fastai--fastai
wehub-resource-sync
666 行
26 KiB
Plaintext
666 行
26 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "48e777ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| hide\n",
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"#| eval: false\n",
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"! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d32b9e93",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| default_exp data.external"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "55d5ab7c",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"from fastai.torch_basics import *\n",
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"from fastdownload import FastDownload\n",
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"from functools import lru_cache\n",
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"import fastai.data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "80818f7c",
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"metadata": {},
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"source": [
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"# External data\n",
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"> Helper functions to download the fastai datasets"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b878ae8a",
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"metadata": {},
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"source": [
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"To download any of the datasets or pretrained weights, simply run `untar_data` by passing any dataset name mentioned above like so: \n",
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"\n",
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"```python \n",
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"path = untar_data(URLs.PETS)\n",
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"path.ls()\n",
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"\n",
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">> (#7393) [Path('/home/ubuntu/.fastai/data/oxford-iiit-pet/images/keeshond_34.jpg'),...]\n",
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"```\n",
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"\n",
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"To download model pretrained weights: \n",
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"```python \n",
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"path = untar_data(URLs.WT103_BWD)\n",
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"path.ls()\n",
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"\n",
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">> (#2) [Path('/home/ubuntu/.fastai/data/wt103-bwd/itos_wt103.pkl'),Path('/home/ubuntu/.fastai/data/wt103-bwd/lstm_bwd.pth')]\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fec6164f",
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"metadata": {},
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"source": [
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"## Datasets"
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]
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},
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{
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"cell_type": "markdown",
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"id": "14029004",
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"metadata": {},
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"source": [
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" A complete list of datasets that are available by default inside the library are: "
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]
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},
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{
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"cell_type": "markdown",
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"id": "8fe5235d",
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"metadata": {},
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"source": [
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"### Main datasets"
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]
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},
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{
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"cell_type": "markdown",
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"id": "87aeece0",
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"metadata": {},
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"source": [
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"1. **ADULT_SAMPLE**: A small of the [adults dataset](https://archive.ics.uci.edu/ml/datasets/Adult) to predict whether income exceeds $50K/yr based on census data. \n",
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"- **BIWI_SAMPLE**: A [BIWI kinect headpose database](https://www.kaggle.com/kmader/biwi-kinect-head-pose-database). The dataset contains over 15K images of 20 people (6 females and 14 males - 4 people were recorded twice). For each frame, a depth image, the corresponding rgb image (both 640x480 pixels), and the annotation is provided. The head pose range covers about +-75 degrees yaw and +-60 degrees pitch. \n",
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"1. **CIFAR**: The famous [cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset which consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. \n",
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"1. **COCO_SAMPLE**: A sample of the [coco dataset](http://cocodataset.org/#home) for object detection. \n",
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"1. **COCO_TINY**: A tiny version of the [coco dataset](http://cocodataset.org/#home) for object detection.\n",
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"- **HUMAN_NUMBERS**: A synthetic dataset consisting of human number counts in text such as one, two, three, four.. Useful for experimenting with Language Models.\n",
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"- **IMDB**: The full [IMDB sentiment analysis dataset](https://ai.stanford.edu/~amaas/data/sentiment/). \n",
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"\n",
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"- **IMDB_SAMPLE**: A sample of the full [IMDB sentiment analysis dataset](https://ai.stanford.edu/~amaas/data/sentiment/). \n",
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"- **ML_SAMPLE**: A movielens sample dataset for recommendation engines to recommend movies to users. \n",
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"- **ML_100k**: The movielens 100k dataset for recommendation engines to recommend movies to users. \n",
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"- **MNIST_SAMPLE**: A sample of the famous [MNIST dataset](http://yann.lecun.com/exdb/mnist/) consisting of handwritten digits. \n",
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"- **MNIST_TINY**: A tiny version of the famous [MNIST dataset](http://yann.lecun.com/exdb/mnist/) consisting of handwritten digits. \n",
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"- **MNIST_VAR_SIZE_TINY**: \n",
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"- **PLANET_SAMPLE**: A sample of the planets dataset from the Kaggle competition [Planet: Understanding the Amazon from Space](https://www.kaggle.com/c/planet-understanding-the-amazon-from-space).\n",
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"- **PLANET_TINY**: A tiny version of the planets dataset from the Kaggle competition [Planet: Understanding the Amazon from Space](https://www.kaggle.com/c/planet-understanding-the-amazon-from-space) for faster experimentation and prototyping.\n",
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"- **IMAGENETTE**: A smaller version of the [imagenet dataset](http://www.image-net.org/) pronounced just like 'Imagenet', except with a corny inauthentic French accent. \n",
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"- **IMAGENETTE_160**: The 160px version of the Imagenette dataset. \n",
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"- **IMAGENETTE_320**: The 320px version of the Imagenette dataset. \n",
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"- **IMAGEWOOF**: Imagewoof is a subset of 10 classes from Imagenet that aren't so easy to classify, since they're all dog breeds.\n",
