KerasからTorchVisionを呼んでCIFAR-10を分類してみましょう。. datasets的使用对于常用数据集,可以使用torchvision. Over 100,000 images classified into everyday objects; for example, person, backpack, and bicycle. Hi, I'm trying to install PyTorch on computer (Windows 10 OS). pip3 install torch torchvision Run vid2vid demo Out of the curiosity how well the Pytorch performs with GPU enabled on Colab, let's try the recently published Video-to-Video Synthesis demo , a Pytorch implementation of our method for high-resolution photorealistic video-to-video translation. code:: python import torchvision. You had to rinse the hell out of the coco before using it to rinse out the salts in it. CocoCaptions(root = ' dir where images are', annFile = 'json annotation file', transform = transforms. Whereas torchvision works like seamlessly and reads like a reference for writing PyTorch model code. Discussions, news and information about Jetson Nano. 29 October 2019 AlphaPose Implementation in Pytorch along with the pre-trained wights. You might not want all of the info that I loaded in this checkpoint, but I'm including the one above as an example so that you can see some options. Join GitHub today. In this article, we will use Deep Learning and computer vision for the caption generation of Avengers Endgame characters. When growing cannabis in containers, for example with soil or coco, it's important to give your plant roots enough room to grow. GitHub Gist: star and fork Tony607's gists by creating an account on GitHub. name_scope('conv1_1') as scope:", this is Tensorflow using name_scope to keep all the variables/ops organized. Management must ensure controls are in place to minimize risk and ensure financial reporting is reliable. The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. To run the code given in this example, you have to install the pre-requisites. The training set and randomly selected. PyTorch tutorial: Get started with deep learning in Python. The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. If you fail every time, you can reduce the warmup_ratio, for example, you can reduce from 1. A Keras 1. Related Work Residual Representations. vision / torchvision / datasets / coco. 参数: root(string)- 下载数据的目标目录。. Wide ResNet¶ torchvision. Thus, we need to first download the dataset. Wanna start your own payment gateway like PayPal, Perfect Money, Bitcoin or other’s. We are going to use the pytorch and torchvision package for demonstration purposes. For example:. SqueezeNet 1. The STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. 0, without sacrificing accuracy. Besides, a single model cannot handle flexible multi-domain image translation tasks. Smooth and enjoyable training procedure. The Google documentation tells us: This document lists the OAuth 2. For example:. autograd import Variable import matplotlib. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled. These models have been trained on a subset of COCO Train 2017 dataset which correspond to the PASCAL VOC dataset. pkl --eval bbox segm It is also convenient to visualize the results during testing by adding an argument --show. 1 model from the official SqueezeNet repo. Summary • We can use existing dataset provided by torch and torchvision such as CIFAR10 • Dataset is training example, epochs is on pass througout the model, batch is subset of training model and iteration is a single pass of one batch 43. For example, assuming the files are saved in ~/coco/, you can run:. Anaconda Cloud. The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We focus on. nThreads) In the constructor, each dataset has a slightly different API as needed, but they all take the keyword args:. Tasks: object class recognition, object detection/localization, person identification, gender recognition, scene classification, image captioning, visual question answering, image generation etc. nnasnnfromtorch. python coco. transforms ,分别是预定义好的数据集(比如MNIST、CIFAR10等)、预定义好的经典网络结构(比如AlexNet、VGG、ResNet等. mxnet/datasets/coco. names # 自己. torchvision. Dataset(2)torch. transforms that. A specific example is the K-FAC optimization algorithm, which uses each layer’s inputs and pre-activation gradients to approximate the Fisher information matrix. data │ ├── gesture. Parameters: tensor (Tensor or list) - 4D mini-batch Tensor of shape (B x C x H x W) or a list of images all of the same size. The following are code examples for showing how to use torchvision. NOTES: The models are evaluated on the pascal 2007 testand ms coco2015 test-dev sets. The checkpoint save should be done before you load your checkpoint in the lab. class torchvision. 