{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Computer vision data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hide_input": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from fastai.gen_doc.nbdoc import *\n", "from fastai import * \n", "from fastai.vision import * " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This module contains the classes that define datasets handling [`Image`](/vision.image.html#Image) objects and their tranformations. As usual, we'll start with a quick overview, before we get in to the detailed API docs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quickly get your data ready for training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get you started as easily as possible, the fastai provides two helper functions to create a [`DataBunch`](/basic_data.html#DataBunch) object that you can directly use for training a classifier. To demonstrate them you'll first need to download and untar the file by executing the following cell. This will create a data folder containing an MNIST subset in `data/mnist_sample`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PosixPath('/home/jhoward/.fastai/data/mnist_sample')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_SAMPLE); path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a number of ways to create an [`ImageDataBunch`](/vision.data.html#ImageDataBunch). One common approach is to use *Imagenet-style folders* (see a ways down the page below for details) with [`ImageDataBunch.from_folder`](/vision.data.html#ImageDataBunch.from_folder):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "tfms = get_transforms(do_flip=False)\n", "data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=24)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the datasets will be automatically created in the structure of *Imagenet-style folders*. The parameters specified:\n", "- the transforms to apply to the images in `ds_tfms` (here with `do_flip`=False because we don't want to flip numbers),\n", "- the target `size` of our pictures (here 24).\n", "\n", "As with all [`DataBunch`](/basic_data.html#DataBunch) usage, a `train_dl` and a `valid_dl` are created that are of the type PyTorch [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader). \n", "\n", "If you want to have a look at a few images inside a batch, you can use [`ImageDataBunch.show_batch`](/vision.data.html#ImageDataBunch.show_batch). The `rows` argument is the number of rows and columns to display." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(rows=3, figsize=(5,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second way to define the data for a classifier requires a structure like this:\n", "```\n", "path\\\n", " train\\\n", " test\\\n", " labels.csv\n", "```\n", "where the labels.csv file defines the label(s) of each image in the training set. This is the format you will need to use when each image can have multiple labels. It also works with single labels:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namelabel
0train/3/7463.png0
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\n", "
" ], "text/plain": [ " name label\n", "0 train/3/7463.png 0\n", "1 train/3/21102.png 0\n", "2 train/3/31559.png 0\n", "3 train/3/46882.png 0\n", "4 train/3/26209.png 0" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(path/'labels.csv').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can then use [`ImageDataBunch.from_csv`](/vision.data.html#ImageDataBunch.from_csv):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_csv(path, ds_tfms=tfms, size=28)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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VSuUhYDLw/8q9MmmoWnw+W2VHKtuV/sAHlUplfojNAQaXdD3SCqkjlVrXCXj7E7HVQOcSrkVaKXWkUuvWAF0+EesCvFPCtUgrpY5Uat18oG1KqV+IDQO00CTNplV2pCmltiml9kAboE1KqX1KqbVmMNS0SqWyFpgE/F9KqWNK6XDgNOCf5V6ZNFQtPp+tsiMFrgDWAz8Exn3cvqLUK5Ic32BjmswKYCJwkVKfalrNPZ+pUqmUfQ0iIjWttY5IRUQajTpSEZFM6khFRDKpIxURydSsKQUpJa1sbUWlUkllX0N96X5une7n9mVz91MjUhGRTOpIRUQyqSMVEcmkjlREJJM6UhGRTOpIRUQyqSMVEcmkjlREJFOLrvHXGFLamD+rKle1ZYcdqr/j27dv7+1u3boBMHz4cI996lOfAmCvvfbyWPfu3b3dsWNHAFasWOGxiRMnenvOnDkArFmzplGuXVofjUhFRDKpIxURybTdT+379OlT+BNgt912A2DdunUe22mnnbzdpk0bADZs2OCx5cuXe3vx4sWbxKRx2T2C4jT+0EMPBeBrX/uax/bZZx+g+Prmvffe87bdz3i/LQawdu1aAJ588snGuHRpALuvAC+99BIAr776qsc++uijZr+mbaERqYhIJnWkIiKZampqbyvwUV2r8baKC3DSSScBxamDrfyuX7/eYx06dPB2jx49ANh///09tuuuu3r7nXc2Hpl+7bXXeuzqq68GYOHChfX4l7QObdtu/PFq166dx+L0ui4HHnggAOPHj/fYWWed5e1ddtkFgJ133tlj77//PgCLFi3yWGzvscceAAwdOtRjgwYN8vbee+8NaGpfBnvurrzySo/deeedAEyYMMFjb731lrdtmr+1TJz4MzJy5EigeI8bM0tDI1IRkUwtakRqI5j4Yjm2LZ8wLj4cdthhQDGHsEuXLt7u1asXAAMHDvRYz549geII98MPP/R2Xb/pYqxz584A7Lfffh6zUaxGpFV2v7p27eoxW0jYHFskslH/Jz3wwAMAvPLKKx57/vnnC38XYwCnnHIKAH/60588Fu/9jjvuuMVrkqYzYsQIoDiLvPzyy4HiguNdd93lbZvhDBgwYJMYVPsRm70AjBo1CiiOfJ966ilv5y5maUQqIpJJHamISKYWNbW3aXj//v091rt3b2/vueeeQHFK3a9fP6A6XYfikN5eONtwH6rTR8sf/CSb5j/77LMee+2117w9ePBgAPr27btJLL7MjotZrZG9zN+Wl/o29Z80aZLHYj7vzJkzgeL9WLVqFQBvvvmmx+Li4bhx44DiVtN4TW+//Xa9r0/yxUWgX/7yl0DxmbbtwXHB8cgjj9zk61h/AMX7bc/6u+++6zHbMhxf8cWpfS6NSEVEMpU2IrVR41FHHeWxgw8+GCimpsS0I0tbsoUdgN133x0oLgbFFBtbtIiLCzZymTx5sseeeeYZby9ZsgQo7o6JX3/06NEAnHrqqR6z35izZ8/22BNPPIFsGxvFWyGRT7brYruULI0JYOzYsd62RYl4P+Oi1wsvvJBxxVIfcUZos0ioLgJF8+bNA2Dp0qUei8+vFaGJs4rYXr16NQAvvviixyz90VLloHELGWlEKiKSSR2piEim0qb2llto03mA4447DqjuQoBiXUpbdIhTNJumx4+L0zabsj/99NMes6lDzDWMU4OVK1du8n3qcsABB3jbcls/85nPeExT+6YT7/e+++4LwFe+8hWPjRkzxtu2KDF9+nSP3Xrrrd6OC1fSNGw6DvCFL3zB25b/ec8993js3//+N1B8PuOU3PJ+4yJkzCu2PiEuWh9zzDEATJ061WOa2ouItCDqSEVEMpW+ah9zMS0XNE7bIssXjAUpbPhv03UorsDbMD9uObSv88YbbzTo2i2/dMGCBR6zKb3lwkIxbzHmtEm+uA34+OOPB+DrX/+6x6xQCVR/Nm655RaPPfbYY96O00ZpGnG75xe/+EVvW03f7373ux6z+xW3bcY+wdpxJT9+rOWBx/tqrwGa6jgZjUhFRDKVNiK10VosGGG/aeLoLY4a//73vwPF3UO2MGSjTCjmn22tbFtDWEmveG32Gy/usIqj0/jiXBrOfm6sPCLAT3/6U6BYuCaOVmbNmgXA3LlzPRYXKqTpxd1MccffNddcA8D8+fM9VlcBkc0VMtqSeOqF7WJsqkMwNSIVEcmkjlREJFNpU3ubise8LtvaFfMzY2ECq1kY8/4sf6yMbX5xmmCvJeJLddu+Cpra54hbgm1Kf+6559b59ybeG9uGHF8ZxcWLhx9+GGj5B6zVsrjI849//MPb//vf/4DGXfCzexuLF22uQFGjfc8m/eoiIq1AaSPS119/HSiOSG2k+fLLL3vMzvCBatGSmDK1bNkyoPzzdmwEFBc56jpjSrbMFh9j9fPPfvaz3v785z8PwLBhwzxm/88xBS4uNNguuph2Y2XVoPrzFhcplRLVuOJi8O233+7tpthVZiX14sxWi00iIi2cOlIRkUylTe0tFzMes2qFRe6++26PxSmY5WjGg+rilKEl0NQ+j+XeXnzxxR6z6TxUfwbiTrU77rgDKB6QZguXUD0g8eyzz/bYiSee6G0rdnLjjTd6LNaylHxxsaepF37sGPWDDjrIY1aw5oMPPmiS76kRqYhIJnWkIiKZWtThdyZu67RVeahuyYwrb1urGdoUrCBGzHG1/FFN7evP8v2snijAb37zG6BamxaKxV/sZyAe6XLVVVcBxcPM4s+F5RjHvN4zzzzT24cccghQzScFTe1rTXzW7CC9mCv80EMPAU2XK6wRqYhIphY5Io0vhGO7pRxv3KdPH6A4kurcuTNQdzkvqYqjBNt5ctlll3nMFgjirCOeNPCXv/wFgH/9618e29oCgu0qswIZACeccIK3hw4dClRPOYBifrO0fHYAIlQXLONJGTHHuCloRCoikkkdqYhIphY5tW/phgwZAkDv3r09ZlPRmL9o22ClbrYdtFOnTh5bsmQJUCzyMnHiRG/PnDkT2LbXJlYwY9q0aR5bsWKFt+2c9YEDB9b7a0rLEuud2qu3eFJGU+eba0QqIpJJHamISCZN7esprjbb1N6mhFA9ZM+mplA8PkE2ilkNtj3YzjGH6pbgWF92xowZ3q5rSm95pjHfNH6cbSeNuYZxa7K9YohHlUhtsW2hAB07dgSKq/ZNVfXJaEQqIpJJI9J6ii+z7SjguEhiL7ZVCb/+bPeRFR3ZFjYrgOq9iXVs61roi3m/8fA7G61Y3VKpDTbyhOqR3FDNGW3OZ1EjUhGRTOpIRUQyaWpfT6eddpq37RC+yAqtxDxSaXxWkCJO7S1HMC7u1bVtdMSIEd6Or2VsYapdu3aNe7HSpOxnAeCII47w9m9/+1ugefO4NSIVEcmkEWk9xUP4LE0mjj5tNBRLuUnjO//884HiwpId6RtTmiI7CO/Tn/60x2IJRCvNF3c+WSpVPMJZWhYrfwjVBWCoHtHenAcYakQqIpJJHamISCZN7bcg7paIC0xWaf3JJ5/0mB2u1RTndLd2Y8aM8bbVnYxn0K9cuXKTz4n1KW1KHw9Ds1qoAIsWLQJg8uTJHmuqSuqSzxYF4wLw8uXLvR0PRmwuGpGKiGRSRyoikklT+y0YNGiQt2NBC9uaFguU2Ha0Mg7jq3WxmEi3bt0A+MEPfuCxQw891NuPPPIIAAsWLNji14xT9+985ztAcdXeisxAtcbp3LlzPdZU559Lw8QcX9sOGnOJv/3tb3vb6s82J41IRUQyaUS6BT169PB2LNFm+Wmx8IXyDRuuQ4cO3rbcwPHjx3ssLiw999xzQHGBySqiDxs2zGNnnXWWtw8//HCgeI/uvvtub1977bVAcZFCi00tS9yJZkdpv/rqqx6zxV4oZ1aoEamISCZ1pCIimTS1/1jMO7Q80eOOO85jcUuhTSsXL17ssa0tfsjmxWnbhRdeCFSr1gO8+OKL3rZXLBdccIHHBgwYUPgTqtN9qG4dve222zz217/+1dt2HzWdb1niMxlryQ4dOhSAG2+80WO2zReavhp+XTQiFRHJpBHpx+Ko6JhjjgFg8ODBHotFEewMoVi2rTkLJGxv4lG5EyZMAIojypEjR3rb7kn8e1v0i0csx1Sme++9FyiOSON5PhqJtkw2M4TqAlM0adIkb5cxCo00IhURyaSOVEQkUyp7SCwiUus0IhURyaSOVEQkkzpSEZFM6khFRDKpIxURyaSOVEQkkzpSEZFM6khFRDKpIxURyaSOVEQkkzpSEZFM6khFRDKpIxURyaSOVEQkkzpSEZFM6khFRDKpIxURyaSOVEQkkzpSEZFM6khFRDKpIxURyaSOVEQkkzpSEZFM6khFRDKpIxURyaSOVEQkkzpSEZFM6khFRDKpIxURydQqO9KUUteU0i0ppbUppSUppS+XfU3ScCmli1NKj6eUNqSUri/7eiRPLT6fbcu+gJL8GXgP6AkMB+5IKc2pVCrPlHtZ0kDLgJ8Do4EOJV+L5Ku55zNVKpWyr6FZpZQ6Am8CQyqVyvyPY/8EllYqlR+WenGSJaX0c2CfSqXytbKvRRqmVp/P1ji17w98YDfpY3OAwSVdj4hU1eTz2Ro70k7A25+IrQY6l3AtIlJUk89na+xI1wBdPhHrArxTwrWISFFNPp+tsSOdD7RNKfULsWFAi32RLdKK1OTz2eo60kqlshaYBPxfSqljSulw4DTgn+VemTRUSqltSqk90AZok1Jqn1JqrRkpNa1Wn89W15F+7BtsTJNZAUwELmrJqRWyVVcA64EfAuM+bl9R6hVJjpp7Pltd+pOISGNrrSNSEZFGo45URCSTOlIRkUzqSEVEMjVrikhKSStbW1GpVFLZ11Bfup9bp/u5fdnc/dSIVEQkkzpSEZFM6khFRDKpIxURyVST+5FTqr7v3X///QEYMGCAx/bdd19v77333oU/Adq23fjP3nHHHT328ssve3vGjBkAzJw502PLli1rjEsXke2QRqQiIpnUkYqIZKqpqb1N6XfZZRePnXvuuQAcdNBBHovT+J49ewLQrVs3j9VVqGXFihXePuKIIwC49dZbPfa73/3O2x9++GHD/gFSp3g/TzvtNABGjhzpsdWrV3v72WefBaBPnz4es9c7r7/+uscWLlzo7aeeegqAefPmeWzdunWNcemSyZ7pNm3aeOyDDz4o63IaTCNSEZFMNTUi3WGHjf1+587V41uOPfZYAIYOHeqxnXfe2dvvv/8+AG+/XT0GxkaknTp18lj37t03aduiFBRHOPfccw9QHNWoHGH9xIVCG4meccYZHhs7diwAI0aM8Ng771RPmbD7sN9++3msV69eAKxcudJjL774ordnzZoFwL333uux//3vf95eu3ZtA/4lsi3isxYXee0Z+9SnPuWxz3zmM0BxZrlhwwZvv/nmmwC89NJLHrN7DLBkyRKgeZ9JjUhFRDKpIxURyVRTU/uPPvoIKE71bEFo+fLlHotTf/vYN954w2M25N9zzz09ZtNDgH322Qco5qZedNFF3n7uuecAWLx4scfi1EM2Ly4q2NTtrLPO8phN6+JUcNddd/V2zBE29rrAFhY/2e7Xb+M5anvttZfHbHoI8NBDDwHVny9pPF26bDwQ9OCDD/ZY//79vX3IIYcAxVc5NvXfaaedPBYXoOxnKL7KmTRpkrd/9atfbfI5TU0jUhGRTOpIRUQy1dTU3qbkb731lscsv/Pqq6/2WJw+Ws5nzP2sayp4yimnePvss88G4PDDD/dYnHoMHz4cKE4tNLWvn3hvLLfXpn8A7733HlB8FRPZ9Pvdd9/1WMeOHYFqVgcUp4W77bYbAIceeqjHYhbHk08+CRRfGSkLY9u1a9cOgB49enjMpvQ/+MEPPLb77rt7217D2ecCvPbaa0Ax8yKyV0JDhgzZ5OtANVPn17/+9bb/IxpII1IRkUw1NSKti71QXrNmzTZ/blwsuuWWW7xtO2XiiLR9+/betgWRmLsWR6eyeXFBx0ad06dP95jlicZ80zg6tFHj/PnzPWa7oOKoZtCgQd62xaauXbt6LC5+2AzjiSee8JhyS+sn5mz37dsXKOYFf+tb3wKgQ4cOHov3c9GiRQA8/fTTHrNvxeAWAAAKCUlEQVRiQffdd5/H5s6d6+3DDjsMqC4qxe8NxXvfXDQiFRHJpI5URCRTzU/tG0vMW7RpSJxexrYtVKh4ybazhQCA559/HoBf/OIXHrMtg5v7v7dpYfw6N910E1BcbDrhhBO8/ZWvfAWobieG4qsaa8fvI5uy/5/4imTYsGHetu29xx9/vMdsITDm7T766KPenjZtGlDddg3VaX5cmLQFQ6gWKorbhOO9jwuRzUUjUhGRTK1yRGq/WWOaxvjx47198sknA8WFkZje9NhjjwFaYMplo8qYztZYYkGLVatWAZsf5Ur92A4zSw8EuOSSS7xt6YRxoe7BBx8E4JFHHvHYf/7zH2/bouH69es3+X6DBw/29rhx47z9uc99DiimzcVRrj2fzUkjUhGRTOpIRUQybZdTe3vBDdXdM/EFuS0unHjiiR777Gc/620rbhGnnHFqsmDBAqBhuavSPGLBiroWBWMuo3Yx1Y/Vj407Aq3AD1RPmYgLR1OmTAFg6tSpHou7yup6xWI7n+y0BIAvfelL3raFYXsOobiz8b///W99/jmNSiNSEZFM6khFRDLV/NTepgbHHHOMx2IxA6szGgsl2FbCWIgkHmtg9RCXLl3qsWuuucbbVlRBeaQtV8xBtLYVRIFiURR7haP7uWVW89em61DMx7XsiNtvv91j8cBBE48asVdv8fm1HGDbCgrFPG/bQnrDDTd4bPLkyd4u45WbRqQiIplqckRaVym2Cy64wGN2nDJUD7KLvwXru7gQRyjxBbl9/7ibQtXVyxcr6VvhGagujsRR6Jw5c7xt5driiFU2ZbmesVjPCy+84G0bCcZdZybmfMZF3jFjxgDFw+9sFhm/jp1iADBhwgSgeFx62c+fRqQiIpnUkYqIZKrJqX2shm01Q61GKMAee+zhbZt+x+m8TRnidCBO/W3qHg/E+9GPfuTtK6+8EijWUKxri5s0r1hjNB6oZ/FnnnnGY/Fc+9dff70Zrm77Eafc8XWJLfzG3FBb5L300ks9dtJJJ3nbasVajmpkJxdAcVupLSyVPZ2PNCIVEclUkyPSOOI855xzgOqiEhQXgey3Yyw6YulLsbhCPJrZFi1i6a7Ro0d726rpx/So2JZyfOMb3/D2kUce6W1Ld1u9erXHYoV9abg4+rQdgXFHklXLj0cwx2JB8WwtY7PH3r17b/J1oLqDKi5AlVE6L9KIVEQkkzpSEZFMNTm1j9N0q7I+dOhQj82ePdvbtguirty3OB0YNWqUt20aYXVJobgYdfHFFwPFVwg333wzUN3dIU3LKulDtW5lfL0Tp4xWq/Lf//63x+znRvLEHYO2wGfPB8C+++4LFO9XzNe1XWXxWbKc07ioHE83sFd79sxBcWfT5o5xbkoakYqIZFJHKiKSqSan9rZqB9XtYnaIFlTPPodqjmCcctt2z5iHFnMJbcofz0mP29psO9vAgQM9ZtsQNbVvHvE89dNPPx0oZnPEWrL2eieu8urc+sYRt07bK7f4yswOvYv/3/FZe/bZZ4Hiqxb72FioJOYIH3fccUDx+Y1Hy2hqLyJSg0ofkcaXzLZAEHdOxMIhll8Wf7vNnTu38GdDWYmw+LViYYu6jvKNuzFiVX5pXDFX0UadcSHw1FNPBYoLH7HUm1Vsj6MWaRx1HQoZi8fYYtNTTz3lsYkTJ3rbdhHG2Z8957FEX8zTttlhPBwvVuovg0akIiKZ1JGKiGQqfWofp8e2jWzhwoUei4sG8UCzpmSvE+K0pa4CCbEuqs5Jb1xxqte3b19v27Qunq1+4IEHAsXamLFKux1cqMIyTeuJJ54o/BnFZ6W+JxHEBcX4Os/i8WckLkzZwm98XdfUNCIVEcmkjlREJFNpU3tbkbNpGVSPHbj22ms9FqdjTTm1j1MPW3W0FUcoTt1tmh9rnLak2ojbg3g0hR2GBvC9730PKJ6tblsOY83Kxx9/3Nsx11HKsS0HC9qzeMABB3jM6g7X9XFQ3IIaV/ubi0akIiKZShuR2m+Y8847b5NYXGx69dVXvd2Uu1EGDBjgbctLPPPMMz0Wi2BYTlusuJ6bxyob2cgiHo8dF5tsJBoLXyxYsAAoLjDFhSfNFlq+OKK0OqTjx4/3WKxxajunFi1a5LGpU6d6O1btby4akYqIZFJHKiKSqbSpvdWOtOMJoHpG/dixYz0Wz7u2bWaLFy/2mOWKvfLKKx6L076uXbtu8nWs2Ej83nYIF1S3nsXctDg9vO2224BioZSyjzrYXlgucZzWWVESqOb2Pvfccx777ne/CxSPD9mWxQ1pXnFbuC3sxmNFLrzwQqB4vE8sSGP3+cYbb/TYjBkzvN1c+eaRRqQiIplKG5HaYkBcsLEjlesqTwcwcuRIoHp4HVTLdMXSenFEalW24+LF/vvvDxQP4YoH3XXo0AGAdevWeSz+xrvrrrsAmDdv3pb+ibIVllI2aNAgj1100UVA8cjeWIzESqTFavdWXEal8VquWMgkji5tgfmb3/ymx2xxMaY33Xfffd625++OO+7wWBmj0EgjUhGRTOpIRUQylTa1t8rYccFmxIgRQPEgu3jevC0OxR1FW1NXMZG6Pj/GbEpv+YkA1113nbdt10w8J122neUOxhzBo446Cii+iokLiXfffTdQPOzMdi5ty8+FNFysAxqn6faaLS4G2zQ9LubaIXlQfZ0Xd7KtWbMGqB5aCPC3v/3N2/aazb5fS6ARqYhIJnWkIiKZSpvaWzESO54A4IYbbgDg/PPP91ic4tnKXzxjPkfMDY3Hm1ie2k033eSxO++809uxRqo0nN3HuGpvmRRxFdYOr4NqYZKYMyrNa/jw4d4+5phjvG3PdHwlZtP4+Dkxj9TaL7/8ssfuv/9+oJgnGvuJlpizrRGpiEim0ivkx99Elhs4ffp0j8XyWbbbZdSoUVnf00aiTz/9tMdsEQOqvxFjIRJ7AS55YlVzWzyM+bz29zEveNasWd5++OGHm/oSZSviiDCWwbR2XSPOuBAYC8rYbCM+i3aiQczdbuk0IhURyaSOVEQkU+lT+1hcwvIy49QhLuxYoYq4ZdC2kcXpRNxaVtffW27pqlWrPBZrG9r558oTbXxxam8nEMR7YzmI8Rz0WKBEymPPjW3RhOphhFAtDBQr1Nu9mzJlisdinumDDz4IVJ85KC781gqNSEVEMqXm3A2SUtLWk62oVCo1c66z7ufWbe/3M5aatFMmevXq5TErezh79myPLVu2rMHXWLbN3U+NSEVEMqkjFRHJ1KxTexGR7ZFGpCIimdSRiohkUkcqIpJJHamISCZ1pCIimdSRiohkUkcqIpJJHamISCZ1pCIimdSRiohkUkcqIpJJHamISCZ1pCIimdSRiohkUkcqIpJJHamISCZ1pCIimdSRiohkUkcqIpJJHamISCZ1pCIimdSRiohkUkcqIpLp/wOl0OmctnTM/wAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(rows=3, figsize=(5,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An example of multiclassification can be downloaded with the following cell. It's a sample of the [planet dataset](https://www.google.com/search?q=kaggle+planet&rlz=1C1CHBF_enFR786FR786&oq=kaggle+planet&aqs=chrome..69i57j0.1563j0j7&sourceid=chrome&ie=UTF-8)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "planet = untar_data(URLs.PLANET_SAMPLE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we open the labels files, we seach that each image has one or more tags, separated by a space." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
image_nametags
0train_21983partly_cloudy primary
1train_9516clear cultivation primary water
2train_12664haze primary
3train_36960clear primary
4train_5302haze primary road
\n", "
" ], "text/plain": [ " image_name tags\n", "0 train_21983 partly_cloudy primary\n", "1 train_9516 clear cultivation primary water\n", "2 train_12664 haze primary\n", "3 train_36960 clear primary\n", "4 train_5302 haze primary road" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df =pd.read_csv(planet/'labels.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_csv(planet, folder='train', size=128, suffix='.jpg',\n", " ds_tfms=get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `show_batch`method will then print all the labels that correspond to each image." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(rows=3, figsize=(10,8), ds_type=DatasetType.Valid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can find more ways to build an [`ImageDataBunch`](/vision.data.html#ImageDataBunch) without the factory methods in [`data_block`](/data_block.html#data_block)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ImageDataBunch[source]