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"- **IMAGEWOOF_160**: 160px version of the ImageWoof dataset. \n",
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"- **IMAGEWOOF_320**: 320px version of the ImageWoof dataset.\n",
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"- **IMAGEWANG**: Imagewang contains Imagenette and Imagewoof combined, but with some twists that make it into a tricky semi-supervised unbalanced classification problem\n",
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"- **IMAGEWANG_160**: 160px version of Imagewang. \n",
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"- **IMAGEWANG_320**: 320px version of Imagewang. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "c49ea82c",
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"metadata": {},
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"source": [
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"### Kaggle competition datasets"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e3644fda",
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"metadata": {},
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"source": [
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"1. **DOGS**: Image dataset consisting of dogs and cats images from [Dogs vs Cats kaggle competition](https://www.kaggle.com/c/dogs-vs-cats). "
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]
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},
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{
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"cell_type": "markdown",
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"id": "0ba00dfb",
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"metadata": {},
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"source": [
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"### Image Classification datasets"
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]
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},
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{
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"cell_type": "markdown",
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"id": "85331882",
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"metadata": {},
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"source": [
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"1. **CALTECH_101**: Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc 'Aurelio Ranzato.\n",
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"1. **CARS**: The [Cars dataset](https://ai.stanford.edu/~jkrause/cars/car_dataset.html) contains 16,185 images of 196 classes of cars. \n",
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"1. **CIFAR_100**: The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. \n",
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"1. **CUB_200_2011**: Caltech-UCSD Birds-200-2011 (CUB-200-2011) is an extended version of the CUB-200 dataset, with roughly double the number of images per class and new part location annotations\n",
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"1. **FLOWERS**: 17 category [flower dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/) by gathering images from various websites.\n",
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"1. **FOOD**: \n",
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"1. **MNIST**: [MNIST dataset](http://yann.lecun.com/exdb/mnist/) consisting of handwritten digits. \n",
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"1. **PETS**: A 37 category [pet dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) with roughly 200 images for each class."
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]
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},
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{
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"cell_type": "markdown",
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"id": "5daa2fcb",
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"metadata": {},
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"source": [
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"### NLP datasets"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b90b0f51",
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"metadata": {},
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"source": [
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"1. **AG_NEWS**: The AG News corpus consists of news articles from the AG’s corpus of news articles on the web pertaining to the 4 largest classes. The dataset contains 30,000 training and 1,900 testing examples for each class.\n",
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"1. **AMAZON_REVIEWS**: This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014.\n",
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"1. **AMAZON_REVIEWS_POLARITY**: Amazon reviews dataset for sentiment analysis.\n",
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"1. **DBPEDIA**: The DBpedia ontology dataset contains 560,000 training samples and 70,000 testing samples for each of 14 nonoverlapping classes from DBpedia. \n",
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"1. **MT_ENG_FRA**: Machine translation dataset from English to French.\n",
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"1. **SOGOU_NEWS**: [The Sogou-SRR](http://www.thuir.cn/data-srr/) (Search Result Relevance) dataset was constructed to support researches on search engine relevance estimation and ranking tasks.\n",
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"1. **WIKITEXT**: The [WikiText language modeling dataset](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. \n",
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"1. **WIKITEXT_TINY**: A tiny version of the WIKITEXT dataset.\n",
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"1. **YAHOO_ANSWERS**: YAHOO's question answers dataset.\n",
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"1. **YELP_REVIEWS**: The [Yelp dataset](https://www.yelp.com/dataset) is a subset of YELP businesses, reviews, and user data for use in personal, educational, and academic purposes\n",
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"1. **YELP_REVIEWS_POLARITY**: For sentiment classification on YELP reviews."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3b59cf8",
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"metadata": {},
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"source": [
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"### Image localization datasets"
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]
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},
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{
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"cell_type": "markdown",
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"id": "66c974e6",
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"metadata": {},
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"source": [
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"1. **BIWI_HEAD_POSE**: A [BIWI kinect headpose database](https://www.kaggle.com/kmader/biwi-kinect-head-pose-database). The dataset contains over 15K images of 20 people (6 females and 14 males - 4 people were recorded twice). For each frame, a depth image, the corresponding rgb image (both 640x480 pixels), and the annotation is provided. The head pose range covers about +-75 degrees yaw and +-60 degrees pitch. \n",
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"1. **CAMVID**: Consists of driving labelled dataset for segmentation type models.\n",
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"1. **CAMVID_TINY**: A tiny camvid dataset for segmentation type models.\n",
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"1. **LSUN_BEDROOMS**: [Large-scale Image Dataset](https://arxiv.org/abs/1506.03365) using Deep Learning with Humans in the Loop\n",
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"1. **PASCAL_2007**: [Pascal 2007 dataset](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/) to recognize objects from a number of visual object classes in realistic scenes.\n",
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"1. **PASCAL_2012**: [Pascal 2012 dataset](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) to recognize objects from a number of visual object classes in realistic scenes."