3 mAP) on COCO dataset and 80+ mAP (82. Both films involve the Mexican holiday Día de los Muertos (Day of the Dead), a guitar-playing. Eligible for Free Shipping. Loss is defined as a sum of responses from multi-scale inputs (1x, 0. 1 mAP) on MPII dataset. 1 model from the official SqueezeNet repo. 0, otherwise you will run into errors. 001 and lowered by 10 times at 350K iteration. models torchvision. Example: End-to-end AlexNet from PyTorch to Caffe2¶ Here is a simple script which exports a pretrained AlexNet as defined in torchvision into ONNX. The Torch Blog Jul 25, 2016 Language modeling a billion words Noise contrastive estimation is used to train a multi-GPU recurrent neural network language model on the Google billion words dataset. Unofficial implementation to train DeepLab v2 (ResNet-101) on COCO-Stuff 10k dataset. COCO Stuff 10k is a semantic segmentation dataset, which includes 10k images from 182 thing/stuff classes. You might not want all of the info that I loaded in this checkpoint, but I'm including the one above as an example so that you can see some options. Mask R-CNN is one such algorithm. eval() Line 2 will download a pretrained Resnet50 Faster R-CNN model with pretrained weights. I am looking for Object Detection for custom dataset in PyTorch. Tutorial here provides a snippet to use pre-trained model for custom object classification model_ft = models. Look for their blog post and example colab, it's really good. 2 — L4T R32. In addition to this manual, there are various other resources that may help new users get started with torch, all summarized in this Cheatsheet. from torchvision import datasets, transforms from torch. transforms as transforms import PIL # Define the transforms need to convert ImageNet data to expected model input normalize = transforms. Besides, a single model cannot handle flexible multi-domain image translation tasks. datasets as dset import torchvision. mkvirtualenv pytorch-examples workon pytorch-examples pip install torch torchvision numpy Clone the pytorch/examples repo and go into the fast_neural_style directory, then start training a model. There are 50000 training images and 10000 test images. PyTorch torchvision COCO Dataset The PyTorch torchvision package has multiple popular built-in datasets. e, they have __getitem__ and __len__ methods implemented. 0, without sacrificing accuracy. If your installed package does not work, it may have missing dependencies that need to be resolved manually. MNIST数据集逻辑回归首先来load数据集importtorchimporttorch. There are total 20 categories supported by the models. autograd import Variable import matplotlib. The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. microsoft coco数据集,可以说就是对标pascal,imagenet数据集出现的,同样可以用于图像分类,分割,目标检测等任务,共30多万的数据。COCO数据集有91类,以人类4岁小孩能够辨识为基准。其中82个有超过5000个instance(instance即同一个类不同的个体,比如图像中不同的人)。. pytorch自发布以来,由于其便捷性,赢得了越来越多人的喜爱。 Pytorch有很多方便易用的包,今天要谈的是torchvision包,它包括3个子包,分别是: torchvison. You can vote up the examples you like or vote down the ones you don't like. A Keras 1. The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. My intention is to make a neural network which can do real-time classification from videos. 100000 unlabeled images for unsupervised learning. 2 — L4T R32. datasets ,torchvision. py and visualizing with default vis. torchvision. When growing cannabis in containers, for example with soil or coco, it's important to give your plant roots enough room to grow. Behold, Marvel Fans. Mask R-CNN is one such algorithm. The new release also contains torchvision ops - custom C++/CUDA operators that are used in computer vision. If you want to create and work with Python virtual environments, please follow the instructions on venv and virtual environmen. Free Shipping by Amazon. They are extracted from open source Python projects. datasets torchvision. Learn how to create a simple neural network, and a more accurate convolutional neural network, with the PyTorch deep learning library. Any single existing GAN model is incapable of translating "backward", like in the previous example from blond to black colored hair. DataLoader which can load multiple samples parallelly using torch. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. The following are code examples for showing how to use torchvision. Hence, they can all be passed to a torch. from torchvision import datasets, transforms from torch. COCO is a commonly used dataset for such tasks since one of the target family for COCO is Here is an example of a data point from the COCO dataset: using the torchvision's models package. I try to run ssd300_coco and ssd300_voc with 4gpu, 24 imgs per gpu and lr of 3e-3, they run wihout any problem. The Torch Blog Jul 25, 2016 Language modeling a billion words Noise contrastive estimation is used to train a multi-GPU recurrent neural network language model on the Google billion words dataset. Captions class torchvision. PDF | In the last years, neural networks have proven to be a powerful framework for various image analysis problems. autograd import Variable import matplotlib. 2018년 말 쯤 AlphaPose 라는 Real-Time 환경에서 Multi-Person Pose Estimation 및 Tracking 이 가능한 오픈 시스템이 발표되었다. transforms: (Transforms on PIL Image,Transforms on torch. torchvision是pytorch的数据集,也包含常用数据处理工具,包含几个模块:datasets(包含常用的数据集:minist,COCO等)models(包含常用的著名网络结构:AlexNet 博文 来自: geter_CS的博客. A positive example of Marlboro class can be seen in Figure 7. Shang Tang Technology (2018 COCO Target Detection Challenge Champion) and the Chinese University of Hong Kong recently opened up a Pytorch-based deep learning target detection toolbox mmdetection, supporting Faster-RCNN, Mask-RCNN, Fast-RCNN and other mainstream target detection frameworks. datasets import ImageFolder """ Example PyTorch script for finetuning a ResNet model on your own data. In order to show you how you should use the augmentations we just implemented, we take the example of COCO dataset. ToTensor()) print('Number of samples: ', len. Edit 1: I have done the coding as suggested by Fábio Perez. The Torch Blog Jul 25, 2016 Language modeling a billion words Noise contrastive estimation is used to train a multi-GPU recurrent neural network language model on the Google billion words dataset. If you know how to create COCO datasets, please read my previous post - How to create custom COCO data set for instance segmentation. The specific model we are going to be using is ResNet34, part of the Resnet series. transforms that. The initial learning rate is set to 0. Hats off to his excellent examples in Pytorch! In this walkthrough, a pre-trained resnet-152 model is used as an encoder, and the decoder is an LSTM network. Related Work Residual Representations. Other readers will always be interested in your opinion of the books you've read. A function/transform that takes input sample and its target as entry and returns a transformed version. DataLoader which can load multiple samples parallelly using torch. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. The result of my example is this: Here is my example with all commented lines for a good approach:. My intention is to make a neural network which can do real-time classification from videos. Skip connection enables to have deeper network and finally ResNet becomes the Winner of ILSVRC 2015 in image classification, detection, and localization, as well as Winner of MS COCO 2015 detection, and segmentation. transfoms各种方法解析. , 2014) pre-trained implementation of Mask R-CNN provided by the torchvision library that is developed by the PyTorch (Paszke et al. set_image_backend(backend) #改变图像读取引擎 #backend (string) –图像引擎的名字:是 {‘PIL’, ‘accimage’}其中之一。 accimage 包使用的是因特尔(Intel. utils 致谢 返回 PyTorch 中文网. models 에서는 미리 정의되어 있는 모델들을 사용할 수 있다. For example, assuming the files are saved in ~/coco/, you can run:. PyTorch中提供了MNIST,CIFAR,COCO等常用数据集的加载方法。 MNIST 是 torchvision. torchvision. To see the list of the built-in datasets, visit this link. (diff to make it use the same hyperparameters - click to expand). For this example we will use a tiny dataset of images from the COCO dataset. This tokenization turns any strings into a list of integers. (Sik-Ho Tsang @ Medium). We have chosen eight types of animals (bear, bird, cat, dog, giraffe, horse,. DataLoader which can load multiple samples parallelly using torch. For this example we will use a tiny dataset of images from the COCO dataset. Other readers will always be interested in your opinion of the books you've read. , 2017) proved to be an effective and efficient instance segmentation algorithm, hence we adopted it for use in our system [We use the COCO (Lin et al. Tensor containing the model output; first dimension should be output for each example (length batch_size) and second dimension should be output for each class in ImageNet (length 1000). PyTorch tutorial: Get started with deep learning in Python. Wide ResNet¶ torchvision. 本文共2200字,建议阅读10分钟。 