = Path("./data/imagenette2/")

attack_pets = DataBlock( blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=my_splitter, get_x=get_x, get_y=get_y, )

attack_dls = attack_pets.dataloaders(path, shuffle=False, bs=32)

This dataset consists of 4900 images of logograms from Heptapod B language, in resolution 224x224, and the captions for their meaning in English. There are 49 unique logograms and 100 variations (rotation, scaling, translation) for each of them.

Original source of the data: Wolfram Research GitHub Repository. Distributed under Creative Commons Attribution-NonCommercial 4.0 International License.

The dataset was augmented by merging morphems of the logograms and by applying geometric transformations to create variations of each image.

The captions.txt file provide captions for each unique logogram, and can interpreted as:

• 000.png | Abbot is dead is the caption for images 0000.png to 0099.png
• 001.png | Abbot is the caption for images 0100.png to 0199.png
• 002.png | Abbot chooses save humanityis the caption for images 0200.png to 0299.png
• And so on

Suggested loading for PyTorch:

from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import os

class TextToImageDataset(Dataset):
  def _init_(self, image_dir, captions_file, transform=None):
    self.image_dir = image_dir # Path for the images on the dataset
    self.transform = transform
    self.pairs = [] # Array to store (image, sentence) pairs

    with open(captions_file, "r") as f:
      for line in f:
        idx, caption = line.strip().split("|")
        idx = idx.strip().split(".")[0]
        caption = caption.strip()
        for i in range(100):
          img_file = f"{(int(idx)*100 + i):04d}.png" # Get the image number by doing idx*100 + i 
          self.pairs.append((caption, img_file))   # Apply the same caption for every variation of the same logogram

  def _len_(self):
    return len(self.pairs)

  def _getitem_(self, idx):
    text, img_file = self.pairs[idx]
    image = Image.open(os.path.join(self.image_dir, img_file)).convert("RGB")
    if self.transform:
      image = self.transform(image)
    return text, image #item = (text, image)

transform = transforms.Compose([
  transforms.Resize((224, 224)),
  transforms.ToTensor()
])

base_dir = "/kaggle/input/heptapod-dataset/dataset/"

dataset = TextToImageDataset(image_dir=base_dir+"images",captions_file=base_dir+"captions.txt", transform=transform)

Usage

import torch
from torchvision import models, transforms

# densenet121
model = models.densenet121()
model.load_state_dict(torch.load('densenet121.pth'))

# densenet201
model = models.densenet201()
model.load_state_dict(torch.load('densenet201.pth'))

# resnet50
model = models.resnet50()
model.load_state_dict(torch.load('resnet50.pth'))

# resnet34
model = models.resnet34()
model.load_state_dict(torch.load('resnet34.pth'))

License

From the PyTorch github repo:

From PyTorch:

Copyright (c) 2016-   Facebook, Inc      (Adam Paszke)
Copyright (c) 2014-   Facebook, Inc      (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies  (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU           (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006   Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)

From Caffe2:

Copyright (c) 2016-present, Facebook Inc. All rights reserved.

All contributions by Facebook:
Copyright (c) 2016 Facebook Inc.

All contributions by Google:
Copyright (c) 2015 Google Inc.
All rights reserved.

All contributions by Yangqing Jia:
Copyright (c) 2015 Yangqing Jia
All rights reserved.

All contributions from Caffe:
Copyright(c) 2013, 2014, 2015, the respective contributors
All rights reserved.

All other contributions:
Copyright(c) 2015, 2016 the respective contributors
All rights reserved.

Caffe2 uses a copyright model similar to Caffe: each contributor holds
copyright over their contributions to Caffe2. The project versioning records
all such contribution and copyright details. If a contributor wants to further
mark their specific copyright on a particular contribution, they should
indicate their copyright solely in the commit message of the change when it is
committed.

All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright
  notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
  notice, this list of conditions and the following disclaimer in the
  documentation and/or other materials provided with the distribution.

3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
  and IDIAP Research Institute nor the names of its contributors may be
  used to endorse or promote products derived from this software without
  specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.