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]
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},
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{
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"cell_type": "markdown",
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"id": "173fa4eb",
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"metadata": {},
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"source": [
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"### Audio classification"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e272df6b",
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"metadata": {},
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"source": [
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"1. **MACAQUES**: [7285 macaque coo calls](https://datadryad.org/stash/dataset/doi:10.5061/dryad.7f4p9) across 8 individuals from [Distributed acoustic cues for caller identity in macaque vocalization](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4806230).\n",
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"2. **ZEBRA_FINCH**: [3405 zebra finch calls](https://ndownloader.figshare.com/articles/11905533/versions/1) classified [across 11 call types](https://link.springer.com/article/10.1007/s10071-015-0933-6). Additional labels include name of individual making the vocalization and its age."
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]
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},
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{
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"cell_type": "markdown",
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"id": "cca5e63c",
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"metadata": {},
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"source": [
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"### Medical imaging datasets"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3bc9bafa",
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"metadata": {},
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"source": [
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"1. **SIIM_SMALL**: A smaller version of the [SIIM dataset](https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/overview) where the objective is to classify pneumothorax from a set of chest radiographic images.\n",
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"2. **TCGA_SMALL**: A smaller version of the [TCGA-OV dataset](http://doi.org/10.7937/K9/TCIA.2016.NDO1MDFQ) with subcutaneous and visceral fat segmentations. Citations:\n",
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"\n",
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" Holback, C., Jarosz, R., Prior, F., Mutch, D. G., Bhosale, P., Garcia, K., … Erickson, B. J. (2016). Radiology Data from The Cancer Genome Atlas Ovarian Cancer [TCGA-OV] collection. The Cancer Imaging Archive. [paper](http://doi.org/10.7937/K9/TCIA.2016.NDO1MDFQ)\n",
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"\n",
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" Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. [paper](https://link.springer.com/article/10.1007/s10278-013-9622-7)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2ccf2a7d",
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"metadata": {},
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"source": [
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"### Pretrained models"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1275aa1b",
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"metadata": {},
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"source": [
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"1. **OPENAI_TRANSFORMER**: The GPT2 Transformer pretrained weights.\n",
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"1. **WT103_FWD**: The WikiText-103 forward language model weights.\n",
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"1. **WT103_BWD**: The WikiText-103 backward language model weights."