本文用浅显易懂的方式解释了什么是"看图说话"(Image Captioning),借助github上的PyTorch代码带领大家自己做一个模型,并附带了很多相关的学习资源。. SqueezeNet 1. import torchvision: import torchvision. Management must ensure controls are in place to minimize risk and ensure financial reporting is reliable. Model output (following model. mmdetection is an open source object detection toolbox based on PyTorch. import torchvision from torchvision. This is typical when. pytorch读取训练集是非常便捷的,只需要使用到2个类:(1)torch. torchvision. The following are code examples for showing how to use pycocotools. To analyze traffic and optimize your experience, we serve cookies on this site. For this example we will use a tiny dataset of images from the COCO dataset. Colab 是谷歌内部类 Jupyter Notebook 的交互式 Python 环境,免安装快速切换 Python 2、3 的环境,支持 Google 全家桶,支持 pip 安装任意自定义库。. The training set and randomly selected. Touch to PyTorch ISL Lab Seminar Hansol Kang : From basic to vanilla GAN 2. Datasets¶ All datasets are subclasses of torch. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. transfoms各种方法解析. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. pkl --eval bbox segm It is also convenient to visualize the results during testing by adding an argument --show. annFile (string) - json 注释文件存放的路径. image_classification import ImageNet from torchvision. from collections import OrderedDict import torch from torch import nn import torch. Running on VOC2006 test data If at all possible, participants are requested to submit results for both the VOC2007 and VOC2006 test sets provided in the test data, to allow comparison of results across the years. Deep learning has penetrated all aspects of our lives and brought us great convenience. ImageNet, COCO, CelebA etc. Contents October 9, 2018 Setup Install Development Tools Example What is PyTorch? PyTorch Deep Learning. PyTorch includes following dataset loaders − MNIST; COCO (Captioning and Detection) Dataset includes majority of two types of functions given below − Transform − a function that takes in an image and returns a modified version of standard stuff. image_classification import ImageNet from torchvision. PyTorch tutorial: Get started with deep learning in Python. import torchvision: import torchvision. shellpython tools/test. These examples are extracted from a similar but broader distribution of images. torchvision. 0098 and validation classification accuracy of 81. 3 includes many popular models for segmentation, detection, and classification. Here is an example, taken from the PyTorch examples: There is a lot going on here, but it is fairly straightforward if we take it a line at a time. You can vote up the examples you like or vote down the ones you don't like. datasets中包含了以下数据集. Related Work Residual Representations. A specific example is the K-FAC optimization algorithm, which uses each layer’s inputs and pre-activation gradients to approximate the Fisher information matrix. For example: torch. vision / torchvision / models / detection / faster_rcnn. I used 300 annotated images with three labels, pytorch, torchvision 0. x example. com Vijay Vasudevan Google Brain [email protected] For this we transform the captioned associated with the image into a list of tokenize words. torchvision (conda install torchvision -c soumith) opencv (conda install -c conda-forge opencv ) cython (conda install cython) matplotlib (conda install matplotlib) scikit-image (conda install scikit-image) ninja (conda install ninja) (required for Pytorch 0. The RNN component of the captioning network is trained on the captions in the COCO dataset. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled. « Tasks Installing licenses ». Coco is a film that reminds us of the effect and importance of mise-en-scène, even when it has been assembled by animators and computers. Besides, a single model cannot handle flexible multi-domain image translation tasks. 1% mAP on the COCO dataset. Neural Network Input Hidden 1 Hidden 2 Output Layers 45. A Keras 1. TorchVisionをtransforms. Define the class names given by PyTorch's official Docs. PyTorch includes following dataset loaders − MNIST; COCO (Captioning and Detection) Dataset includes majority of two types of functions given below − Transform − a function that takes in an image and returns a modified version of standard stuff. I'd try to keep it as general as possible so you can also make it work with other libraries or your own custom code. In this chapter, we will focus more on torchvision. Lambdaを使ってchannel-last化するだけではなく、TorchVision特有の変形操作も使えるというのが確認できました。 TorchVisionをKerasで使ったCIFAR-10分類. models ,torchvision. 