\n", "\n", "> ImageDataBunch(`train_dl`:[`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader), `valid_dl`:[`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader), `test_dl`:`Optional`\\[[`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader)\\]=`None`, `device`:[`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch-device)=`None`, `tfms`:`Optional`\\[`Collection`\\[`Callable`\\]\\]=`None`, `path`:`PathOrStr`=`'.'`, `collate_fn`:`Callable`=`'data_collate'`) :: [`DataBunch`](/basic_data.html#DataBunch)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch, doc_string=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Factory methods" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

create[source]

\n", "\n", "> create(`train_ds`, `valid_ds`, `test_ds`=`None`, `path`:`PathOrStr`=`'.'`, `bs`:`int`=`64`, `ds_tfms`:`Union`\\[[`Transform`](/vision.image.html#Transform), `Collection`\\[[`Transform`](/vision.image.html#Transform)\\], `NoneType`\\]=`None`, `num_workers`:`int`=`16`, `tfms`:`Optional`\\[`Collection`\\[`Callable`\\]\\]=`None`, `device`:[`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch-device)=`None`, `collate_fn`:`Callable`=`'data_collate'`, `size`:`int`=`None`, `kwargs`) → `ImageDataBunch`\n", "\n", "Factory method. `bs` batch size, `ds_tfms` for [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset), `tfms` for [`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader). \n", "\n", "- *bs*: Desired batchsize for the dataloaders\n", "- *num_workers*: The number of process to launch for data collection\n", "- *ds_tfms*: Tuple of two lists of transforms (first for training and second for validation and test set)\n", "- *size*: Target size for those transforms\n", "- *tfms*: List of transforms to be applied at a batch level (like normalization)\n", "- *device*: The device on which to put the batches " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.create, arg_comments={\n", " 'bs': 'Desired batchsize for the dataloaders',\n", " 'num_workers': 'The number of process to launch for data collection',\n", " 'ds_tfms': 'Tuple of two lists of transforms (first for training and second for validation and test set)',\n", " 'size': 'Target size for those transforms',\n", " 'tfms': 'List of transforms to be applied at a batch level (like normalization)',\n", " 'device': 'The device on which to put the batches'\n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You don't often need to call this directly yourself; instead, you'll normally use one of the convenience wrappers below. However, these wrappers all accept a `kwargs` that is passed to this method, so you can pass any of the above parameters as well.\n", "\n", "If you quickly want to get a [`ImageDataBunch`](/vision.data.html#ImageDataBunch) and train a model, you should process your data to have it in one of the formats the following functions handle. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_folder[source]