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]
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},
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{
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"cell_type": "markdown",
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"id": "77e32d72",
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"metadata": {},
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"source": [
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"## Config"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fd51c505",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"@lru_cache(maxsize=None)\n",
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"def fastai_cfg() -> Config: # Config that contains default download paths for `data`, `model`, `storage` and `archive`\n",
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" \"`Config` object for fastai's `config.ini`\"\n",
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" return Config(Path(os.getenv('FASTAI_HOME', '~/.fastai')), 'config.ini', create=dict(\n",
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" data = 'data', archive = 'archive', storage = 'tmp', model = 'models'))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "1daa3491",
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"metadata": {},
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"source": [
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"This is a basic `Config` file that consists of `data`, `model`, `storage` and `archive`. \n",
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"All future downloads occur at the paths defined in the config file based on the type of download. For example, all future fastai datasets are downloaded to the `data` while all pretrained model weights are download to `model` unless the default download location is updated. The config file directory is defined by enviromental variable `FASTAI_HOME` if it exists, otherwise it is set to `~/.fastai`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "47a68059",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"('data', Path('/home/jhoward/.fastai/data'))"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"cfg = fastai_cfg()\n",
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"cfg.data,cfg.path('data')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ce8ec243",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"def fastai_path(folder:str) -> Path: \n",
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" \"Local path to `folder` in `Config`\"\n",
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" return fastai_cfg().path(folder)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2289b789",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Path('/home/jhoward/.fastai/archive')"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fastai_path('archive')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0143ec12",
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"metadata": {},
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"source": [
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"## URLs -"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b53b4121",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"class URLs():\n",
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" \"Global constants for dataset and model URLs.\"\n",
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" LOCAL_PATH = Path.cwd()\n",
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" MDL = 'http://files.fast.ai/models/'\n",
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" GOOGLE = 'https://storage.googleapis.com/'\n",
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" S3 = 'https://s3.amazonaws.com/fast-ai-'\n",
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" URL = f'{S3}sample/'\n",
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"\n",
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" S3_IMAGE = f'{S3}imageclas/'\n",
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" S3_IMAGELOC = f'{S3}imagelocal/'\n",
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" S3_AUDI = f'{S3}audio/'\n",
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" S3_NLP = f'{S3}nlp/'\n",
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" S3_COCO = f'{S3}coco/'\n",
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" S3_MODEL = f'{S3}modelzoo/'\n",
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"\n",
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" # main datasets\n",