1-16 of 31 results for "coco chanel perfume sample" Skip to main search results Amazon Prime. ¶ While I do not like the idea of asking you to do an activity just to teach you a tool, I feel strongly about pytorch that I think you should know how to use it. resnet18(pretrained=T. DataLoader(coco_cap, batch_size=args. cfg ├── data │ ├── coco. The code for this example can be found on GitHub. The following are code examples for showing how to use torchvision. annFile (string) - json 注释文件存放的路径. 3 mAP) on COCO dataset and 80+ mAP (82. Neural Network Input Hidden 1 Hidden 2 Output Layers 45. fasterrcnn_resnet50_fpn(pretrained=True) model. If you are planning to contribute back bug-fixes, please do so without any further discussion. transforms as T: from torchvision. Chesterfield (brand) doesn’t belong to any of the 10 brands and is a negative class while Marlboro (brand) is one of the 10 brands but the given instance is a different product than those in our trainset. We are assuming that you have default Python environment already configured on your computer and you intend to install scikit-image inside of it. Here, we are creating 1st convolutional layer so we have added ' conv1_1' as a prefix in front of all the variables. Home; People. Using this special label, we will be able to use the gift-breaking information. As an example, the MAQC initiatives [30, 31], led by the US FDA, investigate best practices and causes of variability in the development of biomarkers and predictive classifiers from massive omics data (e. The following example is used to demonstrate the COCO implementation of dataset using PyTorch − import torchvision. It is inspired by the CIFAR-10 dataset but with some modifications. Over 100,000 images classified into everyday objects; for example, person, backpack, and bicycle. models의 모델을 사용하는 방법 torchvision. pytorch读取训练集是非常便捷的,只需要使用到2个类:(1)torch. models torchvision. nThreads) In the constructor, each dataset has a slightly different API as needed, but they all take the keyword args:. I am a little bit confused about the data augmentation performed in PyTorch. 3 includes many popular models for segmentation, detection, and classification. torchvision. As an example, the MAQC initiatives [30, 31], led by the US FDA, investigate best practices and causes of variability in the development of biomarkers and predictive classifiers from massive omics data (e. There are total 20 categories supported by the models. CocoCaptions(root, annFile, transform=None, target_transform=None) MS Coco Captions Dataset. Related Work Residual Representations. I am looking for Object Detection for custom dataset in PyTorch. wide_resnet50_2 (pretrained=False, progress=True, **kwargs) [source] ¶ Wide ResNet-50-2 model from "Wide Residual Networks" The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. from torchvision import datasets, transforms from torch. patches as patches from PIL import Image. transforms as transforms cap = dset. It produces good results and. Unofficial implementation to train DeepLab v2 (ResNet-101) on COCO-Stuff 10k dataset. I am running deeplab on the DeepFashion2 Dataset and am encountering problems when visualizing my results with vis. ├── assets # 4张示例标注图片 ├── config │ ├── coco. Based on this framework, we recorded the 1st place of ICDAR2013 focused scene text, ICDAR2019 ArT and 3rd place of ICDAR2017 COCO-Text, ICDAR2019 ReCTS (task1). py Find file Copy path varunagrawal updated all docstrings and code references for boxes to be consistent… 95a8785 Jul 12, 2019. cfg │ └── yolov3_gesture_6. which will automatically download and extract the data into ~/. I try to run ssd300_coco and ssd300_voc with 4gpu, 24 imgs per gpu and lr of 3e-3, they run wihout any problem. Q&A for Work. Transform a tensor image with a square transformation matrix computed offline. Here is an example, taken from the PyTorch examples: There is a lot going on here, but it is fairly straightforward if we take it a line at a time. 8, and through Docker and AWS. In web development, it's good practice to provide a description for any image that appears on the page so that an image can be read or heard as opposed to just seen. Apart from mmdetection, we also released a library mmcv for computer vision research,which is heavily depended on by this toolbox. Transfoms 是很常用的图片变换方式,可以通过Compose将多个变换方式结合在一起. 