\n", "\n", "> from_folder(`path`:`PathOrStr`, `train`:`PathOrStr`=`'train'`, `valid`:`PathOrStr`=`'valid'`, `test`:`Union`\\[`Path`, `str`, `NoneType`\\]=`None`, `valid_pct`=`None`, `kwargs`:`Any`) → `ImageDataBunch`\n", "\n", "Create from imagenet style dataset in `path` with `train`,`valid`,`test` subfolders (or provide `valid_pct`). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.from_folder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"*Imagenet-style*\" datasets look something like this (note that the test folder is optional):\n", "\n", "```\n", "path\\\n", " train\\\n", " clas1\\\n", " clas2\\\n", " ...\n", " valid\\\n", " clas1\\\n", " clas2\\\n", " ...\n", " test\\\n", "```\n", "\n", "For example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=24)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this (and all factory methods in this section) pass any `kwargs` to [`ImageDataBunch.create`](/vision.data.html#ImageDataBunch.create)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_csv[source]

\n", "\n", "> from_csv(`path`:`PathOrStr`, `folder`:`PathOrStr`=`'.'`, `sep`=`None`, `csv_labels`:`PathOrStr`=`'labels.csv'`, `valid_pct`:`float`=`0.2`, `fn_col`:`int`=`0`, `label_col`:`int`=`1`, `test`:`Union`\\[`Path`, `str`, `NoneType`\\]=`None`, `suffix`:`str`=`None`, `header`:`Union`\\[`int`, `str`, `NoneType`\\]=`'infer'`, `kwargs`:`Any`) → `ImageDataBunch`\n", "\n", "Create from a csv file. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.from_csv)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create [`ImageDataBunch`](/vision.data.html#ImageDataBunch) from `path` by splitting the data in `folder` and labelled in a file `csv_labels` between a training and validation set. Use `valid_pct` to indicate the percentage of the total images for the validation set. An optional `test` folder contains unlabelled data and `suffix` contains an optional suffix to add to the filenames in `csv_labels` (such as '.jpg'). \n", "For example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_csv(path, ds_tfms=tfms, size=24);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_df[source]

\n", "\n", "> from_df(`path`:`PathOrStr`, `df`:`DataFrame`, `folder`:`PathOrStr`=`'.'`, `sep`=`None`, `valid_pct`:`float`=`0.2`, `fn_col`:`int`=`0`, `label_col`:`int`=`1`, `test`:`Union`\\[`Path`, `str`, `NoneType`\\]=`None`, `suffix`:`str`=`None`, `kwargs`:`Any`) → `ImageDataBunch`\n", "\n", "Create from a DataFrame. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.from_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Same as [`ImageDataBunch.from_csv`](/vision.data.html#ImageDataBunch.from_csv), but passing in a `DataFrame` instead of a csv file. E.gL" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namelabel
0train/3/7463.png0
1train/3/21102.png0
2train/3/31559.png0
3train/3/46882.png0
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\n", "
" ], "text/plain": [ " name label\n", "0 train/3/7463.png 0\n", "1 train/3/21102.png 0\n", "2 train/3/31559.png 0\n", "3 train/3/46882.png 0\n", "4 train/3/26209.png 0" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(path/'labels.csv', header='infer')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_df(path, df, ds_tfms=tfms, size=24)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Different datasets are labeled in many different ways. The following methods can help extract the labels from the dataset in a wide variety of situations. The way they are built in fastai is constructive: there are methods which do a lot for you but apply in specific circumstances and there are methods which do less for you but give you more flexibility.\n", "\n", "In this case the hierachy is:\n", "\n", "1. [`ImageDataBunch.from_name_re`](/vision.data.html#ImageDataBunch.from_name_re): Gets the labels from the filenames using a regular expression\n", "2. [`ImageDataBunch.from_name_func`](/vision.data.html#ImageDataBunch.from_name_func): Gets the labels from the filenames using any function\n", "3. [`ImageDataBunch.from_lists`](/vision.data.html#ImageDataBunch.from_lists): Labels need to be provided as an input in a list" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_name_re[source]

\n", "\n", "> from_name_re(`path`:`PathOrStr`, `fnames`:`FilePathList`, `pat`:`str`, `valid_pct`:`float`=`0.2`, `test`:`str`=`None`, `kwargs`)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.from_name_re)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creates an [`ImageDataBunch`](/vision.data.html#ImageDataBunch) from `fnames`, calling a regular expression (containing one *re group*) on the file names to get the labels, putting aside `valid_pct` for the validation. In the same way as [`ImageDataBunch.from_csv`](/vision.data.html#ImageDataBunch.from_csv), an optional `test` folder contains unlabelled data.\n", "\n", "Our previously created dataframe contains the labels in the filenames so we can leverage it to test this new method. [`ImageDataBunch.from_name_re`](/vision.data.html#ImageDataBunch.from_name_re) needs the exact path of each file so we will append the data path to each filename before creating our [`ImageDataBunch`](/vision.data.html#ImageDataBunch) object." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_sample/train/3/7463.png'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/train/3/21102.png')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fn_paths = [path/name for name in df['name']]; fn_paths[:2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pat = r\"/(\\d)/\\d+\\.png$\"\n", "data = ImageDataBunch.from_name_re(path, fn_paths, pat=pat, ds_tfms=tfms, size=24)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['3', '7']" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.classes" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_name_func[source]

\n", "\n", "> from_name_func(`path`:`PathOrStr`, `fnames`:`FilePathList`, `label_func`:`Callable`, `valid_pct`:`float`=`0.2`, `test`:`str`=`None`, `kwargs`)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.from_name_func)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Works in the same way as [`ImageDataBunch.from_name_re`](/vision.data.html#ImageDataBunch.from_name_re), but instead of a regular expression it expects a function that will determine how to extract the labels from the filenames. (Note that `from_name_re` uses this function in its implementation).\n", "\n", "To test it we could build a function with our previous regex. Let's try another, similar approach to show that the labels can be obtained in a different way." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['3', '7']" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_labels(file_path): return '3' if '/3/' in str(file_path) else '7'\n", "data = ImageDataBunch.from_name_func(path, fn_paths, label_func=get_labels, ds_tfms=tfms, size=24)\n", "data.classes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_lists[source]

\n", "\n", "> from_lists(`path`:`PathOrStr`, `fnames`:`FilePathList`, `labels`:`StrList`, `valid_pct`:`float`=`0.2`, `test`:`str`=`None`, `kwargs`)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.from_lists)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The most flexible factory function; pass in a list of `labels` that correspond to each of the filenames in `fnames`.\n", "\n", "To show an example we have to build the labels list outside our [`ImageDataBunch`](/vision.data.html#ImageDataBunch) object and give it as an argument when we call `from_lists`. Let's use our previously created function to create our labels list." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['3', '7']" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels_ls = list(map(get_labels, fn_paths))\n", "data = ImageDataBunch.from_lists(path, fn_paths, labels=labels_ls, ds_tfms=tfms, size=24)\n", "data.classes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Methods" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

show_batch[source]