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" ADULT_SAMPLE = f'{URL}adult_sample.tgz'\n",
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" BIWI_SAMPLE = f'{URL}biwi_sample.tgz'\n",
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" CIFAR = f'{URL}cifar10.tgz'\n",
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" COCO_SAMPLE = f'{S3_COCO}coco_sample.tgz'\n",
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" COCO_TINY = f'{S3_COCO}coco_tiny.tgz'\n",
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" HUMAN_NUMBERS = f'{URL}human_numbers.tgz'\n",
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" IMDB = f'{S3_NLP}imdb.tgz'\n",
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" IMDB_SAMPLE = f'{URL}imdb_sample.tgz'\n",
|
|
" ML_SAMPLE = f'{URL}movie_lens_sample.tgz'\n",
|
|
" ML_100k = 'https://files.grouplens.org/datasets/movielens/ml-100k.zip'\n",
|
|
" MNIST_SAMPLE = f'{URL}mnist_sample.tgz'\n",
|
|
" MNIST_TINY = f'{URL}mnist_tiny.tgz'\n",
|
|
" MNIST_VAR_SIZE_TINY = f'{S3_IMAGE}mnist_var_size_tiny.tgz'\n",
|
|
" PLANET_SAMPLE = f'{URL}planet_sample.tgz'\n",
|
|
" PLANET_TINY = f'{URL}planet_tiny.tgz'\n",
|
|
" IMAGENETTE = f'{S3_IMAGE}imagenette2.tgz'\n",
|
|
" IMAGENETTE_160 = f'{S3_IMAGE}imagenette2-160.tgz'\n",
|
|
" IMAGENETTE_320 = f'{S3_IMAGE}imagenette2-320.tgz'\n",
|
|
" IMAGEWOOF = f'{S3_IMAGE}imagewoof2.tgz'\n",
|
|
" IMAGEWOOF_160 = f'{S3_IMAGE}imagewoof2-160.tgz'\n",
|
|
" IMAGEWOOF_320 = f'{S3_IMAGE}imagewoof2-320.tgz'\n",
|
|
" IMAGEWANG = f'{S3_IMAGE}imagewang.tgz'\n",
|
|
" IMAGEWANG_160 = f'{S3_IMAGE}imagewang-160.tgz'\n",
|
|
" IMAGEWANG_320 = f'{S3_IMAGE}imagewang-320.tgz'\n",
|
|
"\n",
|
|
" # kaggle competitions download dogs-vs-cats -p {DOGS.absolute()}\n",
|
|
" DOGS = f'{URL}dogscats.tgz'\n",
|
|
"\n",
|
|
" # image classification datasets\n",
|
|
" CALTECH_101 = f'{S3_IMAGE}caltech_101.tgz'\n",
|
|
" CARS = f'{S3_IMAGE}stanford-cars.tgz'\n",
|
|
" CIFAR_100 = f'{S3_IMAGE}cifar100.tgz'\n",
|
|
" CUB_200_2011 = f'{S3_IMAGE}CUB_200_2011.tgz'\n",
|
|
" FLOWERS = f'{S3_IMAGE}oxford-102-flowers.tgz'\n",
|
|
" FOOD = f'{S3_IMAGE}food-101.tgz'\n",
|
|
" MNIST = f'{S3_IMAGE}mnist_png.tgz'\n",
|
|
" PETS = f'{S3_IMAGE}oxford-iiit-pet.tgz'\n",
|
|
"\n",
|
|
" # NLP datasets\n",
|
|
" AG_NEWS = f'{S3_NLP}ag_news_csv.tgz'\n",
|
|
" AMAZON_REVIEWS = f'{S3_NLP}amazon_review_full_csv.tgz'\n",
|
|
" AMAZON_REVIEWS_POLARITY = f'{S3_NLP}amazon_review_polarity_csv.tgz'\n",
|
|
" DBPEDIA = f'{S3_NLP}dbpedia_csv.tgz'\n",
|
|
" MT_ENG_FRA = f'{S3_NLP}giga-fren.tgz'\n",
|
|
" SOGOU_NEWS = f'{S3_NLP}sogou_news_csv.tgz'\n",
|
|
" WIKITEXT = f'{S3_NLP}wikitext-103.tgz'\n",
|
|
" WIKITEXT_TINY = f'{S3_NLP}wikitext-2.tgz'\n",
|
|
" YAHOO_ANSWERS = f'{S3_NLP}yahoo_answers_csv.tgz'\n",
|
|
" YELP_REVIEWS = f'{S3_NLP}yelp_review_full_csv.tgz'\n",
|
|
" YELP_REVIEWS_POLARITY = f'{S3_NLP}yelp_review_polarity_csv.tgz'\n",
|
|
"\n",
|
|
" # Image localization datasets\n",
|
|
" BIWI_HEAD_POSE = f\"{S3_IMAGELOC}biwi_head_pose.tgz\"\n",
|
|
" CAMVID = f'{S3_IMAGELOC}camvid.tgz'\n",
|
|
" CAMVID_TINY = f'{URL}camvid_tiny.tgz'\n",
|
|
" LSUN_BEDROOMS = f'{S3_IMAGE}bedroom.tgz'\n",
|
|
" PASCAL_2007 = f'{S3_IMAGELOC}pascal_2007.tgz'\n",
|
|
" PASCAL_2012 = f'{S3_IMAGELOC}pascal_2012.tgz'\n",
|
|
"\n",
|
|
" # Audio classification datasets\n",
|
|
" MACAQUES = f'{GOOGLE}ml-animal-sounds-datasets/macaques.zip'\n",
|
|
" ZEBRA_FINCH = f'{GOOGLE}ml-animal-sounds-datasets/zebra_finch.zip'\n",
|
|
"\n",
|
|
" # Medical Imaging datasets\n",
|
|
" #SKIN_LESION = f'{S3_IMAGELOC}skin_lesion.tgz'\n",
|
|
" SIIM_SMALL = f'{S3_IMAGELOC}siim_small.tgz'\n",
|
|
" TCGA_SMALL = f'{S3_IMAGELOC}tcga_small.tgz'\n",
|
|
"\n",
|
|
" #Pretrained models\n",
|
|
" OPENAI_TRANSFORMER = f'{S3_MODEL}transformer.tgz'\n",
|
|
" WT103_FWD = f'{S3_MODEL}wt103-fwd.tgz'\n",
|
|
" WT103_BWD = f'{S3_MODEL}wt103-bwd.tgz'\n",
|
|
"\n",
|
|
" def path(\n",
|
|
" url:str='.', # File to download\n",
|
|
" c_key:str='archive' # Key in `Config` where to save URL\n",
|
|
" ) -> Path:\n",
|
|
" \"Local path where to download based on `c_key`\"\n",
|
|
" fname = url.split('/')[-1]\n",
|
|
" local_path = URLs.LOCAL_PATH/('models' if c_key=='model' else 'data')/fname\n",
|
|
" if local_path.exists(): return local_path\n",
|
|
" return fastai_path(c_key)/fname"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c4b94425",
|
|
"metadata": {},
|
|
"source": [
|
|
"The default local path is at `~/.fastai/archive/` but this can be updated by passing a different `c_key`. Note: `c_key` should be one of `'archive', 'data', 'model', 'storage'`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "12394b14",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Path('/home/jhoward/.fastai/archive/oxford-iiit-pet.tgz')"