0 0 1 0 0 0 0 0 0 0 PyTorch has an integrated MNIST dataset in the torchvision package which we can use via the DataLoader functionality use to store the MNIST dataset PyTorch will download the dataset into this folder for you. In addition to this manual, there are various other resources that may help new users get started with torch, all summarized in this Cheatsheet. Using this special label, we will be able to use the gift-breaking information. This was the codebase of the MMDet team, who won the COCO Detection 2018 challenge. Q&A for Work. torchvision. faster_rcnn import FastRCNNPredictor # load a model pre-trained pre-trained on COCO model = torchvision. COCO(用于图像标注和目标检测)(Captioning and Detection) (详情请看下面关于torchvision-tranform Here is an example. Hats off to his excellent examples in Pytorch! In this walkthrough, a pre-trained resnet-152 model is used as an encoder, and the decoder is an LSTM network. which will automatically download and extract the data into ~/. PyTorch tutorial: Get started with deep learning in Python. pytorch -- a next generation tensor / deep learning framework. You can replace every component with your own code without change the code base. TorchVisionをtransforms. models 에서는 미리 정의되어 있는 모델들을 사용할 수 있다. Here they have used Standard COCO Dataset. transforms torchvision. 11/02/18 - To achieve the long-term goal of machines being able to engage humans in conversation, our models should be engaging. TorchVisionをtransforms. Thus, we need to first download the dataset. batchSize, shuffle=True, num_workers=args. code:: python import torchvision. Hats off to his excellent examples in Pytorch! In this walkthrough, a pre-trained resnet-152 model is used as an encoder, and the decoder is an LSTM network. transforms as transforms cap = dset. COCO(用于图像标注和目标检测)(Captioning and Detection) LSUN Classification. Finally, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4. For example, in the COCO (Chen et al. torchvision. Management must ensure controls are in place to minimize risk and ensure financial reporting is reliable. PyTorch 튜토리얼 (Touch to PyTorch) 1. transforms and makes it easier to do data augmentation for object detection with torchvision. From a clean conda env, this is what you need to do conda create --name maskrcnn_benchmark conda activate maskrcnn_benchmark # this installs the right pip and dependencies for the fresh python conda install ipython # maskrcnn_benchmark and coco api dependencies pip install ninja yacs cython matplotlib tqdm opencv-python # follow PyTorch. Define the class names given by PyTorch's official Docs. Transoforms in hutil transform inputs and targets of datasets simultaneously, which is more flexible than torchvison. The batch size is left at the default (4) so it will be easier to replicate these results on smaller hardware, but of course feel free to increase the. DeepLab is one of the CNN architectures for semantic image segmentation. 29 October 2019 AlphaPose Implementation in Pytorch along with the pre-trained wights. Besides, a single model cannot handle flexible multi-domain image translation tasks. GitHub Gist: star and fork Tony607's gists by creating an account on GitHub. e, they have __getitem__ and __len__ methods implemented. py file added to the torchvision repository to test one of its constituent models: from torchbench. Considering additional models provided by scene parsing challenge 2016, we do a combination of these models via post network. Source code for torchvision. Deep learning has penetrated all aspects of our lives and brought us great convenience. COCO Stuff: For COCO, there is two partitions, CocoStuff10k with only 10k that are used for training the evaluation, note that this dataset is outdated, can be used for small scale testing and training, and can be downloaded here. As we mentioned above, torchvision 0. With some modification for scene parsing task, we train multiscale dilated network [2] initialised by trained parameter of ResNet-101, and FCN-8x and FCN-16x [3] trained parameter of ResNet-50. For example:. patches as patches from PIL import Image. COCO is a commonly used dataset for such tasks since one of the target family for COCO is Here is an example of a data point from the COCO dataset: using the torchvision’s models package. datasets¶ All datasets are subclasses of torch. Lambdaを使ってchannel-last化するだけではなく、TorchVision特有の変形操作も使えるというのが確認できました。 TorchVisionをKerasで使ったCIFAR-10分類. I am using the following command in the windows command line: conda install. DeepLab is one of the CNN architectures for semantic image segmentation. transforms as transforms import PIL # Define the transforms need to convert ImageNet data to.
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