\n", "\n", "> show_batch(`rows`:`int`=`None`, `figsize`:`Tuple`\\[`int`, `int`\\]=`(9, 10)`, `ds_type`:[`DatasetType`](/basic_data.html#DatasetType)=``)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.show_batch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a `rows` by `rows` grid of images from dataset `ds_type` for a `figsize` figure. This function works for all type of computer vision data (see [`data_block`](/data_block.html#data_block) for more examples).\n", "\n", "Once you have your [`ImageDataBunch`](/vision.data.html#ImageDataBunch), you can have a quick look at your data by using this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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AkBe4XpBSHMeb4ziu+G/8//96ZXFI1Y5Ck54HQwh/iLkNRNVwvaBKoih6MYqi8yGET0MIpSGEBVkeUrVi6qyKoijqFkLYFULoHcfx7myPB7mN6wXpiqKodghhbAhhUgjh53EcX87uiKoPdzRVd38IYRkvGqgirhekJY7jq3EcLwshdA4hPJbt8VQnCk3VPRBCeC3bg0De4HrBF1Un0KOpeaIoGhdC6BRCeCPbY0Hu43pBVUVR1DaKojlRFBVFUVQ7iqJpIYSvhhAWZXts1alOtgeQJx4MIfwtjuPybA8EeYHrBVUVh/9Mk/0m/Of/+O8NIfwgjuN3szqqasabAQAArpg6AwC4otAAAFxRaAAArig0AABXGX3XWRRFvPMgy+I4jrI9hqrS66Vhw4bm8/379zd53759Jvfs2TPp17dt29bkUaNGmdy6dWuTmzRpYnLLli1N7tixo8l169YN6tq1ayYfOHDA5MWLF5v84osvmrx582aTKyoqTK7uN/fk8/WCzPu864U7GgCAKwoNAMBVRtfRcGubfUyFfKZWLfv/s3RqTj+vU2nTp083+Vvf+pbJOpUWQuL0W506dvb68mW7j+KRI0dM/u1vf2vy66+/bvKOHTsSnvN6cL18vlTXT6NGjZJ+Porsj/bs2bMJz6Ef06nSXMPUGQAgKyg0AABXFBoAgCt6NDUMc+7Vp0GDBibr2527d++e8D1z5swxefLkySYPGDDA5KtXr5qsPRjt0TzzzDOfP+AvoJCuF+2RdO7c2eQePXqYfPPNN5vcvHlzkydNmpT08YqKikzW3+W2bdtMfvPNNxPGvHz5cpPXrVtn8qlTp0zO9t6V9GgAAFlBoQEAuKLQAABccfAZ8AVdvHjRZF3zcvLkyYTvuXDhgsm1a9c2WbfF0b5Pp06dTO7atWvSXFpaarKu0ykk2hObMGGCyWPGjDF54MCBJus6qXbt2pmsWwppD0Z/l7pORtfdDBo0KGkOIYTycnt23q9//WuTf/Ob35h89OhRk/X3na0eDnc0AABXFBoAgCsKDQDAFT0aoJroOgnNIYSwdetWk3ft2mXywYMHTW7VqpXJ2ifQtTzaw0k1Z19I7r//fpPvuecek7WH07hxY5O1p6I/Kz2GQn9X+vvWIyG0P9KiRYuk4wshhKZNm5r85JNPmqxHX7zwwgsm67qb8+fPJzxHJnBHAwBwRaEBALii0AAAXNWoHk29evVM1vnv9u3bm6xzqPo++BAS53X1vJH69eubrHOkejRvdZ8nguzRayOExGtIrw/9Hp3X13n/srIyk/X8kitXrlRtsAXgK1/5ismDBw82Wdc9bdy40eSSkpKkecOGDSafPn066eNrvnTpksm699rIkSODevTRR03WnsysWbNM1t/3z372M5PXr1+f8ByZwB0NAMAVhQYA4IpCAwBwlVM9Gj0/Xfd90p6K7i2k/Q/d20j3gbrllltMnjhxosk6p/rpp58mjFn3s9I5d/036Xvt9+zZY/L8+fNNfuedd5KOCblD17hozy+ExDNMRo8ebbLur5VqLccnn3ySNNck2jPRfoX+7J577jmTdc2JrpPR/pf2y9Klr1+V9Wf3799v8hNPPGHy0KFDTR43bpzJup8bPRoAQEGi0AAAXFFoAACucqpHo+c76Pzjl770JZN1zYrOmer8pM6Z6/kT2k/RrOMJIbFHc+zYMZN1HYSeL6Lvi+/du7fJvXr1MlnfF4/s0X7ckCFDTNYeYAgh3HXXXSb36dPHZN3bSs+40R5etubcc9GCBQtM1r+1jz76yOR58+aZXNnedJ70+U6cOJHwNR9++KHJPXr0MLlz584m62uc9gRXrFhh8u7du6s01uvFHQ0AwBWFBgDgikIDAHCVUz0a3Vts2LBhJj/88MMmN2rUyGRdx6D9Ee2n6Pxkqr2KNIcQwqJFi0zW80batGlj8k033WTy2LFjTR4+fLjJuvcVPZrM0XUOevaL/u5uv/12k2+99daEx9SenF6junfZtm3bTP7jH/9ocqbm2PPBL3/5S5Pnzp1rcocOHUzWfecy3aOpCt2fUdcK6jor7Ss3a9bMZH2NzBTuaAAArig0AABXFBoAgKuc6tEcOHDAZJ2P1jnzmTNnmqw9m+PHj5u8adMmk1euXGnyqVOn0sohhFBaWmrymTNnTNZ5X+3pfOMb3zBZezRdunQxWc+wuHDhQsKY8MXo/LWuUdD+2n333WfyiBEjTNaeQAiJ+29p1nUOb731lsnbt283uaKiIuE5airtV+heZZozTfut+rfcr1+/hO+ZPXu2yePHjzdZ93/UdXx63lW2fgbc0QAAXFFoAACuKDQAAFc51aPROda9e/ea/NJLL5n86quvmpzqPeaa9eszQXsq2vdJdaa87oVFj6bqtMfXsWNHk/W8Ip0P1zPpdW8z/V2Vl5cnjEHPRFm6dKnJurfVv//9b5P5fWePXj+NGzc2Wdes6N+q7uWo+5bp9RVCCNOnT0/6GNr30fOI9Iybc+fOJTxHJnBHAwBwRaEBALii0AAAXOVUjyYVfY+4zk9q1n2kNGeD7pem55Drugjt2eg8sJ5Xgs/oz07P6tA58WnTppl84403mlzZupj/tXPnTpO13xJCCO+++67JunZM12Xp/nzIHN03THtyd999t8mTJ082uVOnTiZrj0f7LZpDSHxNS0Wved2fT8eQqf3duKMBALii0AAAXFFoAACu8qpHo3KxB5OK9mh0LY/ulZbqfAl8Rs+IT3UW0GOPPWay7m2me1Hpuhg9C0b35isuLk4Yo+49lY21XKga3UfskUceMVn3KdS/1VQ946rQ1wvdG0/Pq9E+5IQJE0xevny5yXrekRfuaAAArig0AABXFBoAgCsKDQDAVV6/GSAfaUNQN0nUhrM2JGvymwG0Oa+bYo4ePdpkXUA3btw4k3v16mWybrr60UcfmawbXK5bt85kPUjv0KFDQXFQWf4oKyszWQ8tHDhwoMl6/elBerqBbpMmTUyubMPLDRs2mKyHOd5www0m9+/f3+QpU6aYvHHjRpN5MwAAoCBQaAAArig0AABX9GiyTBdkaY+mdevWJjdv3tx9TLmiUaNGJusml9/+9rdNnjhxosm6gFPnwFevXm3yihUrkn5+y5YtJushZrpB6rVr1wLyl/bTtEdTUlJicpcuXUzWg/C0P6LXpy7WDiHxGtNFyD/+8Y9N7t27t8l6OJse7pcp3NEAAFxRaAAArig0AABX9GiyTOdM9b31uu5GDzIqZMOHDzf5jjvuMPnLX/6yybrB4KpVq0xOtQ5m69atJuuhZJXNoaNw6d+erqvR62H79u0m6yFjumZu//79JlfW09ODyQ4fPmyy9ol00039N2RrHRd3NAAAVxQaAIArCg0AwBU9mizTvoJmfe99ZftnFaof/vCHJo8aNcpkXRczb948kxcvXmzypk2bTNY58sr2mgI+z/X2P3RvvarQHoyuszly5IjJepjfnDlzTH766adNvnTpUtpjqgruaAAArig0AABXFBoAgCt6NBmmZ6ro+TJ6/oyu5dD31Reym2++2WRd5/L3v//d5LffftvkXbt2maz7yAH5Rvc61HV4LVq0MFnX8ujeifp6RI8GAJCXKDQAAFcUGgCAK3o0GabnSei597qX2bFjx0z+9NNPfQaWg3TfN90r6uTJkybrXlHZ2tcJ2RFFUbU+nq6TuV46Pu2f1KmT+HI8YMAAk++9916T77zzTpP1/Bn9G3j99ddNztSZSdzRAABcUWgAAK4oNAAAV/RoMizVOhqdM9Vzx7/I/kj5avXq1SafOHHC5Hbt2pk8cOBAk/V8EBS2li1bmqw9j0aNGpmsf1vak9F9xbR/qt+f6vN6veoaGF3jEkII/fv3N1nPaOrQoYPJ58+fN3np0qUm//znP0/69V64owEAuKLQAABcUWgAAK7o0WSYzvvq3kQXL140Wc9M0bUjhex3v/udyTrnrmsEjh8/brL+LFHYdu7cabL2TPSsJ93XS89y0X0Gdd1LrVq1kn5es+5jqD0ZHV8IiX2js2fPmrxmzRqTV6xYYfLcuXNN1p9RpvZO5I4GAOCKQgMAcEWhAQC4okeTYdo3GDRokMl169Y1uWnTpu5jylUvv/xyWl9f2V5RqDmKioqSfl77EdpD0X0HtYeie+3p37KuSdGv155PSUlJ0u8PIfEMJX2MlStXmvzxxx+bvGfPnoTHzAbuaAAArig0AABXFBoAgCsmtZ3pGRRt27Y1uX379iZrn2H37t0+AytAukYJNcvkyZNN1p7HuXPnkn5eeyr6ee3JZGoNSiHgjgYA4IpCAwBwRaEBALiiR+NM9yqaNWtW0s9rT2ft2rUm6/5KmTrzG8h1xcXF2R4CPgd3NAAAVxQaAIArCg0AwFWkPQIAAKoTdzQAAFcUGgCAKwoNAMAVhQYA4IpCAwBwRaEBALii0AAAXFFoAACuKDQAAFcUGgCAKwoNAMAVhQYA4IpCAwBwRaEBALii0AAAXFFoAACuKDQAAFcUGgCAKwoNAMAVhQYA4IpCAwBwRaEBALii0AAAXFFoAACuKDQAAFcUGgCAKwoNAMAVhQYA4IpCAwBwRaFJIYqis/Lf1SiKfpXtcSE3cb0gHTXleqmT7QHkujiOi/77v6MoKgohHA4hvJG9ESGXcb0gHTXleuGOJj13hRCOhhCKsz0Q5AWuF6SjYK8XCk16Hgwh/CGO4zjbA0Fe4HpBOgr2eokK8N/kIoqibiGEXSGE3nEc7872eJDbuF6QjkK/Xrijqbr7QwjLCvEigAuuF6SjoK8XCk3VPRBCeC3bg0De4HpBOgr6emHqrAqiKBoXQvgghNA+juPybI8HuY3rBemoCdcLdzRV82AI4W+FehGg2nG9IB0Ff71wRwMAcMUdDQDAFYUGAOCKQgMAcEWhAQC4yuimmlEU8c6DLIvjOMr2GKqK6yX7uF6Qjs+7XrijAQC4otAAAFxRaAAArig0AABXFBoAgCsKDQDAFYUGAOAqo+togOtRq5b9/0X9+/c3ecKECSbPnj3b5CZNmph89OhRk9977z2Ti4vt0e27d9szqa5evZpixABC4I4GAOCMQgMAcEWhAQC4yqseTe3atU3WOfdWrVqZfPLkSZPPnDlj8rVr10yOoihpZk4+s4qKikzu2rWrybNmzTJ5xowZJg8dOtTkRo0amXzp0iWTu3XrlvT53nrrLZO3bt1q8uXLlwOARNzRAABcUWgAAK4oNAAAV3nVo2nfvr3J06ZNM3ns2LEmL1u2zOQ1a9aYrD2bNm3amDx+/HiTDx06lDCmI0eOmKx9HB3zxYsXk2btC7Vu3drkiooKk8+ePWvy6dOnk44vnzRu3NjkUaNGmTxlyhSTx40bl9bj16tXz2Tt6ejzN2/e3OS5c+eafPz4cZMr6+npWqBUfcHq/vpUny8rK0sYM3C9uKMBALii0AAAXFFoAACu8qpH07NnT5Nnzpxpsq6jePDBB03+17/+ZbKue+jXr5/JHTt2NFn7HyGEcOLECZPPnz9vss6J6+c161qPQYMGmaxrS+rWrWvysWPHTF6wYEHCmPOV7m3Wrl27pF8fx/YIee1HpNKnTx+Tu3TpYrL29Pbv32+yrtMKIXEtWJ06dZJ+Pt2vT/fzmvfu3ZswZhQO/RvQvxEv3NEAAFxRaAAArig0AABXUabm6EIIIYqi63qyG2+80eRHHnnE5K9//esm6zqJVHP01fGzyMRzJHs+Xbuh63SaNWuWXqMii+rUqWN+WNozmThxosl33nmnyXq9pOrppEt7fPq70P5IZV+Ta06dOmVyq1atcnvA/+N6X1+86e++QYMGJnfu3Dnhe/SaveWWW0zWHm7Dhg1N/vjjj01+8803Tdb9+q5XHMeVXi/c0QAAXFFoAACuKDQAAFd5tY5m165dJv/zn/80uVmzZibr/GWPHj1M1n5J06ZNr3eIYf369Sbr3lEtW7Y0WfsO9evXT+v59EyVHTt2mLxkyRKTv/e976X1+Nmk/Sb9t+ledUePHjV5xIgRJg8ePNjk7t27m6zrZFq0aJF0fLqGqRCk+jcXMu2paX9E13Fpj0Vfn/R61MfTv/177703YUydOnUyWfuOeiaX/s306tXL5O3bt5u8c+dfcZ+VAAAIWElEQVROk/X1pLpwRwMAcEWhAQC4otAAAFzl1ToapetkdO+pG264weThw4eb/Oqrr5qsc7R6/khl89f6Md0PrUOHDibrGTcPP/ywyanm/bUvsWrVKpNfeeUVk4uLi00uLS0tmHURuo+criFo27atyd26dTNZe3i9e/dOmm+66SaT9Xefbn8tH9SqVatgrpdU9O9/0qRJJj/66KMma79Ee7z6epRKZWus9PwpPV9K99PTa1zXRT311FMmz5s3z+STJ09WbbCfg3U0AICsoNAAAFxRaAAArvJqHY3S93wfPHgwaV64cGHSx9M5f/1+/XwIqc93mDp1qsl63ky6azG05/L444+brD2iCxcupPX4+UTnp8+dO2fy7t27Td6zZ4/J69atM1nXJOi6h9mzZ5us/TdVXl6e8mOa9TknTJhg8pAhQ5I+p54vU9k1i8rpGhT929Gfrfbw9G9fr8eSkhKTdd2NnmcUQuI1u23bNpO1r/Tcc8+Z3LdvX5P1eqhsPz4PXIUAAFcUGgCAKwoNAMBVXvdoqltlZ7z/L53DDSFxjlN7MLfffrvJ06ZNMznd80l0XvfQoUMmZ3JdVL7Rn43uQ6f5wIEDJm/evNlknbNP9XyVfUxzq1atTJ4/f37Sz+t+Wz/4wQ9M1p4OPZuq27Rpk8lPP/20yW+88YbJI0eONPnYsWMmv//++yYfPnzYZF0zE0JiH1pz48aNTdZrVtfV7Nu3z+QTJ04kPKcHrjoAgCsKDQDAFYUGAOCKHs110jnzYcOGmaxnfOv+RzpHr+fQ67qZ5cuXJ/1++Ll48aL7c+gcu/bgdO3O97//fZPbt29vcro9QK6nz+i+gtqj27Jli8l/+ctf3MekfcEBAwaYrOfXpOoBpupLVxfuaAAArig0AABXFBoAgCt6NNdJz6V/5JFHTNYzu1PZsWOHyS+99JLJa9euTevxkF90XZb2XB566CGT7777bpO1B5huj0b359IzVmqyTPUzkrly5YrJt912m8m6X5/2lbK19yF3NAAAVxQaAIArCg0AwBWFBgDgijcDJKGHklV20NXMmTNNnjVrlsmpFsCtWbPG5HfeecfkxYsXm3z+/Pmkj4f8os16bebfddddJj/wwAMmd+zY0eR0D7I6e/asyQsWLDB5zpw5aT0efLVs2dJkXYCpeefOnSaXlpb6DCwF7mgAAK4oNAAAVxQaAIArejRJtGjRwuRJkyYlfM2oUaOu6zl0zrSkpMRk3diPTQ8Li15jM2bMMPm73/2uyXqQlfYRU9Ee37Jly0x+5plnTKZHk1u0T1xUVGSy9vz08LWNGzcm/Xqv1xfuaAAArig0AABXFBoAgCt6NEl07tzZ5JEjRyZ8TdeuXU2uVcvWbt2Ir6yszGTdJFPf905PprDoOojp06eb/OSTT5rcpUsXk9NdJ3Pp0iWT169fb/LTTz9tsvYIkV160NkNN9xg8vjx403Wnsz8+fNN1k01M/X6wh0NAMAVhQYA4IpCAwBwRY/mf9SrV89kPdTszjvvTPie5s2bm3z16lWTKyoqTNb3sS9dutTkgwcPVm2wyHmNGzdO+NjUqVNN/tGPfmTy9fZk9u/fb/Kf//xnkz/88EOTtWeTC4d74TO6l13fvn1N7tGjh8n79u0zWfdS1NenTOGOBgDgikIDAHBFoQEAuKrRPRrdJ6pTp04m63vWGzZsmPZz7Nixw+RFixaZvGHDBpP1fe7IXdo/0etJ18iEEMLjjz9ucr9+/ZI+ptJ1D7pOpri42ORnn33WZO0Zsk4rt6V6jdLfn76+XLx40WR6NACAgkShAQC4otAAAFzV6B6Nns8+ZcoUk8eNG2dykyZN0n4OXbewZMkSk8vLy9N+TGSH9k90zcugQYNMfuyxxxIeY8yYMSbr3nipnD592uRPPvnEZO3RaA+Hnkzuql+/fsLH9PyZadOmmXzq1CmTP/jgA5NPnDhhcrZ+/9zRAABcUWgAAK4oNAAAVzW6R9OnTx+T77vvvqSfr4pDhw6ZrOfN7NmzJ+3HRG7Qfadefvllk3VvPD3P/YvQ80VeffVVk3/xi1+YXFpaet3Piexo0KBBwsceeOABk5s1a2ay/r7Pnz9v8tmzZ6tpdNeHOxoAgCsKDQDAFYUGAOCqRvVodA5Uz3LQ89x1b7PK1jzoOhg9b2bbtm0mnzlzpmqDRdbpeTJ6vei6qnTXxFRG1z389a9/NfmVV14xmZ5M4ajstaFdu3YmR1Fk8vLly03WHnCurJvijgYA4IpCAwBwRaEBALgq6B5NqrMcdN8p/byqbL7z+PHjJuu6mV27dpms73NH7tK98GbMmGGyXi9VOa9IryG9HhYuXGjya6+9ZrKeb4TCoT3iEBJ7dnv37jV5xYoVJm/fvr36B1YNuKMBALii0AAAXFFoAACuCrpHo+tm9D3pQ4cONVn3ptL59Mp6NHo+SElJicllZWVVGyxyjp4P0rt3b5Pr1atnsq5xqMyVK1dM1vNjfv/735us67KydeY7/A0ePDjhY9on1NcT7RFfvny5+gdWDbijAQC4otAAAFxRaAAArgq6R3Pt2jWTdQ5d97LS+XOdo9d9zUJIPC+EvacKh57loTnVfLheTyGEsG/fPpOff/55k1euXGnyxYsXU44T+al27domt27dOuFrdH89fQ3TtVv6mLnS0+OOBgDgikIDAHBFoQEAuIpy5bwCAEBh4o4GAOCKQgMAcEWhAQC4otAAAFxRaAAArig0AABXFBoAgCsKDQDAFYUGAOCKQgMAcEWhAQC4otAAAFxRaAAArig0AABXFBoAgCsKDQDAFYUGAOCKQgMAcEWhAQC4otAAAFxRaAAArig0AABXFBoAgKv/AzPqPM+yzEk2AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(rows=3, figsize=(6,6))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