|
|
]
|
|
},
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"url = URLs.PETS\n",
|
|
"local_path = URLs.path(url)\n",
|
|
"test_eq(local_path.parent, fastai_path('archive'))\n",
|
|
"local_path"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b3beac1c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Path('/home/jhoward/.fastai/models/oxford-iiit-pet.tgz')"
|
|
]
|
|
},
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"local_path = URLs.path(url, c_key='model')\n",
|
|
"test_eq(local_path.parent, fastai_path('model'))\n",
|
|
"local_path"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "59a66ef1",
|
|
"metadata": {},
|
|
"source": [
|
|
"## untar_data -"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "29ca0dc8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#| export\n",
|
|
"def untar_data(\n",
|
|
" url:str, # File to download\n",
|
|
" archive:Path=None, # Optional override for `Config`'s `archive` key\n",
|
|
" data:Path=None, # Optional override for `Config`'s `data` key\n",
|
|
" c_key:str='data', # Key in `Config` where to extract file\n",
|
|
" force_download:bool=False, # Setting to `True` will overwrite any existing copy of data\n",
|
|
" base:str=None # Directory containing config file and base of relative paths\n",
|
|
") -> Path: # Path to extracted file(s)\n",
|
|
" \"Download `url` using `FastDownload.get`\"\n",
|
|
" cfg = None\n",
|
|
" if base is None:\n",
|
|
" cfg = fastai_cfg()\n",
|
|
" # A base must be provided as FastDownload initializes a Path with it even\n",
|
|
" # though the config provided is ultimately used instead.\n",
|
|
" base = '~/.fastai'\n",
|
|
" d = FastDownload(cfg, module=fastai.data, archive=archive, data=data, base=base)\n",
|
|
" return d.get(url, force=force_download, extract_key=c_key)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8093527a",
|
|
"metadata": {},
|
|
"source": [
|
|
"`untar_data` is a thin wrapper for `FastDownload.get`. It downloads and extracts `url`, by default to subdirectories of `~/.fastai` (see `fastai_cfg` for details), and returns the path to the extracted data. Setting the `force_download` flag to 'True' will overwrite any existing copy of the data already present. For an explanation of the `c_key` parameter, see `URLs`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "104f7f96",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Path('/home/jhoward/.fastai/data/mnist_sample')"
|
|
]
|
|
},
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"untar_data(URLs.MNIST_SAMPLE)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2a1d58c8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#| hide\n",
|
|
"#Check all URLs are in the download_checks.py file and match for downloaded archives\n",
|
|
"# from fastdownload import read_checks\n",
|
|
"# fd = FastDownload(fastai_cfg(), module=fastai.data)\n",
|
|
"# _whitelist = \"MDL LOCAL_PATH URL WT103_BWD WT103_FWD GOOGLE\".split()\n",
|
|
"# checks = read_checks(fd.module)\n",
|
|
"\n",
|
|
"# for d in dir(URLs): \n",
|
|
"# if d.upper() == d and not d.startswith(\"S3\") and not d in _whitelist: \n",
|
|
"# url = getattr(URLs, d)\n",
|
|
"# assert url in checks,f\"\"\"{d} is not in the check file for all URLs.\n",
|
|
"# To fix this, you need to run the following code in this notebook before making a PR (there is a commented cell for this below):\n",
|
|
"# url = URLs.{d}\n",
|
|
"# fd.get(url, force=True)\n",
|
|
"# fd.update(url)\n",
|
|
"# \"\"\"\n",
|
|
"# f = fd.download(url)\n",
|
|
"# assert fd.check(url, f),f\"\"\"The log we have for {d} in checks does not match the actual archive.\n",
|
|
"# To fix this, you need to run the following code in this notebook before making a PR (there is a commented cell for this below):\n",
|
|
"# url = URLs.{d}\n",
|
|
"# _add_check(url, URLs.path(url))\n",
|
|
"# \"\"\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c23260c6",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Export -"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "da388dfc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#| hide\n",
|
|
"from nbdev import nbdev_export\n",
|
|
"nbdev_export()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e4e67669",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"jupytext": {
|
|
"split_at_heading": true
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "python3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|