{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This module has the necessary functions to be able to download several useful datasets that we might be interested in using in our models."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"from fastai.gen_doc.nbdoc import *\n",
"from fastai.datasets import * \n",
"from fastai.datasets import Config\n",
"from pathlib import Path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> URLs()"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(URLs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This contains all the datasets' and models' URLs, and some classmethods to help use them - you don't create objects of this class. The supported datasets are (with their calling name): `S3_NLP`, `S3_COCO`, `MNIST_SAMPLE`, `MNIST_TINY`, `IMDB_SAMPLE`, `ADULT_SAMPLE`, `ML_SAMPLE`, `PLANET_SAMPLE`, `CIFAR`, `PETS`, `MNIST`. To get details on the datasets you can see the [fast.ai datasets webpage](http://course.fast.ai/datasets). Datasets with SAMPLE in their name are subsets of the original datasets. In the case of MNIST, we also have a TINY dataset which is even smaller than MNIST_SAMPLE.\n",
"\n",
"Models is now limited to `WT103` but you can expect more in the future!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'http://files.fast.ai/data/examples/mnist_sample'"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"URLs.MNIST_SAMPLE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Downloading Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the rest of the datasets you will need to download them with [`untar_data`](/datasets.html#untar_data) or [`download_data`](/datasets.html#download_data). [`untar_data`](/datasets.html#untar_data) will decompress the data file and download it while [`download_data`](/datasets.html#download_data) will just download and save the compressed file in `.tgz` format. \n",
"\n",
"By default, data will be downloaded to `~/.fastai/data` folder. \n",
"Configure the default `data_path` by editing `~/.fastai/config.yml`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> untar_data(`url`:`str`, `fname`:`PathOrStr`=`None`, `dest`:`PathOrStr`=`None`, `data`=`True`)\n",
"\n",
"Download `url` if it doesn't exist to `fname` and un-tgz to folder `dest` "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(untar_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PosixPath('/home/jhoward/.fastai/data/planet_sample')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"untar_data(URLs.PLANET_SAMPLE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> download_data(`url`:`str`, `fname`:`PathOrStr`=`None`, `data`:`bool`=`True`)\n",
"\n",
"Download `url` to destination `fname` "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(download_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: If the data file already exists in a data directory inside the notebook, that data file will be used instead of ~/.fasta/data. Paths are resolved by calling the function [`datapath4file`](/datasets.html#datapath4file) - which checks if data exists locally (`data/`) first, before downloading to `~/.fastai/data` home directory.\n",
"\n",
"Example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PosixPath('/home/jhoward/.fastai/data/planet_sample.tgz')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"download_data(URLs.PLANET_SAMPLE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> datapath4file(`filename`)\n",
"\n",
"Returns URLs.DATA path if file exists. Otherwise returns config path "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(datapath4file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All the downloading functions use this to decide where to put the tgz and expanded folder. If `filename` already exists in a data directory in the same place as the calling notebook/script, that is used as the parent directly, otherwise `~/.fastai/config.yml` is read to see what path to use, which defaults to ~/.fastai/data is used. To override this default, simply modify the value in your `~/.fastai/config.yml`:\n",
"\n",
" data_path: ~/.fastai/data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> Config()\n",
"\n",
"Creates a default config file at `~/.fastai/config.yml` "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(Config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You probably won't need to use this yourself - it's used by `URLs.datapath4file`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> get_path(`path`)"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(Config.get_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the key corresponding to `path` in the [`Config`](/datasets.html#Config)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> data_path()"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(Config.data_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the `Path` where the data is stored."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> model_path()"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(Config.model_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the `Path` where the pretrained models from fastai are stored."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Undocumented Methods - Methods moved below this line will intentionally be hidden"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> create(`fpath`)"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(Config.create)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> url2name(`url`)"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(url2name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> get_key(`key`)"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(Config.get_key)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> get(`fpath`=`None`, `create_missing`=`True`)"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(Config.get)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## New Methods - Please document or move to the undocumented section"
]
}
],
"metadata": {
"jekyll": {
"keywords": "fastai"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}