labels_to_csv[source]

\n", "\n", "> labels_to_csv(`dest`:`str`)\n", "\n", "Save file names and labels in `data` as CSV to file name `dest`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.labels_to_csv)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a functional version of [`ImageDataBunch.show_batch`](/vision.data.html#ImageDataBunch.show_batch)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next two methods we will use a new dataset, CIFAR. This is because the second method will get the statistics for our dataset and we want to be able to show different statistics per channel. If we were to use MNIST, these statistics would be the same for every channel. White pixels are [255,255,255] and black pixels are [0,0,0] (or in normalized form [1,1,1] and [0,0,0]) so there is no variance between channels." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PosixPath('/home/ubuntu/.fastai/data/cifar10')" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.CIFAR); path" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

channel_view[source]

\n", "\n", "> channel_view(`x`:`Tensor`) → `Tensor`\n", "\n", "Make channel the first axis of `x` and flatten remaining axes " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(channel_view)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_folder(path, ds_tfms=tfms, valid='test', size=24)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def channel_view(x:Tensor)->Tensor:\n", " \"Make channel the first axis of `x` and flatten remaining axes\"\n", " return x.transpose(0,1).contiguous().view(x.shape[1],-1) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function takes a tensor and flattens all dimensions except the channels, which it keeps as the first axis. This function is used to feed [`ImageDataBunch.batch_stats`](/vision.data.html#ImageDataBunch.batch_stats) so that it can get the pixel statistics of a whole batch.\n", "\n", "Let's take as an example the dimensions our MNIST batches: 128, 3, 24, 24." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t = torch.Tensor(128, 3, 24, 24)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([128, 3, 24, 24])" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t.size()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tensor = channel_view(t)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([3, 73728])" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor.size()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

batch_stats[source]

\n", "\n", "> batch_stats(`funcs`:`Collection`\\[`Callable`\\]=`None`) → `Tensor`\n", "\n", "Grab a batch of data and call reduction function `func` per channel " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.batch_stats)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gets the statistics of each channel of a batch of data. If no functions are specified, default statistics are mean and standard deviation. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[tensor([0.5047, 0.5339, 0.5599]), tensor([0.2412, 0.2319, 0.2493])]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.batch_stats()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

normalize[source]

\n", "\n", "> normalize(`stats`:`Collection`\\[`Tensor`\\]=`None`)\n", "\n", "Add normalize transform using `stats` (defaults to `DataBunch.batch_stats`) " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageDataBunch.normalize)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adds the normalize transform to the set of transforms associated with the data. In the fast.ai library we have `imagenet_stats`, `cifar_stats` and `mnist_stats` so we can add normalization easily with any of these datasets. Let's see an example with our dataset of choice: MNIST." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.normalize(cifar_stats)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[tensor([0.0556, 0.2135, 0.4325]), tensor([0.9765, 0.9543, 0.9552])]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.batch_stats()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other functions" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

show_image_batch[source]

\n", "\n", "> show_image_batch(`dl`:[`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader), `classes`:`StrList`, `rows`:`int`=`None`, `figsize`:`Tuple`\\[`int`, `int`\\]=`(9, 10)`)\n", "\n", "Show a few images from a batch. \n", "\n", "- *dl*: A dataloader from which to show a sample\n", "- *classes*: List of classes (for the labels)\n", "- *rows*: Will make a square of `rows` by `rows` images\n", "- *figsize*: Size of the graph shown " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(show_image_batch, arg_comments={\n", " 'dl': 'A dataloader from which to show a sample',\n", " 'classes': 'List of classes (for the labels)',\n", " 'rows': 'Will make a square of `rows` by `rows` images',\n", " 'figsize': 'Size of the graph shown'\n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a functional version of [`ImageDataBunch.show_batch`](/vision.data.html#ImageDataBunch.show_batch)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data normalization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may also want to normalize your data, which can be done by using the following functions." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

normalize[source]

\n", "\n", "> normalize(`x`:`Tensor`, `mean`:`FloatTensor`, `std`:`FloatTensor`) → `Tensor`\n", "\n", "Normalize `x` with `mean` and `std`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(normalize)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

denormalize[source]

\n", "\n", "> denormalize(`x`:`Tensor`, `mean`:`FloatTensor`, `std`:`FloatTensor`) → `Tensor`\n", "\n", "Denormalize `x` with `mean` and `std`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(denormalize)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

normalize_funcs[source]

\n", "\n", "> normalize_funcs(`mean`:`FloatTensor`, `std`:`FloatTensor`) → `Tuple`\\[`Callable`, `Callable`\\]" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(normalize_funcs, doc_string=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create [`normalize`](/vision.data.html#normalize) and [`denormalize`](/vision.data.html#denormalize) functions using `mean` and `std`. `device` will store them on the device specified. `do_y` determines if the target should also be normaized or not." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On MNIST the mean and std are 0.1307 and 0.3081 respectively (looked on Google). If you're using a pretrained model, you'll need to use the normalization that was used to train the model. The imagenet norm and denorm functions are stored as constants inside the library named imagenet_norm and imagenet_denorm. If you're training a model on CIFAR-10, you can also use cifar_norm and cifar_denorm.\n", "\n", "You may sometimes see warnings about *clipping input data* when plotting normalized data. That's because even although it's denormalized when plotting automatically, sometimes floating point errors may make some values slightly out or the correct range. You can safely ignore these warnings in this case." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data = ImageDataBunch.from_folder(untar_data(URLs.MNIST_SAMPLE),\n", " ds_tfms=tfms, size=24)\n", "data.normalize()\n", "data.show_batch(rows=3, figsize=(6,6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Depending on the task you are tackling, you'll need one of the following fastai datasets." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ImageClassificationDataset[source]

\n", "\n", "> ImageClassificationDataset(`fns`:`FilePathList`, `labels`:`StrList`, `classes`:`Optional`\\[`ArgStar`\\]=`None`) :: [`ImageClassificationBase`](/vision.data.html#ImageClassificationBase)\n", "\n", "[`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) for folders of images in style {folder}/{class}/{images}. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageClassificationDataset, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the basic dataset for image classification: `fns` are the filenames of the images and `labels` the corresponding labels. Optionally, `classes` contains a name for each possible label." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_folder[source]

\n", "\n", "> from_folder(`folder`:`Path`, `classes`:`Optional`\\[`ArgStar`\\]=`None`, `valid_pct`:`float`=`0.0`, `extensions`:`StrList`=`{'.jpg', '.jpe', '.jpeg', '.gif', '.ico', '.rgb', '.xbm', '.ppm', '.xwd', '.svg', '.xpm', '.pgm', '.png', '.bmp', '.tiff', '.ras', '.tif', '.pbm', '.ief', '.pnm'}`) → `Union`\\[`ImageClassificationDataset`, `List`\\[`ImageClassificationDataset`\\]\\]\n", "\n", "Dataset of `classes` labeled images in `folder`. Optional `valid_pct` split validation set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageClassificationDataset.from_folder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create an [`ImageClassificationDataset`](/vision.data.html#ImageClassificationDataset) automatically from a `folder`. If `classes` is None, it will be set to the names of the directories in `folder`. `check_ext` forces the function to only keep filenames with image extensions." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_single_folder[source]

\n", "\n", "> from_single_folder(`folder`:`PathOrStr`, `classes`:`ArgStar`, `extensions`:`StrList`=`{'.jpg', '.jpe', '.jpeg', '.gif', '.ico', '.rgb', '.xbm', '.ppm', '.xwd', '.svg', '.xpm', '.pgm', '.png', '.bmp', '.tiff', '.ras', '.tif', '.pbm', '.ief', '.pnm'}`)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageClassificationDataset.from_single_folder, doc_string=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typically used for define a test set. Label all images in `folder` with `classes[0]`. `check_ext` forces the function to only keep filenems with image extensions." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ImageMultiDataset[source]

\n", "\n", "> ImageMultiDataset(`fns`:`FilePathList`, `labels`:`StrList`, `classes`:`Optional`\\[`ArgStar`\\]=`None`) :: [`ImageClassificationBase`](/vision.data.html#ImageClassificationBase)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageMultiDataset, doc_string=False, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the basic dataset for image classification with multiple labels: `fns` are the filenames of the images and `labels` the corresponding labels (may be more than one for each image). Optionally, `classes` contains a name for each possible label." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_folder[source]

\n", "\n", "> from_folder(`path`:`PathOrStr`, `folder`:`PathOrStr`, `fns`:`Series`, `labels`:`StrList`, `valid_pct`:`float`=`0.2`, `classes`:`Optional`\\[`ArgStar`\\]=`None`)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageMultiDataset.from_folder, doc_string=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To create an [`ImageMultiDataset`](/vision.data.html#ImageMultiDataset) automatically in `path` from a `folder` and `fns`. If `classes` is None, it will be set to the names of the different `labels` seen. You can split the images in this `folder` in a train/valid dataset if `valid_pct` is non-zero. `check_ext` forces the function to only keep filenems with image extensions." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_single_folder[source]

\n", "\n", "> from_single_folder(`folder`:`PathOrStr`, `classes`:`ArgStar`, `extensions`=`{'.jpg', '.jpe', '.jpeg', '.gif', '.ico', '.rgb', '.xbm', '.ppm', '.xwd', '.svg', '.xpm', '.pgm', '.png', '.bmp', '.tiff', '.ras', '.tif', '.pbm', '.ief', '.pnm'}`)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageMultiDataset.from_single_folder, doc_string=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typically used for define a test set. Label all images in `folder` with `classes[0]`. `check_ext` forces the function to only keep filenems with image extensions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To help scan a folder for these [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset), we use the following helper function:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_image_files[source]

\n", "\n", "> get_image_files(`c`:`PathOrStr`, `check_ext`:`bool`=`True`, `recurse`=`False`) → `FilePathList`" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(get_image_files, doc_string=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Return list of files in `c` that are images. `check_ext` will filter to keep only the files with image extensions." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_labels[source]

\n", "\n", "> get_labels(`idx`:`int`) → `StrList`" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageMultiDataset.get_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gets the labels of a batch of images of choice. Pass in the batch number/index and will return the labels for each of the examples in that batch." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

encode[source]

\n", "\n", "> encode(`x`:`Collection`\\[`int`\\])\n", "\n", "One-hot encode the target. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageMultiDataset.encode)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class SegmentationDataset[source]

\n", "\n", "> SegmentationDataset(`x`:`FilePathList`, `y`:`FilePathList`, `classes`:`ArgStar`) :: [`ImageClassificationBase`](/vision.data.html#ImageClassificationBase)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(SegmentationDataset, doc_string=False, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the basic dataset for image sementation: `x` contains the filenames of the images and `y` the ones of the masks." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ObjectDetectDataset[source]

\n", "\n", "> ObjectDetectDataset(`x_fns`:`FilePathList`, `labelled_bbs`:`Collection`\\[`Tuple`\\[`Collection`\\[`int`\\], `str`\\]\\], `classes`:`StrList`=`None`) :: [`ImageClassificationBase`](/vision.data.html#ImageClassificationBase)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ObjectDetectDataset, doc_string=False, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the basic dataset for object detection: `x_fns` contains the filenames of the images, `labelled_bbs` the corresponding bounding boxes with their corresponding labels. `classes` contains the list of classes." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_json[source]

\n", "\n", "> from_json(`folder`, `fname`, `valid_pct`=`None`, `classes`=`None`)\n", "\n", "Create an [`ObjectDetectDataset`](/vision.data.html#ObjectDetectDataset) by looking at the images in `folder` according to annotations in the json `fname`. If `valid_pct` is passed, split a training and validation set. `classes` is the list of classes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ObjectDetectDataset.from_json)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This factory method uses the following helper function." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_annotations[source]

\n", "\n", "> get_annotations(`fname`, `prefix`=`None`)\n", "\n", "Open a COCO style json in `fname` and returns the lists of filenames (with maybe `prefix`) and labelled bboxes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(get_annotations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, to apply transformations to [`Image`](/vision.image.html#Image) in a [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset), we use this last class." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class DatasetTfm[source]

\n", "\n", "> DatasetTfm(`ds`:[`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset), `tfms`:`Union`\\[[`Transform`](/vision.image.html#Transform), `Collection`\\[[`Transform`](/vision.image.html#Transform)\\]\\]=`None`, `tfm_y`:`bool`=`False`, `kwargs`:`Any`) :: [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(DatasetTfm, doc_string=False, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dataset that applies the list of transforms `tfms` to every item drawn. If `tfms` should be applied to the targets as well, `tfm_y` should be True. `kwargs` will be passed to [`apply_tfms`](/vision.image.html#apply_tfms) internally." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then this last function automatizes the process of creating [`DatasetTfm`](/vision.data.html#DatasetTfm):" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform_datasets[source]

\n", "\n", "> transform_datasets(`train_ds`:[`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset), `valid_ds`:[`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset), `test_ds`:`Optional`\\[[`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)\\]=`None`, `tfms`:`Optional`\\[`Tuple`\\[`Union`\\[[`Transform`](/vision.image.html#Transform), `Collection`\\[[`Transform`](/vision.image.html#Transform)\\]\\], `Union`\\[[`Transform`](/vision.image.html#Transform), `Collection`\\[[`Transform`](/vision.image.html#Transform)\\]\\]\\]\\]=`None`, `resize_method`:[`ResizeMethod`](/vision.image.html#ResizeMethod)=`None`, `kwargs`:`Any`)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(transform_datasets, doc_string=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create train, valid and maybe test DatasetTfm from `train_ds`, `valid_ds` and maybe `test_ds` using `tfms`. It should be a tuple containing the transforms for the training set, then for the validation and test set." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ImageClassificationBase[source]

\n", "\n", "> ImageClassificationBase(`fns`:`FilePathList`, `classes`:`Optional`\\[`ArgStar`\\]=`None`) :: [`LabelDataset`](/basic_data.html#LabelDataset)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageClassificationBase, title_level=3, doc_string=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Base class for computer vision datasets. Maps `classes` to indexes via `class2idx`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Links with the data block API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The vision application adds a few methods to implement data augmentation in the data block API or create an [`ImageDataBunch`](/vision.data.html#ImageDataBunch)." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class SplitDatasetsImage[source]

\n", "\n", "> SplitDatasetsImage(`path`:`PathOrStr`, `train_ds`:[`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset), `valid_ds`:[`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset), `test_ds`:`Optional`\\[[`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)\\]=`None`) :: [`SplitDatasets`](/data_block.html#SplitDatasets)\n", "\n", "A class regrouping `train_ds`, a `valid_ds` and maybe a `train_ds` dataset, inside a `path`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(SplitDatasetsImage, title_level=3)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform[source]

\n", "\n", "> transform(`tfms`:`Union`\\[[`Transform`](/vision.image.html#Transform), `Collection`\\[[`Transform`](/vision.image.html#Transform)\\]\\], `kwargs`) → `SplitDatasets`\n", "\n", "Apply `tfms` to the underlying datasets, `kwargs` are passed to [`DatasetTfm`](/vision.data.html#DatasetTfm). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(SplitDatasetsImage.transform)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

databunch[source]

\n", "\n", "> databunch(`path`:`PathOrStr`=`None`, `kwargs`) → `ImageDataBunch`\n", "\n", "Create an [`ImageDataBunch`](/vision.data.html#ImageDataBunch) from self, `path` will override `self.path`, `kwargs` are passed to [`ImageDataBunch.create`](/vision.data.html#ImageDataBunch.create). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(SplitDatasetsImage.databunch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To change the default `extensions` in [`InputList.from_folder`](/data_block.html#InputList.from_folder) to image extensions, we subclass it to the following:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ImageFileList[source]

\n", "\n", "> ImageFileList(`items`:`Iterator`, `path`:`PathOrStr`=`'.'`) :: [`InputList`](/data_block.html#InputList)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageFileList, title_level=3, doc_string=False)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_folder[source]

\n", "\n", "> from_folder(`path`:`PathOrStr`=`'.'`, `extensions`:`StrList`=`{'.jpg', '.jpe', '.jpeg', '.gif', '.ico', '.rgb', '.xbm', '.ppm', '.xwd', '.svg', '.xpm', '.pgm', '.png', '.bmp', '.tiff', '.ras', '.tif', '.pbm', '.ief', '.pnm'}`, `recurse`=`True`) → `ImageFileList`\n", "\n", "Get the list of files in `path` that have a suffix in `extensions`. `recurse` determines if we search subfolders. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ImageFileList.from_folder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Undocumented Methods - Methods moved below this line will intentionally be hidden" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New Methods - Please document or move to the undocumented section" ] } ], "metadata": { "jekyll": { "keywords": "fastai", "summary": "Basic dataset for computer vision and helper function to get a DataBunch", "title": "vision.data" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }