{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## The data block API" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [], "source": [ "from fastai.gen_doc.nbdoc import *\n", "from fastai.basics import *\n", "np.random.seed(42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data block API lets you customize the creation of a [`DataBunch`](/basic_data.html#DataBunch) by isolating the underlying parts of that process in separate blocks, mainly:\n", " 1. Where are the inputs and how to create them?\n", " 1. How to split the data into a training and validation sets?\n", " 1. How to label the inputs?\n", " 1. What transforms to apply?\n", " 1. How to add a test set?\n", " 1. How to wrap in dataloaders and create the [`DataBunch`](/basic_data.html#DataBunch)?\n", " \n", "Each of these may be addresses with a specific block designed for your unique setup. Your inputs might be in a folder, a csv file, or a dataframe. You may want to split them randomly, by certain indices or depending on the folder they are in. You can have your labels in your csv file or your dataframe, but it may come from folders or a specific function of the input. You may choose to add data augmentation or not. A test set is optional too. Finally you have to set the arguments to put the data together in a [`DataBunch`](/basic_data.html#DataBunch) (batch size, collate function...)\n", "\n", "The data block API is called as such because you can mix and match each one of those blocks with the others, allowing for a total flexibility to create your customized [`DataBunch`](/basic_data.html#DataBunch) for training, validation and testing. The factory methods of the various [`DataBunch`](/basic_data.html#DataBunch) are great for beginners but you can't always make your data fit in the tracks they require.\n", "\n", "\"Mix\n", "\n", "As usual, we'll begin with end-to-end examples, then switch to the details of each of those parts." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples of use" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's begin with our traditional MNIST example." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/valid')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_TINY)\n", "tfms = get_transforms(do_flip=False)\n", "path.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train/3'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train/7')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(path/'train').ls()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In [`vision.data`](/vision.data.html#vision.data), we can create a [`DataBunch`](/basic_data.html#DataBunch) suitable for image classification by simply typing:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a shortcut method which is aimed at data that is in folders following an ImageNet style, with the [`train`](/train.html#train) and `valid` directories, each containing one subdirectory per class, where all the labelled pictures are. There is also a `test` directory containing unlabelled pictures. \n", "\n", "Here is the same code, but this time using the data block API, which can work with any style of a dataset. All the stages, which will be explained below, can be grouped together like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = (ImageList.from_folder(path) #Where to find the data? -> in path and its subfolders\n", " .split_by_folder() #How to split in train/valid? -> use the folders\n", " .label_from_folder() #How to label? -> depending on the folder of the filenames\n", " .add_test_folder() #Optionally add a test set (here default name is test)\n", " .transform(tfms, size=64) #Data augmentation? -> use tfms with a size of 64\n", " .databunch()) #Finally? -> use the defaults for conversion to ImageDataBunch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can look at the created DataBunch:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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p6Wlbd4O1CsjX2p/RC9Pe3l7btc6i3bFjBxe16/To+/WD01LazMwMH5URjUY3XJzqBO53jYIgCEJBUbAtc7S84fP5WN44efIkjh8/DsBqmWP2xBoaGgIAXLx4kaOc8+fPc0ZLNBrlFYZZt5GOYDDI4XZZWRmvfFOpFGfEjYyMFFrhaFFSUlLCEU1jYyMfb9DY2GiTeAGrNc7evXttj2l01FRRUcH2euDAAU5A6enp4VZOCwsLbHuFFPnkA/rvnUql7tsyC7AiH/PxSCQCwFJKdEZrVVUVJxXs2LGD54rDhw8DuPfYBD0HRaNRPgphaGgIAwMDAKyM23w4CK5gHY/OVAsGgzwZPPXUUzhy5Ag/rlNiTXnt9ddf52rx8fFx1l/NPZn1PuxerxctLS0ArBMqtdSmlOIQe3x8XBxPARAIBFgqOXr0KB599FEAlpSmpVyt7be0tNxz7AFg725RXl7Ox2pXVlZix44dAKyFkk7Fvn37NksrWvYVnEUpte48sPr/zb0fE71Inpqa4vlhLRleO6+pqSnefx4cHGTHMzMzc98egW5BpDZBEATBUQo24jGjE50UYK4SSktLWfbq6+vD2bNnAQA3btxg2S0ajd6zOskEImKZxWzHs7y8zGOJRCKuzrMX1qakpITv7759+/DMM88AAI4fP4729nYAVk2GThLQUY6WUQD7ERmxWIwjl7KyMk6AicVinPSya9cuLl784IMPcPr0aQAS8eQ7iUSC7+HMzAxHPDqyMTFrCScnJznbbmJigrPaFhcXH2jOcpqCdzzJZJJvZk9PDz9eW1vLTR/PnTuHy5cvA7Bnmz1oCisRZZRZkg/ZJ8K9BINBdjCnTp3Ciy++CMBqNqudjM/nu6cKffWegLavxcVFttHa2lrOiDTtaPv27Szfer1ePutndHQ0YwlYcB+mzLq8vGzbw0mHnptGRkZsad5ms+J8sAOR2gRBEARHKdiIR68A4vE4Z5D8+te/Rm9vLwBr41ZLbbdu3cLg4CCAjWWFaCmltLSU5TUi4tWL2TZ/aWkpL0Ji4V6CwSBnnnV2dnIkUl1dnbbXlhl9a6nkzp07HLXcuXOHbXTHjh2cDLN7927s3LkTgGVbut3Onj17OOLq6+vj19SynJA/eDwelmQrKyt53lhrDtKy3MTEBB+xEolE1kxccCsF63jMDgHaqUxOTnJ6aiAQ4Ju0sLDAzuFB9108Hg+nPtbV1XEGEhFxEeDQ0BDLe5OTkzJR5CklJSWcAmvu5awuKtSYxYg6G+3dd9/F66+/DsDKUtN7j62trZzV9sILL/D71NTUcNp2R0cHOjs7AVjHa5h7RUL+oeekyspKvl5LLtNz1uzsLO/rxONxtkFTujMLjd0mv4nUJgiCIDhKwUY8GqUUh6dLS0u8IvV6vbZisI2uCDweD0c59fX1nPUEgFez169f52tzQ1DILzweD9eJrdUXy7QnHU0PDw/zoWLvv/8+zp8/D8DKZtISbDQaZQl47969ePLJJwFYMp7Z800nIASDQR7L6oJFIT/QUU5VVRUnp6xlV2ZCim7lk0wm+eeIyBb5urU3ZME7ntWYevvqxzaCx+NhZ2NWrSuluLjr7NmzrMu6zRCEzFlcXOQuAv39/Xj44YcB2J2D2Vpfy2vvvfcey2tXrlzhicOUdxcXF3lxMjQ0xGm1yWSSX7ukpISllUAgsKVHLAtbi1KK94YrKip48brWPdUOaWlpiTMhvV6vrUjd7CmpF92RSISv3TDviNQmCIIgOErRRTyazfb6Ho+HN3/b2tq4u6xSile2g4OD3ILHDasO4cGIRqN8OFh/f7+ta7QmlUpxtplObnn77bfx1ltvAbAyHNPVasTjcW55Mjk5ya9htmjxer28SjZrfURqyx9MyV9HOXV1dTxv6Ih2NY2N1unfDz/8MGfAAbCdYGt209bF8Hfu3GEJzg1qi0Q8giAIgqMUbcSz2fh8Pl6N7N27lzVXofCIx+OcIn/x4kX89KfWSc0HDhzgqDcWi/GBgVeuXOHn6iSCtRo5EpGts7r5uIneo4zFYpKk4jLMvTgdiXg8Hn48GAxyckhdXR0OHDgAwKrb0l3L9R5xKpWyJRroWq7HHnsM+/bt4/fREZK5pzwwMIALFy4AsPYOdacD/br6+blAHM8m4fV6bQd+6c7EQuGRSCRYXuvu7mZncubMGe5Uvbi4yG2YzN5/2uGs9YE3M+a8Xm9aGY2IeOIwT7uVFky5h4hsJw7rOcHj8bADqays5CLhzs5Ortvq6OhAa2srgI8PD1yNzl7TP7OaeDzO7xMMBjkBoa+vj+3KbKuTK8cjUpsgCILgKBLxbBJEZGt9oXPzVz9HcIZ0kcJmre6UUhxlhMNh3L59G4BVj6Ojm1gsxtfZHHEeCoX4/J729nbuhkFEnLxgptKGw2FOUhDJLXeY9qYji8rKSo5g/H4/R0I1NTXYvXs3AEte01FyQ0MDKyXp5o9M8Pl8LMeVl5dzTdjly5d5fjLrGXNFUTge85x0E7OA9EFfVxuZWdRXU1PDWUfRaJQnnkgkkheHNOU75iLA5/PxxGy2ENkszP5ry8vLtoMD1+owfD+qqqpw6NAhAMD+/fvZpjweD2clLS4u8oQyPz/Pj+c6U6mY0ZlpNTU1vE/T0dHBJ86GQiF2JhUVFexstm3bxs6mvLzc1t0cyHyxau7Z6PfxeDw2R6bnJDccpSFSmyAIguAoGUc8ROQFcBbAsFLqBSLqAPAjALUAzgP4ilLKlSebaU/v9/t5BWFWli8vLz9Q1GMmFLS0tHCX4sbGRo5sxsbGWIrp7e1Ne8BTIZILe9H31mziGQwGOTpYXFzk+7JZ55Yopfg14/H4A60mS0pK2EZ37NjBnRD27t3LK+lUKsXy2tDQEGcomckF+U4+zzE6Mu3s7OSI9dChQzh+/DgAezPZ0tJS2+GAZlRjzk8aPTclEgm2NbMZqFmXY9aPRSIR7poRi8X4td1Qx5ON1PYNAF0AKle+/xMA/10p9SMi+gsAXwPw3U0e36agJTYzPRXYuCbu8/l4gmtra+NW+VVVVVw0ODg4iOHhYQDgdjlFgqP2QkSoq6sDYEkcp06dAgA0NTVxC5r+/n709PQAsO6FlsW2QoJbDzO9tqmpibOUnnzySTz++OMArFNH9WQVjUbR3d0NAHjrrbc4ZTbXE8gmk7dzjHYkra2tOHLkCAArvV6fGqsd0/1Y6wBJs/u4lu0TiQQ7nmQyaZPa9AJlenqaHc/S0tKGtxY2k4ykNiJqBfDbAP5y5XsC8ByAn6w85QcAvrAVAxTyD7EXIVvEZoqLTCOePwPwnwHolst1AGaVUrpHyBCAlk0e26axVu2EXjE86KqxpKSEC0Xb29tZdlteXuYV6cWLF/ncjCLCcXvx+XycQfTMM8/gpZdeAmBJVxMTEwCse/GLX/wCAHD+/Hne/M/FccEej4fltfb2dnzyk58EYEU8x44dAwBbLVgkEsH169cBAL/61a+4ZU+hyGzI8zkmXY1OTU1NRg1czYy41RGP2SppYWGBz/OanZ3lWjKzAajZ3dyMeNzWMmddx0NELwAYV0qdI6Jn9cNpnpr2NyGiVwC88sAj3ATMkHQz8fl8LO+0tbXxRBGPx3liuH79elE5nlzZi8/n40yh3bt3c0pyVVWVrXBOywytra0sXd2+fZs/0HNzc1vWxdfMgmxtbeWU2k984hN49NFHAVhOSMs2qVSK5cD+/n4+PdfsWp3rCWQzKIQ5RjuM8vJy7Nq1C4BVBGqeKGpmJernBwIBzkLzer02WwUsSU33Xjt//jzOnTsHwDpqRduGmdkYi8XYfhcXF/l6dnaW937cILVlEvGcAvC7RPR5AEFY+uufAagmIt/KiqQVwN10P6yUehXAqwBARPn/KRHWQ+xFyBaxmSJjXcejlPpDAH8IACurkf+klPqXRPQ3AL4IK+vkZQA/28JxuhKPx8Or0+rqat4IjsfjuHvX+oz09PRw6FsM5MpeSkpKOLmjtbUVpaWlAOzdf9vb27m47siRI1zgeebMGbz33nsArPul791mR8gej4dXt4cOHcLv/u7vArDqdfQquaqqylZvocd49epVPgNoZmZm3dY7+UQhzDE6spiYmMCNGzcAWPdSJxUQEWcijo+P2zrZ62zYQCDAkZDZi08nFHR3d+P9998HYLXA0RFPOBy2nemUD2ykgPRbAH5ERP8vgAsAvrc5Q3I/Ws/1+/1sWLW1tTzZxeNxzqQaGBjIqnK9gNlSe/F6vayne71evkdmj6zy8nJ2QpWVlZyRGIvFWGqbnJzkCeJBHI+p15vNIvX76z3Bhx9+mDPvzCJC8yCv0dFR3tc5f/48y7duKAB0iLyZY/RnfGhoCGfPngVg3Vd9v30+HzeNHRgY4IbCyWTStpenbUVLZ5OTk7z4uH37Ni+KJiYm+Dn55nSALB2PUuptAG+vXPcBeHTzhyQUCmIvQraIzRQHRdEyZ7PQMolOKNi9eze3xOjs7OToZ3FxkVchsViskDKPXEsqleKjCqampjLaQNUyVTAYZAnO3BCOx+NZbcSayQPBYJBXsvq19+zZg4MHDwIAHn/8cX68rKyMo7LJyUlcu3YNAHDhwgVcvHgRAHDt2rViqwPLK3QUOjo6iqtXr/K1tgGfz8fRyvj4OEe1O3bssGWyaZvUMlp3dzd3Ob9z5w4nlcRiMdvBg/mGOJ4s0I5H7yXs27ePz9Lo7Oxk3X16epoNq5Aqy91MMpnktOmxsTFb/7K1+l3px4PBIC8mqqqq2PFEo9GsHY+WSioqKji9Wzubp59+mrPXGhoabC3z9Vimpqbw9ttvA7DSpnXXi/HxcbEjF6PtbXx8nOeBsrIyXoh4PB52TsvLy7zQ0Blrq9HPHRgY4MXHnTt3OIU6H+U1E+nVJgiCIDiKRDxZoFcveqXa2NjIbetjsRhu3rwJwCpU1JvViUTCFXnzhY5ZtHvlyhWcPHkSgJUAou+XWczn9Xo5GcQ8LXRubo4jnlAoxHKGWchXWlrKsmpZWRm/TmVlJW8mNzY2crbSzp07AVgRst5U9vl8vGqdnZ3laO3DDz/E+fPnAVirXS0f5vsKt9DR0ej09LQti9Wsx9FZr2VlZSzBLS4u8vxgds/X1/F43FboXCiI48kCLaOYjkdnScViMe4DdvbsWdbjC8lY3Ew8Hmdnf+3aNXR1dQGwJC3tJEzH4/F4eCKIxWLstMLhsO1IhXRHBDc2NqKzsxOAlbqt92qamprY2dTX17Nt6K+hUMj2nlrHv3btGk6fPg0AOHfuHGeyTU9Pi8PJE7R93O9+mUeoaDswm4SaTYzNvpLaBgtpLhGpTRAEQXAUiXiyQK9CzEOf9MollUrxasc8ZsE8AVPYOsxjLkZHR7kg1GxBX1dXx7KYmQiwbds2HD58mK/1xq7ZYieVSvF1U1MTdx02I57Gxka+DoVCHGGZsoke48zMDNd6nTlzBm+88QYA6+gM3V1Yop3CQttDaWkp22EgEODH/X4/zxVm6xyduBCNRgsm6hHHkwXaGCoqrD6GpuMxJ754PM4GlC5VUtga9N93cnKSHY/f7+e+bfv37+eMRLPgdPfu3fjiF78IwMomMs88Sed4TI3elM/MCcXr9d4jv4yNjXFK7bVr13DlyhW+No/JLpTJRbBjLly1/QSDQVsvSb2fo79GIhHeM5qdnS2YxYhIbYIgCIKjSMSTIUTEdTx6Bd3W1sbRj1KKJTizFkT/n+AcZo+zixcvor29HYAVhZjn3ut7VFtby3U82XK/e6sjYC2VDA4O4qOPPgIAnD59mlurzM7O2jKXJAuyMNERttk6qby8nG0omUzaan0AKwLWtTtzc3MFYxvieDKEiFhGaW5uBmCdEKknr0gkYuvbpie4QjGUfEN/mIeGhvgMnvHxcZa69uzZgx07dgCwFgp6H2itYtPVr53uVMhIJMKTxMLCAj+u92yuXLnCVe03btzgFOpYLCaLkyJA7/WFQiHeCywvL7cdd60XK/qrWVxcSHOJSG2CIAiCo0jEsw5647iyspJrNPRqJRgMcmg8OTnJq+nh4WHuqSTkBh1BTExM8EF84+PjXOvz5JNP8krS6/VyxJPpa+sN4fn5ee4aPTAwwIWoQ0NDXPxBtMcMAAAgAElEQVSp26KMjY1xlBMOhwvqaANhfXTE4vV6ufBcKyOAvZO6/hoMBm3PKRTE8ayDNpCOjg5Ooa2pqQFghcNaRhkeHuYTLW/cuLFmDybBefQHfmpqigtLU6kU36P29nbuLlBSUsLZRz6fj3V5n8/Hj6dSKZZCxsfH+WTQnp4e7l5x584ddjxafovFYuxshOJDL1bM4uVgMMhOxjz6Wj9mdsYwOxvku+wmUpsgCILgKBLxrIOOeHbs2GE7Sx2wVjBaUpucnMTw8DAAS2YRqc19LCwsYHBwEIAlf+nox6yrWB3x6CJT89pMQFhYWODIZmZmhmsuwuHwPQd1SX1OcaOj5FQqZavzM3u16WQlHRHV1NTwfBMKhThiNovU8xFxPOugtf+2tjZOy9Up1IlEAouLiwDsjR4nJiZEUnEhiUSCFwSRSOSBz7dJJ4lo9IRiNhVd/X9CcWI6Db0oSSaTNglOL26046mtreWTcsvLy3m+yfe0e5HaBEEQBEeRiGcddOhbXV3NSQX6sUQiwRvH09PT3G1YZ7oJhYkZzeTzqlNwlvUintUn2AJWxKPnnZqaGlsbpnxWVcTxrINZKLj6XAylFGdGTUxM8P8LgiCsRjuKeDzOjieVSvF8kkql2PFoKdfsqlFXV8eL2sXFxbxe4IrUJgiCIDiKRDzroDOSpqamcPv2bQD2luW6dufOnTuSySYIwpqYMlk6qS2RSLCMr+cYU2qrrq7mYmjzoLh8JKPRE1E1gL8EcBiAAvCvAXQD+GsA7QAGAHxJKTWzJaPMIZOTkwCADz74gIsDdVZbMpm0ZbLp5xY7xWwvwoNRTDazvLxsS7E3j0VY3SvQ7FxQUlJyT2eDfCXT0f85gH9SSu0HcBRAF4BvA/iVUmoPgF+tfC8IgNiLkD1iM0XEuhEPEVUCeBrAHwCAUmoZwDIRvQjg2ZWn/QDA2wC+tRWDzCW6ODAcDnP7FPNkSbOluS4QK2aK3V6E7Ck2mzGTC1ZHPKspLS3liMfv9xdMxJOJ1LYLwASA/01ERwGcA/ANANuUUiMAoJQaIaLGdD9MRK8AeGWTxus4+tyMzs5OtLW1Afi4SSgRcWPQoaEhbhb5oIWJBUJR24vwQBSVzcTjcZbaUqkUXy8tLbHUph3T4uIiL34jkQgXkMbjcdtxCvlGJm7TB+AEgO8qpY4DWEAWIa9S6lWl1Eml1MkHHKOQX4i9CNkiNlNkZBLxDAEYUkp9uPL9T2AZxRgRNa2sRJoAjG/VIHNJY6O1yHryySfx5JNPAgCOHj0KwFppvPfeewCsEyV1VluRRzxFbS/CA1FUNrO4uMhzRE9PD0cxy8vL3Ila1wdOTk5y9/OxsTHuBZjvNYPrOh6l1CgRDRLRPqVUN4BPAbi+8u9lAN9Z+fqzLR1pjtDpjRUVFXzCqHm6qG7gV1dXV5DnZmRLsduLkD3FZjNLS0ucDXvr1i2b7KZ7Q5oNh/v6+gBYR3Do7ijJZDIvJTZNpsng/wHAD4nID6APwL+CJdP9mIi+BuAOgN/fmiEKeYjYi5AtYjNFBDnpNYloApZ+67aCl3q4b0zA5o9rp1KqYRNfb0txsb0A7rSZrRhTvtnMPKz6H7fhRnsBcjTHOOp4AICIzrptE9CNYwLcOy4ncevfwI3jcuOYnMatfwMZl538TgYXBEEQ8g5xPIIgCIKj5MLxvJqD91wPN44JcO+4nMStfwM3jsuNY3Iat/4NZFwGju/xCIIgCMWNSG2CIAiCo4jjEQRBEBzFMcdDRJ8lom4i6iWinLU3J6I2InqLiLqI6BoRfWPl8T8momEiurjy7/M5GNsAEV1Zef+zK4/VEtEbRHRz5WuN0+PKBWIvGY1N7MXADTYj9pLhWJzY4yEiL4AeAJ+B1ZfpDIAvK6Wub/mb3zuWJgBNSqnzRFQBqxPuFwB8CUBEKfVfnR6TMbYBACeVUpPGY/8FwLRS6jsrH6YapVTet4a/H2IvGY9tAGIvANxjM2IvmeFUxPMogF6lVN/KWRs/AvCiQ+9tQyk1opQ6v3I9D+vAqZZcjCVDXoR1FglWvn4hh2NxCrGXB6cY7QVwic2IvWSGU46nBcCg8f0QXHAziKgdwHEAuivu14noMhF9P0cShQLwOhGdWzljBFh1JgmAtGeSFBhiL5kh9vIxrrMZsZe1ccrxUJrHcprHTUTlAP4WwDeVUmEA3wXQCeAYgBEA/y0HwzqllDoB4HMA/j0RPZ2DMbgBsZfMEHv5GFfZjNjL/XHK8QwBaDO+bwVw16H3vgciKoFlFD9USv0UAJRSY0qppFIqBeB/wQrdHUUpdXfl6ziAv1sZw9iKbqz144I4k2QdxF4yQOzFhmtsRuxlfZxyPGcA7CGiDrLanr8E4DWH3tsGERGA7wHoUkr9qfF4k/G03wNw1eFxhVY2I0FEIQC/tTKG12CdRQIU0Jkk6yD2sv64xF7suMJmxF4yI9PzeDaEUipBRF8H8EsAXgDfV0pdc+K903AKwFcAXCGiiyuP/RGALxPRMVjh+QCAf+vwuLYB+DvLbuED8H+VUv9ERGdQZGeSiL1khNiLgYtsRuwlA6RljiAIguAo0rlAEARBcBRxPIIgCIKjiOMRBEEQHEUcjyAIguAo4ngEQRAERxHHIwiCIDiKOB5BEATBUcTxCIIgCI4ijkcQBEFwFHE8giAIgqOI4xEEQRAcRRyPIAiC4CjieARBEARHEceTAUT0f4hohIjCRNRDRP8m12MS3AsRRVb9SxLR/8j1uAR3Uoz2IsciZAARHQLQq5SKEdF+AG8D+G2l1LncjkxwOyuHbo0B+LxS6t1cj0dwN8ViLxLxZIBS6ppSKqa/XfnXmcMhCfnDF2EdJ/xergci5AVFYS/ieDKEiP4nES0CuAFgBMDPczwkIT94GcBfKZEWhMwoCnsRqS0LiMgL4HEAzwL4E6VUPLcjEtwMEe0A0A9gt1KqP9fjEdxNMdmLRDxZoJRKKqVOA2gF8O9yPR7B9XwVwOlCn0SETaNo7EUcz4Phg+zxCOvzVQA/yPUghLyhaOxFHM86EFEjEb1EROVE5CWi5wF8GcCbuR6b4F6I6AkALQD+JtdjEdxPsdmLL9cDyAMULFntL2A56tsAvqmU+llORyW4nZcB/FQpNZ/rgQh5QVHZiyQXCIIgCI4iUpsgCILgKBtyPET0WSLqJqJeIvr2Zg1KKFzEZoRsEHspTB5YalupaekB8BkAQwDOAPiyUur65g1PKCTEZoRsEHspXDYS8TwKq39Zn1JqGcCPALy4OcMSChSxGSEbxF4KlI1ktbUAGDS+HwLw2P1+gIgkkyHHKKUoh2+flc2IvbiCSaVUQ47eW+aYPCSTOWYjjifdi99z04noFQCvbOB9hMJhXZsRe3Edt3P43jLHFCgbcTxDANqM71sB3F39JKXUqwBeBWQ1IqxvM2IvgoHMMQXKRvZ4zgDYQ0QdROQH8BKA1zZnWEKBIjYjZIPYS4HywBGPUipBRF8H8EsAXgDfV0pd27SRCQWH2IyQDWIvhYujnQskDM49OU4uyAqxF1dwTil1MteDyBSxmdyTyRwjnQsEQRAERxHHIwiCIDiKOB5BEATBUcTxCIIgCI4ijkcQBEFwFHE8giAIgqOI4xEEQRAcRRyPIAiC4CjieARBEARHEccjCIIgOMpGulPnPR6P5Xf9fj9KSkoAACUlJfD5rD8LESGZTAIAUqkUUqkUX6d7LN21UgpOtiUSBEFwO0XteLSDKSsrQ2VlJQCgqqqKr30+n82ZmE4IAJLJ5Iau9eslEgksLy8DABYXFxGPx7fwtxYEQcgtIrUJgiAIjpL3EY/X6wVgyWZaOjPx+Xwso/n9fgQCAQBAIBBAKBQCAFRXV6O6uhoAUFNTg9raWn5OIpEAYEUl+tqMVMzH1oqO9PXqiEf/bDQaxfz8PADg9u3bmJqa2oS/jCAIgjvJa8dDRCyX+f1+dkJEH3flrqioYKdSW1uL5uZmAEBzczPq6ur4ORUVFQCAyspKvi4pKbE5DfNafzUdz3rXiURiTXltenoaAFBaWoquri4AwPLyMstu8XicnZYgCIKJ3+8HAASDQZSWlvK13l9OJBJYWloCACwsLPDckytEahMEQRAcxdURj/bifr+fr81opqSkxBap6OekUin29FVVVaivrwcAbN++Hfv27QMA7N27F42NjQCs5AK9SggEAizHeb1em3ymX1N/XUtGM7PaMkkuMKW2hx56CN3d3QCAyclJTExMAACuXbuGO3fuPPDfUhAEd6JVG5/Px9fl5eUALOlfz3FlZWUoKytLe623DczHA4EARkdHAQBDQ0Po6+sDAPT29mJyctKJX21NXO149B+zvr6enYfphMrLy9HW1gYAaGtr42w0ImIHFQgE+EaUl5fz/k1tbS2/fklJiS2dWl+n2zPSr6/ZjFTpVCrFctyePXtw8qR14OPdu3cxODgIwJLdxPFsHH3vgsEg32fz729Koxu5t16vl6VfjZlaby5OhMLH6/WiqqoKgDWf6cXtWnNPZ2cnAODAgQPYvn07APsCubS01Capmdf6tYPBIHp7ewEAly5dwunTpwEAU1NTOXc8IrUJgiAIjuKqiMfMTCMiXiG0tbVh165dAKwoyIyE9u/fDwDYvXs3ampq7nkd/Vr6q37c6/WmfdyMlpyCiHh1XFFRwWMJBAIccn/iE5/AzMwMAODOnTs5X7HkK1rKqKqq4qQTpRTLnrFYjDdhY7GYLULRmJFLMplMW3dVUlJik2zNn139GqFQCMFgEIC1Caxfb3l5mTeBo9Go1HflMV6vlyOXw4cP89y2VsRz4sQJAMBjjz2G1tZWANY8YdqPtlmlFNvY6ihbzx+mUqS/5hJXOZ5t27aho6MDgBVq6hvV1NSEbdu2AbDvwYRCITQ1NQGwpDP9R17twDTmjcuE1dLIakwntdooVr/vetfaYEKhkC2c1vruY489xhLQm2++KY5nHbS8GgwG+W9oTvCVlZW8UInFYlhcXARgZfzMzc0BAD+m0U7L6/Xy4qeiooIfj0ajiEajAOwdMLTDiEaj/JqJRIIngObmZrb11ROKvvZ4PGwj0WiU9/6mpqY4/T4SifDzBXcRCATQ3t4OADh27BjvL5vlHqbj2blzJwDLvtLNGbFYDJFIBIBlJ+Yej4n52m5yPCK1CYIgCI6ybsRDRG0A/grAdgApAK8qpf6ciGoB/DWAdgADAL6klJrZyGBaWlrwzDPPAACee+4520pVe3Rz09bn89k20sw6ns2Qy5RSLHUsLy/fU0Dq8Xh49VBSUsLPjcVittcxC1j1tc/nSztGU3YLBoMcue3du5ffa3BwEOfOnQOQPhLLJU7ay1p4PB62ndraWuzevRsAsG/fPo4sgsEg29TCwgJHItPT0xgfH+drHcGY95SI+HU6Ojo4qSUWi3F0Y24EmxHPwsICAHt0UlNTwzVlZrRs2kttbS2vkiORCHp6egAAN27cwIULFwAAt27d4t9DjzsfcIPNbBZmAtO2bdtYJuvo6MCBAwcAWGqOlnnNTDbzWtuvtqHVLC8vc2Qej8d5nigtLbWpNNpuZ2dn2a5XR/K5IBOpLQHgPyqlzhNRBYBzRPQGgD8A8Cul1HeI6NsAvg3gWxsZTFVVFU8SR44c4T+6eUNWy1sbYXV6tEa/rul45ufn+cOsv3q9Xp50ysrKOCU6HA7zpEJE/HtUVlbyZGfuK62Fz+djJ9Ta2sqZfR999BE/7jbHAwftZS28Xi876YqKCt4ffPbZZ/HQQw8BsO6FXrREIhGWLaanp1nGmpmZ4Q/p0tKSTQLTE0NDQwNLvKlUiu20vr6eZWA9luXlZbaRubk53ksCPs6gjMfjNtvRj9fV1aGlpYWfoye08vJyll3Hx8dtTi6PyLnNbBa1tbXsYI4cOYJjx44BsOQ1Le2GQiG2CZ/PZ5sHtKPQ989c/CwtLbHNTE9Pc9G5z+fDoUOHAFiLGD1nLSwscDr1jRs3cOnSJQDAxMSEbY7LBetKbUqpEaXU+ZXreQBdAFoAvAjgBytP+wGAL2zVIIX8QexFyBaxmeIjq+QCImoHcBzAhwC2KaVGAMtwiKhxjZ95BcArGb4+SwuBQMBWT7PZUU4ymeTVhSmvJBIJfo9EIsGrycnJSYTDYQDg1bHP5+NVbV1dHa+Ux8fHWZbzeDycGLF//37s2LEDgLUy0b/fWpgrITNhIhAIcMTj5kynrbaXtfB4PBx5mEXGdXV1fG1m+dTW1rI80tTUxCvGeDzO9mIWE5uyq8fjsdmUprS0lBMZ9L0qLS1liUXXn6VDv47ZMsnn8/HreTwelt0qKipsm8ZOZ2RuNrmymc2isbERBw8eBACcPHkSR48eBWDJa9omAdhsSUc0Q0NDuHHjBgCrZyNgyWJanl1YWODrRCLBdtXQ0MBZcu3t7Wy/s7OzGBkZAWBFPNevX9+i3zp7MnY8RFQO4G8BfFMpFc7UwJVSrwJ4deU11o3rzP0Q/YEyw8GNfrDMzgH6Jt66dQvnz58HYDkV/R7Ly8u4e/cuAKuYU6czawfk9XpZ/mhqasLw8DA/V08YRMRSz2/91m+x8Zmp0pkwPz+PgYEBAJaBuh2n7GUtzPOQtB2ZjmctPB4P22A8HmepLZlM2iZ4M1XaTGXViwOfz7emhKvHtdb/mY4qncYfCATQ0NAAwJJtTCebz+TaZrJF28CePXtw/PhxAJa8tnfvXgBWZpqW11aj7//S0hJLZpcuXcLf//3fAwA++OADAJbjMeVe/XPl5eWc+Xbo0CHMzs4CsGxWL4rm5uZ4H0g/5hYyymojohJYBvFDpdRPVx4eI6Kmlf9vAjC+NUMU8g2xFyFbxGaKi0yy2gjA9wB0KaX+1Piv1wC8DOA7K19/ttHBmD3L5ufneRWYrv2IJl1BlVmEt7S0ZNv0N7PU9CrhypUruHjxIr+vJpFI2OoltMRmbgrr/x8cHOTXC4fDvBqqrq5+4OJUs5XL6Ogozpw5A8CK0Nx6qqmT9rIWqVTKlqV29epVAMA//MM/cHfyUCjEEad5XV5ebksW0FHx0tKSLZvSPI7DjHjSyWT6q1LKloVpvqeObDKxD/M4jtHRUZaJ87XI1A028yDoSPPo0aN46aWXAFgtbnTCUWlpKUfJq4uItZ3MzMzY2tpoW12vPZbH4+H3qaurYxnW7AEZDodtkZCeh9yQkJSJ1HYKwFcAXCGiiyuP/REsY/gxEX0NwB0Av7/RwSwtLXEx3OTkJP+hysrK1nQ8pnSmnUo4HOYQc3BwENeuXQMAXL58mZ1GMplkbXViYoL3csx24alUypZOqz/UppavHVU8Hrd1H9CZLU8//TSH4R0dHbwnZMpsaxW2xmIx/j2uXLmCf/zHfwQAXL161XWhs4Fj9rIWpsNIpVL8YZ6bm7NlIaZruFhaWsrXS0tL7MCIKK1zMo/UqKio4Pedn59nadYs8NQ27fF4bI5Pyybt7e2832TKaIB970fb3cjICOv4kUgkLx0PXGAz2WIuENra2vjzrgvg02Gev6UXsdevX8c777wDAPjwww8zLgw3i0a3bdvGCxelFM+Js7OzLMvPz8+7KhN2XcejlDoNYK1l2Kc2dzhCviP2ImSL2Ezx4aqWOYuLiyxdDQ8P2zy0uVI0MaUzvdocGxvjjf5Lly7hzTffBGBt2G32AUh6hZlKpbgIsLGxkTtM/87v/A5nuZhtVFaTTmJZWlri5IZLly7xhqMOn4X0pFIpW3KHXkXGYjHegF+dFGBKZ3r1OD8/z5GFx+NJ24beLG4OhUL8OkopjpZ11LqwsGArQtYyTFlZGR555BEer1lAatqLjr6Ghoa4gPTmzZu2wkAXR8IFgb6/ZWVlHD37/f6MIk0daSwuLnIU3NXVxfPT5cuXM75/qVSK5bW6ujq2QeDjyPju3bucNDUyMuIqed5Vjmd6ehqXL18GYM8Yq6ur40l9teMxi6u04xkeHuYJu7+/n48W2Kw+VmZas1mZrOWSnTt34siRIwAsJ2QWv+qbv9rRpDOK6elp7lDQ09Mjk0qGKKVssqj+uy0uLqZ1Nqsdj76/8/PznMF4v7+92VtPTwZ+v/8eTd28xyUlJSypmbZQVlaWts8g8PGC49atW9ytoLe3lx+XPm1bjymn6zKJYDCYkXyl78/4+DjvKV+9epUX29l8vuPxOO8X1tTU2PpUakZHR9nxmA1v3YD0ahMEQRAcxVURTzgcRldXFwAruUDnwFdXV3PhndfrtXlus42NliLGx8d5FTE3N8fP2eimml6BmkkP+liGT37yk9izZw8AK+LRdRaVlZW2HnIasyfXWiuR6elpzmTr7u7O+Tnp+YjZr2p1Dz2TdLU4ZrR0P3TCipntuB5m4ef27ds5ou/s7OSV7Oouwnqj+Ny5c7ySHR4evu/vJWwcbQ9+v5/npD179rCEvmvXrnu6QqdDzz8DAwN44403AFj38kGk81Qqxe9ZU1PDNjMzM8Ntckx5zU3RDuAyxxOLxViPn5ubYwnD1NRXOx49McTjcVtfNZ01slF5Sn/4S0tLWdPVxtfW1oZTp04BAB5++GEOvWtra9koSkpK1k2RNTNR5ufnWf+9cuUK+vv7AVgyixuyUQoV8whzJwiFQiwl79u3z9bRYq0uHbdu3QIA/OY3v8HNmzcBgIsPha1DZy22tLRwL8kTJ07wmTl79+7lhfH90A5scHAQ7777LgBwUXgm+Hw+7tfY2trKC93Gxka2lZ6eHpbnb9265dpOFiK1CYIgCI7iqohHKWU77948FVJHMKvlKh0FpFIpfn48Ht+06EBv2jU1NfFBTnrVs3//fhw+fBiAFXqbUY65Wb0eZqHoxMQEbzyeO3eOezbNzc3J5nEBUV9fzwkojz32GNvW/YqMdcJMX18fKwN51oU6L9Gf6/b2djz88MMAgCeeeIKvzbY4SimWPs16wnA4zPfs/PnzDySPer1eLoA+ceIEt+bZtm0bzzN9fX34xS9+AcAqQnXrnOE6x5NO8lj94dpqvdLsmKCbMR48eJBb6pvnapgnoK5V5JoOs7vCzMwMxsbGAFjy2ocffgjASqHWUorZsFLIf6qrq7nYsL29nSWUtZyOeZ7K6Ogoy8punVgKCX1PAoGArfHrWqcS6/3lq1evcmPOwcFBXkR2dXWxQ8oGc1/HlPPN/oKxWIztZH5+3rVzhkhtgiAIgqO4KuJZCye9ttfr5aSGUCjEUc6nP/1pzmLRGWs1NTW86sh2E29paYlbqnR1dXH22oULF7jFy/j4OGfqSQ1PYVFRUcF1PMFgMG1n6VgsxgWs3d3dXBQdjUYl0cRBtDKxuv/eWkWjWqU4c+YMfv7znwOwIh6dbfag9y6RSNgK5vV1IpHgeSgej9t6Rrp13sgLx+MkZg+tbdu2seM5deoUH29gVr8/qFOcmZlhB/Ob3/wG77//PgCrIFDLbpI+XRiYnQh02mtjYyMfn202kzRZWFhgZ3Pt2jXuyaaUcq2EUoiYjkenzJu9+Px+PxcaT01NsVR++fJllt0247hpszDalN5TqRTbldnpwq1OBxCpTRAEQXAYiXhWQUScJGCuUM2+WZuRG3/r1i38+Mc/BmBfzbo5PBYeDG03oVCIo5ydO3dyBB0KhdJKbdPT0+jr6wNgSW169SzRjrPoDLSpqSk+rqCsrIwTO8bGxriu6sqVK9wNv7+/nzf6l5eXN0Ue1ZJaZ2cnZ9dWVFTwPGX2DnQzEvEIgiAIjiIRTxrMdGqzK/ZazRsfhL6+Prz++usArCQCWcUWLmYXYZ1CbR6LXFJSkrbea2xsDN3d3QAse9F7CmIrzqL3Ws3zbTweDzclrq+v504Ep0+fzvhMnQdBRzxmxFxaWsr2Yx5q6GbE8azC3LhNJBIcZpuhcroTRbOdDGpqariztdlZW+oyCgdtG1r6aG5u5qK/1tZWTijweDxsP9FolCe6mzdv4sqVKwCsYkBdRC04i3mip5bEzfZelZWVLMFtRUKQnm+CwSD39Kuvr2cHs7i4yAlJk5OTebEwEalNEARBcBSJeFahlOJVy8LCAufEm12u9UaeKY9kK79VVVVxY8ipqSmOrCTiKQxMmVY3jz148CAOHToEwGo4aSYU6DTZSCTCq+quri4+d2dmZkba4+SYhYUFVj3C4TCnuvt8Pp4nsulQnilaGXnooYfw2GOPAbAUEx3ZTE9P88GAQ0NDeVGGIY5nFalUim/c4uIiG9TMzAzLYRqfz5e27bjphFb33tLPa2lpwfPPPw/A0mv1YXVDQ0Mcts/MzNgONBPyB5/Px1Kazj567rnnuN2SbrWk0TYXDoe5Y3F/f79N2pGi0dwSjUbZ+TspZ2lbefLJJ/H4448DsOrA9MJmZmaGj5Pp6+vbEue32YjUJgiCIDiKRDyrMLvLzs3NcSRy69YtbuSoG4eudbCbKbOY1ybNzc341Kc+BQA4cuQIr2wvX76Mf/7nfwYA3LhxgzeUzdVuPmweFiu6BqyyshJVVVUArPN2AGvFqtstrUavpGdmZjjiHRsb49WrRDu5J1efO53J1tzczN2pdaYkYHVR0NmP3d3d3EXBzWTseIjIC+AsgGGl1AtE1AHgRwBqAZwH8BWllPvFxXUw93ji8Ti3or916xY7HG2AgUCAnYp5RIPH47GdaKkLCM3H/X4/h9BVVVXs1Kqrqzlzpaenh7VbU3bJh+ymYrGX1Wh5rbGxkQ/q0mmv9zssTPf36u/vR29vLwArzb6YiomL1WbWQ88rpaWlnIKv95kBa0tAHxLY3d29Zg85N5GN1PYNAF3G938C4L8rpfYAmAHwtc0cmJD3iL0I2SI2UyRkFPEQUSuA3wbw/wH4f8jSlJ4D8C9WnvIDAH8M4LtbMEbHMWUN3abk8uXLLMHpyKe+vp5XF7FYjKOfiooKPia7srKSV7o1NTW8YikrK+Mu2BUVFXxdV1fHG9Dj4+M4ffo0AOCtt95i2V/AD0EAACAASURBVMXtEU+x2YuGiPi+d3Z24uTJkwDARaPp2uJodDR7/fp1br+iC0aLgWK1mUzQUa/f7+easEQiwZLa9PQ0Jz7lQ7QDZC61/RmA/wygYuX7OgCzSimtAwwBaEn3g0T0CoBXNjLIXKKz2q5evcrZRqZ0pmW5WCzGMktDQwP35GpubuZ0yF27dmHnzp0ArEwVHS6b3Ym1ngtYhYfa2Xm9Xn7OhQsXWAJ0qZ5bVPaS7uDAEydO4KmnngIATps3UUrxvk4sFmON/sKFC3xgmNsXGJtMUdnMeuhFSmlpqW1O0MzOzvJe4ODgYN6l2q8rtRHRCwDGlVLnzIfTPDXtzptS6lWl1Eml1MkHHKOQR4i9CNkiNlN8ZBLxnALwu0T0eQBBAJWwVifVRORbWZG0Ari7dcPMHXolEY1GudPsWuhVSiQSYZlkZGSEpZOrV69yEsHjjz/OWW1aZltNMBjkDeqqqip+XiAQ4N5QLox4is5e9Iq0vLycEwmOHj3KxaLp7m8ymeRD3kZHR7k1ztmzZ/nsFh3tFgFFZzProdWN2tpalm+JiLcB5ubmOPHo1q1bm3Lej5Os63iUUn8I4A8BgIieBfCflFL/koj+BsAXYWWdvAzgZ1s4zrxAa7Gzs7Msk2hJDLAck3ZOi4uLOHHiBICP94xW4/V6UVFhKQ8+nw/Hjh0DYE1a2pnptvluoRjtRe/htbW1Yf/+/QCsomDtkMzDuTThcBjXr18HYO0f3rhxA4C9YWyxpM0Xo82sh3lgoE6hDoVCPMeYJ43OzMzkRbcCk40UkH4L1iZgLyw99nubMyShQBF7EbJFbKZAyaqAVCn1NoC3V677ADy6+UPKX/QKda3z2D0eD69+w+HwuhkoZoFqMBjk7Ci/389HKriZYrAXj8fDEev+/fu5+3RTU1Pa46w1Y2NjOHv2LADr6HOdUFDshaLFYDOZoOXZjo4Olmzr6ups0bCWaicnJ1mWJaK8iJSlc4GDpFIpdjYLCwsZpT6aGVO6DXpDQwNLcEJuCQaDLIUcP36cFwfr3Z+7d+/ySZXd3d2Ympra2oEKeYWW5FtbW/HQQw8BgK3rRSKRwNzcHACr5CPf9nikV5sgCILgKBLxbAFma5yysjIuGq2srOQMlVOnTqG2tpZ/JpsD5cw2PILzBAIB7sPW0tLCUshDDz3ENVtmLy2N2c9vcHCQI56hoaG86CgsbC0ej4cjHR0xNzQ0sJTr8Xg4i3VmZoaj5LGxsXs657sdmb02iHYA5pGzwWCQK4wPHz7MFezNzc3sbHbu3MnntJisPtdHO6JUKsWT0927d0WaySGVlZV81MGRI0fwyCOPALCager7no7r16+jv78fgLWvoxvQLi4uyrEXAvx+Py9odL/Guro6fszj8bC8NjMzwyegrlfm4UZEahMEQRAcRSKeDaIzl+rr63mTuaqqims7XnjhBTzzzDMArHoPHUqnOyoBsEtt5hEN8/PzuHr1KgDgnXfe4boPwTl0NFpdXY3Ozk4Alrymi3wbGhrSHvqnZZALFy7grbfeAmAVE+s6jGLPZBMsSkpKWJbXEU91dTXLtolEgusDZ2dnuXbH6/XmXcQsjidDiIiLuvTXYDCIlharfdTBgwdZfqmvr2dJ7eDBg1xIqPd9ALver79fjVKK5bWxsTFuGPraa69haGhoU38/4f4QES8W6urqOG26s7OTJwnztFkzg3FsbAyAdeSBrjYfGxvLm4aOgjN4vV7eA9bzh9/vZ6didruYnZ1l+/H5fHnneERqEwRBEBxFIp4V9GrWzCwJBAK8AmlpaWF5RW8gh0Ihltd2796NtrY2ANbms85KKS8v5whptbymo5zVCQXm/+skguvXr7O8NjQ0lHdZLPmKvjetra3ccfyRRx7h9kU7d+609dLSxONxlkXMU2z1td4kFgQNEdlq9QBLVTGLRrVNjY6OcvSzlmzvZsTxwLpxeq8mEAjwza+qqmJn8sgjj+Dxxx8H8HGqY2lpKU86NTU1/HM+n88mq20EfR7QxYsXuXW+ZLQ5h1407NmzB6dOnQIAHDt2DA8//DAA+8LCZHp6mk+F/OijjwBYjkfv6xRRA1AhQ5RSvKjVjsfMkkwmk9z78fz583ydbzIbIFKbIAiC4DBFHfGY7Wh0b6Rt27ZxEWBLSwtnLB0/fhz79u0D8HE7C6/XazvMTUc5a0lna5FMJvn4hXA4zO0vZmdn8c477wCwVs2jo6MP9osKD4xece7cuZPrsXbt2sUr0rXu9djYGBeIXr58GQBw+/btYjvcTciCZDLJ84lOLgiFQjYb04kqly5d4qNX8jFJpWgdj9mws6amho8oePbZZ/nEyNraWj4/p6GhgQu59M95vV7b3tB6DieVSnFYHI1GWecfGxtjWaarq8tWJKbPaenv75dJKwdoKXX37t04cuQIAEtqTXevlVKsx4+MjKCrqwsA+KRIrckLQjri8ThL/nquMW0tlUrx3u7S0lJeOhyNSG2CIAiCoxRtxGPKZA0NDXjyyScBAF/60pe4N1IgELhvFLNeHc5qzLY309PT6O3tBWCFzW+//TYAa9NQFxSaK5xoNJoX7c4LDV2DVVlZyYV86ZIJAEsq0RHt7du3WWobGRkB8PFptoKgISJWUILBIBee60LS0tJStptIJML2FQgE8jqCLlrHYxYEmoV/i4uLthbj6Zo9ZoNSiiuMw+EwhoeHAQC9vb24ePEiAGsPQJ8oOj8/b3M8+sRBcTq5QTueiooKm72Y6PsVi8W4b9bNmzdZatOLh3yWRoStwTxluL6+nouRteMpLy/nxqDhcJjnEr3PnK+I1CYIgiA4StFGPMlkkmspTNnrypUr3A6ltbU1bcSjw914PM4rkGg0yjKaeb20tMQR1OjoKNfi3Lhxg6OcwcFBXhXn29nphY6+j11dXdxN/KGHHuKkk+XlZZY8xsbGuCVOd3e3ZCEK62IWjTY1Nd1Tv+Pz+bj2y7SpfO/vV7SOJ5VK8SQ/OzvL7eovXbrEjiUWi3GYu9qZ6K/6OhKJsPNYWFjga/PxoaEh9PX1AbB0f/3aUkzoXvSH/sKFC5zi2tHRwfLI9PQ039MLFy7g0qVLAMCPCcL9ICJe0Bw9epQL1vU+olKK5dtLly5xhmS+L1BFahMEQRAcpWgjHrPmIh6Pc2FWV1cXS2ODg4O8yWdGMekim2g0ypFLLBZLex0Oh7ndTSQS4cQBwb1oGa2vr4+TC6anp9kukskkr0h7e3sxMDAAID8P5xKcJ5VKcULBwYMHsXPnTgDggnYiwvT0NACgp6eH2+Tke8STkeMhomoAfwngMAAF4F8D6Abw1wDaAQwA+JJSamZLRrnFJBIJPs1PKcVOqLq6mrVW04Hom768vJzWwSwvL9uek+66kLPUCsletBw6NzfHTuXXv/417/2Z2UWmPchR1tlRSDaTDYlEgueY7du3s5yr5f75+XlerE5NTeV1twKTTKW2PwfwT0qp/QCOAugC8G0Av1JK7QHwq5XvBQEQexGyR2ymiFg34iGiSgBPA/gDAFBKLQNYJqIXATy78rQfAHgbwLe2YpBbjVKKJTPzemJigou7YrGYLXIB7Flt8Xhcam5QWPbi8Xg4y6i2tpY3fpubm1mCm5ub4wh5YmKCI518zzpykkKymQdB14WFQiHOcNORcyQSsUU8hXJqbSZS2y4AEwD+NxEdBXAOwDcAbFNKjQCAUmqEiBrT/TARvQLglU0a75aglOLq4Hg8zvKKUsp2+p+QEQVjL4FAgBvGHj58GJ/97GcBAJ/73Odw+/ZtANY+4HvvvQcAOHPmDGezDQ0N5f3k4CAFYzMPgl6olpaWcrak3meemJjgRU4ikSgYm8pEavMBOAHgu0qp4wAWkEXIq5R6VSl1Uil18gHHKOQXYi9CtojNFBmZRDxDAIaUUh+ufP8TWEYxRkRNKyuRJgB5ncYjkc2mUTD2UlVVhaNHjwIAnn/+eRw4cACA1UZHy27l5eUcLXu9Xpba9EmjQkYUjM1kis5aq66u5t6QZqKKtqmpqSmW/rM9bsXNrOt4lFKjRDRIRPuUUt0APgXg+sq/lwF8Z+Xrz7Z0pEJeUEj2UlNTg+PHjwMAnnnmGc4+8ng8LIkEg0Hb8Rq6P5ssYDKnkGwmE4iIO1/s378f7e3tACxnpFP29R7P1NQUS22FtHecaR3PfwDwQyLyA+gD8K9gyXQ/JqKvAbgD4Pe3ZohCHiL2ImSL2EwRQU56USKagKXfTjr2pplRD/eNCdj8ce1USjVs4uttKS62F8CdNrMVY8o3m5mHVf/jNtxoL0CO5hhHHQ8AENFZt20CunFMgHvH5SRu/Ru4cVxuHJPTuPVvIOOyI73aBEEQBEcRxyMIgiA4Si4cz6s5eM/1cOOYAPeOy0nc+jdw47jcOCancevfQMZl4PgejyAIglDciNQmCIIgOIo4HkEQBMFRHHM8RPRZIuomol4iyll7cyJqI6K3iKiLiK4R0TdWHv9jIhomoosr/z6fg7ENENGVlfc/u/JYLRG9QUQ3V77WOD2uXCD2ktHYxF4M3GAzYi8ZjsWJPR4i8gLoAfAZWH2ZzgD4slLq+pa/+b1jaQLQpJQ6T0QVsDrhfgHAlwBElFL/1ekxGWMbAHBSKTVpPPZfAEwrpb6z8mGqUUoVXGt4E7GXjMc2ALEXAO6xGbGXzHAq4nkUQK9Sqm/lrI0fAXjRofe2oZQaUUqdX7meh3XgVEsuxpIhL8I6iwQrX7+Qw7E4hdjLg1OM9gK4xGbEXjLDKcfTAsBs1zsEF9wMImoHcByA7or7dSK6TETfz5FEoQC8TkTnVs4YAVadSQIg7ZkkBYbYS2aIvXyM62xG7GVtnHI86fp55zSPm4jKAfwtgG8qpcIAvgugE8AxACMA/lsOhnVKKXUCwOcA/HsiejoHY3ADYi+ZIfbyMa6yGbGX++OU4xkC0GZ83wrgrkPvfQ9EVALLKH6olPopACilxpRSSaVUCsD/ghW6O4pS6u7K13EAf7cyhrEV3VjrxwVzJsl9EHvJALEXG66xGbGX9XHK8ZwBsIeIOshqe/4SgNccem8bREQAvgegSyn1p8bjTcbTfg/AVYfHFVrZjAQRhQD81soYXoN1FglQQGeSrIPYy/rjEnux4wqbEXvJjEzP49kQSqkEEX0dwC8BeAF8Xyl1zYn3TsMpAF8BcIWILq489kcAvkxEx2CF5wMA/q3D49oG4O8su4UPwP9VSv0TEZ1BkZ1JIvaSEWIvBi6yGbGXDJCWOYIgCIKjSOcCQRAEwVHE8QiCIAiOIo5HEARBcBRxPIIgCIKjiOMRBEEQHEUcjyAIguAo4ngEQRAERxHHIwiCIDiKOB5BEATBUcTxCIIgCI4ijkcQBEFwFHE8giAIgqOI4xEEQRAcRRzPOhBRZNW/JBH9j1yPS3AvYjNCNhSjvThyHk8+o5Qq19crByiNAfib3I1IcDtiM0I2FKO9SMSTHV+EdTTse7keiJA3iM0I2VAU9iKOJzteBvBXSk7PEzJHbEbIhqKwFzmBNEOIaAeAfgC7lVL9uR6P4H7EZoRsKCZ7kYgnc74K4HShG4SwqYjNCNlQNPYijidzvgrgB7kehJBXiM0I2VA09iJSWwYQ0RMA3gCwXSk1n+vxCO5HbEbIhmKzF4l4MuNlAD8tBoMQNg2xGSEbispeJOIRBEEQHEUiHkEQBMFRxPEIgiAIjrIhx0NEnyWibiLqJaJvb9aghMJFbEbIBrGX/7+9M4ttNLvy+/9yp0RSFLUVVVJJtai6q7uqN1e1G+12Y7xMkHQGsQeYGYwxMJzEQOchHniABLHHT3nIgxMkk8xTgM7YgR8cOONZbD9Nx2iMHwwUGl3dXV2bqkpSaV8oUgtFcV9uHj6dU/dTSS2qS6L4kecHFPSJoshb+g7vOed/zz23NfnMazxKKTeAhwB+F8ACgA8AfENrfe/ohie0EmIzwmEQe2ldnqZJ6KsAJrXWjwBAKfUzAF8DsK9RKKWkkuGE0VqrE3z7Q9mM2EtTkNJa953Qe8sc40DqmWOeRmo7DWDe+H5h5zEbSqm3lVI3lFI3nuK9hNbgQJsRe2k6Zk/wvWWOaVGeJuPZy6s9EW1ord8B8A4g0YhwsM2IvQgGMse0KE+T8SwAGDa+HwKw9HTDEVocsRnhMIi9tChP43g+ADCmlDqrlPIB+GMAvzqaYQktitiMcBjEXlqUzyy1aa0rSqnvAHgXgBvAj7XWd49sZELLITYjHAaxl9aloS1zRH89eU64qu1QiL00BR9qra+e9CDqRWzm5DnuqjZBEARBODTieARBEISGIo5HEARBaCjieARBEISGIo5HEARBaCjieARBEISGIo5HEARBaCjieARBEISGIo5HEARBaCjieARBEISGIo5HEARBaCjieARBEISGIo5HEARBaChPcwKpILQlSikoZTXgdbvdcLms+M3j8cDr9QIAvF4vP4c6wNdqNVSrVQBAtVrl6/1ee/fv1mo1vqbHtdZ7Xgutgdvths/nAwAEAgEEAgEAgM/ng8djTd/5fB6ZTAYAUCwWbTbWrIjjeUpoouns7ERXVxcAyyhoMtre3sb6+joAy0AE50L3VCnFH/pQKITOzk4AQFdXF+LxOADg1KlTCAaDAGCbCLa3twEAW1tbPFmYDsvv9/PrdXR0sLMpFAooFAoAgFwux7ZULpdtYzQdz17X5XIZpVIJAJDNZpFOpwFYdipO62hxuVxsM2aA4nK5OLhwuVxsS16vFx0dHQCAcDgMwLKpnp4eAEBvby9CoRAAa46h+/XgwQOMj48DAJLJJNtJMzsekdoEQRCEhiIZzwGYsgdFLBR19Pb24uzZswCAK1eu4LnnngNgZTZbW1sAgDt37uD9998HAMzOztpkEcFZ7BWlhkIhjkjj8TguX74MwLKH/v5+AOAMo1QqIZlMAgAWFxexvLwMAAgGg5wdhcNh/r2BgQFUKhUAQDqdxubmJgBgfX0dGxsbAPbPonfbF32fzWaRy+UAWNExZVTZbHbf3xU+HZfLBb/fD8C6lySNmZmsmfEopWx/Y5LPAoEAuru7AQCDg4MAgOHhYZ5jLl68iN7eXn5tssef//znbBtbW1tPZMHNSFs5HrfbzTfZ5XLZNHO6rlQqNmdD1+ZjdPOvXLmCL37xiwCAr371q7hy5QoA6wO9srICwJJc6IO+vb3N17lcTj7gTY4plSilbBIYXQ8MDGBgYAAAMDQ0hEuXLgEAPve5z/GEQdJHoVDA4uIiAMuGaJIxXy8ajWJoaAgAcPbsWXY8q6urWFtbAwAkEgmsrq4CANvTQZCtZTIZlvgCgQBLf2trayzN0HsK+2OuxblcLr5/3d3dHJj6/X6eb0zHA9hlsEgkAsAedIyMjACwHM/w8DBfkxRXKBQ46HC5XHx/zTWeZkakNkEQBKGhtGzGY0YjlPr29fVhdHQUgBVxkByWTqdtmQhlP0opTqFDoRBHJi+++CIA4NVXX8VLL70EwIpg6fcikQin2ABYXllbW+OId3l5GcVi8Rj+58JRYVYU+Xw+LhyIx+N83d/fz1FqPB7nSDUUCrENkiwXCAQQi8UAWFk2Ra8+n4/trKOjA9FolN+fXiMSiXDE7Pf7+Tkk4x0ERcSFQoHtrqOjgyW7RCLBEbRkPPtjziuUzQSDQZw+fRqAlfXSPQ6FQrZ7bEpwZqEBZUtmgVJfXx8AIBaL2TJjsiVTgXG73Tz3FItFR9y/lnQ8Sim43W4A9sljaGgIn//85wFYH95EIgEA2NjYsKWnZtpMN900AJJChoaGWN83Jxq/388Tid/vZ93//v37LHOkUilxPE2O2+22afckqV28eJEltYGBAZtDItsJh8M8uZg6v2kvpOObkp7H42F7NYMXl8vFEk5PTw9PLjTh7MacmExMWRkApqamAFg2T5+BfD7fljKwWV1GTsUMOE1JNBQKsZOIRqMcfPT19fHjHR0dvHZnzgk+n89Wdm+u8ZjX9HNaszHvabVa5UDBvFeVSmVfm2gmDpTalFLDSql/VEqNK6XuKqW+u/N4TCn1a6XUxM7X7uMfrtDsiL0Ih0Vspv2oJ+OpAPh3WuuPlFJhAB8qpX4N4F8CeE9r/UOl1PcBfB/A945vqAdjShEUpQwMDPAi77Vr11gm6+7uttW7UwQSCAQ4soxEInzd2dnJEct+lWl7RZl+v59r8iORCL+GGc22GI6xFxOziISuw+EwSx69vb04d+4cAOD8+fMYGxsDYEltlAnRV4KyEqoYy2aznOWWSiWWyUw7crlcHCUHg0G2F7NCKhgMcnRuLljXg7lxtaOjg19z98bVBtMUNkN/966uLi4gGhwcxJkzZwBYqgfJaH19fTh16hQA4PTp05zlhEIhfh23281ZiSlxFgoFlva3tra4ujGbze6ZxdB7Dg8Pc3adyWRYJi0Wi/z8arXqiGz1QMejtV4GsLxznVFKjQM4DeBrAH5n52k/AfAbnPBEQmnw4OAgnn32WQDA1atX8frrrwOwKkXoJgYCAf7w0lfC3C1OaW6lUuGJglLZ3RIJfYjNycCUSEKh0L7v2So4yV5MzE4EdG9isRguXLgAwJLXaAI6c+YMTwBdXV080eyG7IUq0FZWVngzsVldZkojHo+HJ73+/n5ey+no6GA7CgQCvHZQr+MhmzbtuFwu2yS7kyr1bxabISff09PDa8GXLl1iWdWU0cLhMN93n8/HDiORSPDacSaT4Q26+Xye/+6m48lmsxyYmFWvphRHY7l27RpLtdlslkuoTcfjBJkNOOQaj1JqFMDLAN4HMLBjMNBaLyul+vf5nbcBvP10wxSciNiLcFjEZtqDuh2PUioE4G8B/JnWeqvetFxr/Q6Ad3Ze41hDKVqQGxkZwWuvvQYA+NKXvsT7a/aLTE201rY2JRSNFAoFlijo5z6fj4sYzFYnNA56PVosdrvdjqg4OQqcYC8mZgUaRZvRaNQm09J+ip6eHpbgzJ5Zu6FsmfbfzM7OclVjKpVCKpUCYK8i8/l8XBmXy+VYzolGo7ZMZK8ChHool8scnZsVUNVq9cSj5UbbjFm12tHRwX/3K1eu4IUXXgBgyVtUsRYOh/n5breb54bl5WW+x6urq5zhrq2t8ePZbJYznmKxyL+bz+dtEhzZDMnz4XCYbXNsbIzvt8fjsc09ZA+7N6c2K3U5HqWUF5ZB/FRr/Xc7DyeUUvGdSCQOYPW4BvlpmOs6NBlcunQJr7zyCgBLrjiMDl6r1dhAkskkZmdnAQBLS0ucNlOKGw6HOfUdGhriNYDBwUGbbGFOEmRYWmse10l/4I+aZrYXk702h3Z3d7OcMjIyws5maGiIq9DM/mymbWmteULJ5XLsWMiGJicnsbCwAMCqpCTZzayo9Hg8/BqpVIorKUOhEE9G0WiUH+/p6bFtYN1vsiZ73Nra4vL+VCrFslAul6u7NPs4OAmbMUuZzS4k165dw5e//GUA1rosyZpm2XKpVOJN4o8ePcLc3BwAywmR49nY2OA5o1Ao2Dasm3KnKefv7jpgOrhiscjOZnfQa84xJ3kf66WeqjYF4EcAxrXWf2H86FcAvrVz/S0Avzz64QlOQ+xFOCxiM+1HPRnPFwB8E8BtpdTNncd+AOCHAP5aKfVtAHMA/vB4hvjpkKzV29vLi3DPPfecbUGQooHdUKSZy+W4QmR5eZkj1ImJCTx8+BAAMDMzY1s0BOyLzy+//DK/T39/P0cjtVqNo+JiscjRUAvvlWhqeyFcLpdtrxdVQQ4NDbHkcuHCBd6z1d3dzdHxbnsy7yPZRjKZZFltZmYGgLVnZn5+np9H9mRWIrlcLs5IpqenOdru7Ozk676+Pt43cubMGbbjwcFB/j/txmyZQzZoZjzZbPYkM+8TsRmtNc8f4XCYM0pzo6ZZLLC+vs73xmyLlUqlOHtNp9NsA/l83tZF3Mx49joew9xjZWY2ZsZD0qjX6+V7qpSy7RlzQsZTT1XbbwHsJ7Z+5WiHc3hI8xwYGMD58+cBWB9GqgD6NA2cbtDy8jJu374NwGrq+fHHHwOwNnySNLJXT6z19XWeDEZHR9lASqUSG3SlUrE5HuqN1aq92prdXkzow+31em0VkRS0PPPMM1zJ1tPTw87JLD3effQA2UAymWTboUBmamqKr3O5HNvUp034tC4ZiUT4/c+cOcMTUCgU4tfZrzFotVplCWd9fZ1loZWVFZ4kT7Kx5EnZjFm1WiwWWRq9d+8e/+1SqRQ7m0QiwaXPy8vLNgdDWzNKpRK/5tP0TDOrDc21IXqfWq1mK80n9gs8mg3p1SYIgiA0FMdvJqGU+PTp01xQEI/H69pgR5HCzMwMbty4AQC4desWHj16BMCqUPm0KrRSqcQRiFmPXywWufpld7drikhaMdtxEi6Xy7YhkzKeWCzGRQSDg4O8iO/3+/e0pXK5bOs+TbLanTt3cP/+fQCPM56NjQ1+bqVSqcsGaIyRSITlteHhYd7AeuHCBS6q2V1YQBG3WUW1srLCbXIWFhY4am9X6PNdKpW4aGhqaoqv19bWWJpMJpNcpba+vn6s0qS5L4cyGzPj0VrbHqd77ZSMx/GOh/7gPT09LJHE4/F9JTa6cYlEgieG3/72t/jggw8AWLo66bXZbPZT02WzNDWXy9lKJEkWMUuylVK2Umvh5FBK8VqNuTnT3LV+6tSpAyXbcrlsO1WU5LXx8XF8+OGHAMCVTel02tYtox7HQwFUOBzmcQ0PD+PixYsALDmQNkXvdjxm40jT8UxOTgIA5ufn297xUPC5tbXFf69MJsOSWi6X47/R9vZ2w08R9nq9bKcul4tlvFqtxmtMExMTe1ZINjMitQmCIAgNxfEZj3nImlkltF8aTOnpxMQE3n33XQDAjRs3uHptc3PzwM6/hCmz5HI5jnzz+TxHn6VSiSvmqtXqvhV2QmPZnfFQkUhXVxdLV8PDw3seBGhWH5kVYpB9dgAAFb5JREFUkaurq1y1NjU1hU8++eSpx0mZVigUsnVApuve3l4uQNid8ZA9Tk9PY3p6msdF0fzm5mbbd0g3F+spKzSPo9i9z4YyiuOWyknW7ezsZLk3GAzy+5bLZZYAJycnuTDCCaePAi3geChVNncJmyWKWus9N9Ulk0mMj48DsLRucmCHbbJH7+92u3kCMHu4lUolNpBMJnOSjRgFA6217cRNklDS6TTfr4WFBdtZKcTm5ibLZ0tLS7yuMzc3x/ItTQRPiym1kbOJxWJc+hsMBveVAUkimpqa4krNiYkJdjwbGxtt73goyGy2jiI0TwSDQZZYSfYlKLBYX1/na6dsRhepTRAEQWgojs94iFQqxTLHxsaG7ZgDihq11lxtVigUbDX55qLdYSC5ZnR0lDtiRyIR2x6KRlXCCPVTq9U4yjUX3zc2NtguYrEYt0Ryu92cxa6trWFpaQmAlU1QljM+Ps6/e1QZD1UpmRmPuaeIJEKC/k/lcpntbmZmBnfu3AFgFRSY/cOE5oQynlAoxL3iYrGYbS4je9Ra17080Cy0lOMxy0RNbdxs4mhKYGY54meFHIy5gdXv99vkOkqD19bWbKcJSkn1yWF+WEul0p6OJxwO26rQyHbm5uZs/ddoffD+/fsswX1WrV0pxdJKLBbj/n8XLlzgzhz9/f1POByCnEoikeBxzc7O2nrEOWUdoJ0hB9LZ2Wk7Zt1cIzbL+8mWnTKniNQmCIIgNJSWyXjS6TRnPNevX+cKt1KpxK3lt7e3OSJdXV09koV+KkpIp9McdZgZj8fj4ag5mUzytWQ8J4vZLsWsQpyfn+doM5lM8iJ+KBTiCNPszZVIJHg/RT6ftx0zcBjM6jmqqhsbG2P59tlnn+W+gLFYzFbsYEIVdjMzM7xfZ3Z2ljM08+RToXkhO/J4PFxUEAwGbZuVzRNsRWo7IbLZLO7evQvAmjAePHgAAHj99ddx9epVANaEQZPE7OzsU38AlVIs0aTTab7u7Oxkp+Lz+dgY0uk0y3ridE4e84gK+uDOz8/z5P3o0SPbSZBELpfjKjizY0U2m7W9Zr0opWwNS03Hc/nyZQCW1EZHNJibkM1eXqVSideeHjx4wGtPy8vLtr5wYnvNj1nCbTYDpUrFbDZrKwV3muMRqU0QBEFoKC2T8dRqNSQSCQBWWxBaTM1kMtx3ycx45ubmWHY7LCSLdHZ28kJwT08PP272SwoGg1zcUKlUbL2WhJNlrz0c2WyW76N5dIK5N8vMGswW94fJJswO1x0dHXz4XFdXFxcRnDt3jg8n6+3t3bMPV6VS4Sg4nU7besWZCgBFwmJ3zsA8KI7uncvl4sx1a2uL7U4p5ZhMh5CMRxAEQWgoLZPxAHZ9k7KZjz/+mBdWC4UCR4emTn9YKIMZGRnBm2++CQC4fPkyl7h6PB4eSzAYZE3ePDpXaE7MjMC8V7VazXYGj3m0uXldL+a6jrk7fXBw0HYQHXWh3q9DgZnxrK2t8Vk79+7d4707gnMxO+CbB76ZLZ8+7cyxZqWlHI8JyScrKyssrx0VNCHF43Fcu3YNgLWB1KyxN89mpwokqpASmpu9nMpBz60Xcl4ej4ftpaurizcJPvPMM7x3Jx6Pc+dpk3K5zFJMMpnkQwxv3brF11R1JzibQqHAAbLL5bLJwLQZ3on9H0VqEwRBEBpKy2Y8xwkt6pXLZZtcYu7FMA/hIqltd5M/ofk56sV4s6CAMuB4PM7n61y7do2LC6gr8W7K5TIvMieTSZbU3nvvPW4bRR0MBGdjHv62W2pzcsYjjucpKBQKtgo3s+qI9uskEgmRPYQnCAQCXMl26tQpdjxvvPEG94cztXtT9svlctxBe3Z2llvj3Llz5zNXagrNiel4TKnN6Y5HpDZBEAShodSd8Sil3ABuAFjUWv+eUuosgJ8BiAH4CMA3tdYt3YuD0lyKRD0eD2c5gUCAr7XWvPi7uLjIe4ra6ewTsZfHkN2YBQXd3d3c/DEej3OW09nZuW+RCtmU2W369u3bLK85vfmn2MyT7Jba2rG44LsAxo3v/zOA/661HgOwAeDbRzmwZsTr9cLr9SISiSASiaCzs5Mfo+MXPB4Pb+iic9EnJiYwMTGBTCZj24jY4rS9vRBkI7RRlE45HRoa4n/RaBTRaPQJ2yCJjY5uyGazWFhYwM2bN3Hz5k1cv34dMzMzmJmZaYUebGIzO5CTqVQqHHRUq1W43W4+osPv98Pv98Pn87GNOYW6ZkCl1BCAfw7gr3a+VwC+DOBvdp7yEwBfP44BCs5D7EU4LGIz7UW9Utv/APAfANBGlB4Am1pr6jWyAOD0Xr+olHobwNtPM8hmgaJR2ijqdrttFW5mxGEeGnbv3j0AjzsHtwFiLwa04TgYDHIlW19fHzf9HBoa4go2c5NxrVbj6rWlpSUsLi4CsA6ce/ToEQCrAShtIHX45mSxGTyueiTZvlqtsoRaLBZtLZxMqY3kNqfIrQc6HqXU7wFY1Vp/qJT6HXp4j6fuWXeqtX4HwDs7r+XoRlG7P9jmrmJzoyjweLLJZDJ8REM+n2/5XlliLxamHk/O5syZM1wqPTY2xsccnDlzhjeKut1unjwKhQKXRY+Pj+OTTz7ha1rXSafTtvb4TkRs5jEUsO7Vl69WqyEYDAKw5hdyPH6/31EyG1BfxvMFAP9CKfUWgACACKzoJKqU8uxEJEMAlo5vmIKDEHsRDovYTJtxoOPRWv85gD8HgJ1o5N9rrf9EKfVzAH8Aq+rkWwB+eYzjbApIVqOohIoL6Gf080wmw7KIuYenWq06NiqtF7EXC7OzNe3XGR0dxSuvvMLX1Hl6aGjI1uePWqTk83mWZycmJnD9+nUAwPT0ND9OMpuTEZt5DNkM2UMgEGAZze128+Ner3dPqc0pPM0G0u8B+JlS6j8B+BjAj45mSM0LOQ3S4y9fvozBwUEA1kRDUlwqlcL4uFWck0gkbJp9G9MW9mKWu5ql04AlqT3//PMArGagZDtUSk2QU5mamuIDDScnJ5FKpQBYJ+m2QAVbPbSFzZiYnVAAu+PxeDy2AMU8PJCkfadwqNFqrX8D4Dc7148AvHr0QxJaBbEX4bCIzbQHznKTJwxJaRR1nD9/HkNDQwCsdNfcQLrXsQhCa2O2MRkcHOSO088++ywA62A32jQai8X2tQuzoODGjRsArIyH2uRkMhnb4XVC60GZs1nVVigUeF7J5XIss1YqlT2LEZoZcTx1YkonpNn39PSwTOLz+WydDWjtRxxP62P2zyIbicfjLKvR17GxMQ5UQqHQnvJIuVxGMpkEYDkb6lCQTCa5D1s7dcBoN3bL8rVaje83VdAC1vofnaxcKpXYBp1CW2yhFwRBEJoHyXjqxO/3IxqNAgB/7e7u5msz4nC73baz0du8qKDloUzX7/cjEokAsKQ2Oj2Uvg4ODvLRGC6XiyPZfD7PMtrKygof5jY/P8+y2/b2Nku9Quuy+zTb3RkPzTOFQoEznkKh4LhqWXE8deJ2u1kyI33dNAQTpRTrrysrK2w4SinHGYhwMCS99vb2cqXa6OgoH2FNHQqi0Shr8eVymcumNzc3MT09DQB48OABd7qYm5tj2a1UKsm6ThtwkNRG9lMsFtnxZLNZxwUlIrUJgiAIDUUynjqpVqsccVKksbm5yfspzA1cXq+XH08mkxyxuFwux0UmwsGQfBaPx3H+/HkAwNmzZ3Hu3DkA4K9mMUE+n+eMJ51O89EZDx484D5sqVSKF5TNg+CE1sWU2Oh7M+MhGyqVStje3gbgzIxHHE+d7OV4NjY2kM1mAdgdTzAYZN0/m83aJg/B+ZgNGgOBAJdInz9/Hs899xwAYGRkhDeO7lW9ViqV2HY2NjawsrICwDprh/qwbW5uyvpgm2E6HPqegthisch2p7XmeSWfzzvO8YjUJgiCIDQUyXjqxMx4qGJtfX2d012KbgEr4zGjXKd3DxbsBINB9PX1AQD6+/t5g+jzzz/PvdgGBga4wm0vKpUKZzzpdBqJRAIA8OjRIy40ENqP3VLb7uIC86BAmldyuRzPTU4pYBLHUye1Wo3TWTKKra0tdjymFru9vc1psMfjEbmkxfD7/ex4zp07h4sXLwIAXnjhBbzxxhtPPJ/ufz6f56Blbm4OU1NTAKwGoLTGQz8X2htyHi6Xi68rlQrPK52dnRzYhMNhfjyTyThCdhOpTRAEQWgokvHUiZm+0gJftVrlyiQAfL2yssIyilnJ5oQUWNgbr9fLBSSxWIxb31y8eJGr1np6evgem/u7zArH5eVlAFY7HNqvMz4+jtnZWQCS8QgWJNV3dHTYTjwmVSUajeLUqVMArNNsae5ZX193RMYjjuczQH3YlFK2m0zX+XyeDcfv94vDcTCkqYdCIZY24vE4bw69dOkSdyYg+W03pL+vra1hbm4OAPDw4UN2PHfv3uUNx2Y/LqF9Mc/loZONvV4v21IkEmF76+3t5Q4XHo/HEb38RGoTBEEQGopkPIeA5BOKNEZGRvjApmq1amtlbnaqFpwL3dNgMIhYLAbAqlgbHR0FYB15QNfmXq5KpcLt7Gnf18LCAu7fvw/Aktqoki2TyXCU6gSZRGgcXq+XW3W5XC7OeHw+H88tbrebMySnHAjnjFE2CVSdRI1BL1y4wLvWa7Ua33SPx8OPh8Nhflx6bTkD+kD7fD6WOUZGRrgrgelswuEwP9/lcrGjMneW0/HnCwsLePjwIYAnz9fZXTEptDckz3s8HnY8breb7cQ87nr3aaROQKQ2QRAEoaFIxnMIdh/IlMvlWE6pVCoc+Zrt8SORCEcsFAELzQ3dr2g0yhuDz58/j5deegkA8OKLL3IX6u7ubluxCVEqlfjgNqpkm5+f5+q15eVlrnwkGxIEgjIet9vNVbTmnkCv18v2VqvV9qymbGbE8RwCs6kjYG8SWqlUeMIql8tYXFwEYPXhEpwFfdC7urrQ398PwNooSieJvvDCC/wc+gpYgQkFJ9PT0xgfHwdgVa0BwP3795FKpQBYQQvZjiDsxlzrM6VccjDm2nG1WuXnO8XxiNQmCIIgNJS6Mh6lVBTAXwG4DEAD+NcAHgD4vwBGAcwA+COtdUuH9xRtUNQ6PT2N06dPA7BkGSoo2Nzc5Ch3YWGh7YoKnGYvLpeLC0C8Xi9nOWNjY9xt+uLFi/y43++3RaHm/i2SU+fm5nDz5k0AwPXr1wFYG4vpYDeS2QQLp9nMcWPOGWbREs1BZgVlqVTi7NkpxSn1Sm1/CeAftNZ/oJTyAegA8AMA72mtf6iU+j6A7wP43jGNs6kgxzM5Ocla/8DAAHp7ewEAi4uL3OhxbW2tHUtkHWUvbreby+I7OjowMDAAwHI2r732GgDrFFF6PBAIcLm8Uorlta2tLa5Um5ubw8zMDADYuhKIvLYvjrKZ44bmDLfbzQFtKBRix1OtVjl42dzcxNbWFgDnrBceKLUppSIA3gTwIwDQWpe01psAvgbgJztP+wmArx/XIAXnIPYiHBaxmfajnoznHIAkgP+tlHoRwIcAvgtgQGu9DABa62WlVP/xDbO5oA2B9+7d495at27d4kq2jz76CJOTkwCsSrY2y3gcZy9m5VA4HGZJbXR0FJcuXQJg9Wej57hcLtvhXLRPZ3p6mk8PNffpkPxWKpXazRbqxXE2c9yQ1ObxeHheiUajvE9Ha20rdqI5yQntcoD6HI8HwCsA/lRr/b5S6i9hpbx1oZR6G8Dbn3F8TQmluJOTkyyn7G4Yau4gJlmmTSYdx9mL1+tFT08PAEtSo3W77u5udjZer9e2Oc88AoP6ZM3OznL/tampKT5VlPqwCfviOJs5bkgyU0qxDBwKhXgNp1gscvBTLpcd1/minqq2BQALWuv3d77/G1hGklBKxQFg5+vqXr+stX5Ha31Va331KAYsND1iL8JhEZtpMw7MeLTWK0qpeaXUM1rrBwC+AuDezr9vAfjhztdfHutImwiKOgqFAi/8DQ8PcwXUqVOnuDXKzZs3WY7L5XKOqTr5rDjRXrxeL28UHR4exvDwMADrmAPam+X3+217JCjC3NzcZEltcXHRtkFUNgzXhxNt5rggdcSU1Mw9OrR5PZ/Pc1aktWZpzinzS71VbX8K4Kc71SaPAPwrWNnSXyulvg1gDsAfHs8Qm5doNMprAG+++SbeeustAFar/F/84hcArNSX5Ljl5eV2OW/FEfZCjiQQCHBF4vDwMJ+1E4vF9uxKADyWW1OpFHcmWFpa4pNEl5aWpGT6cDjCZo4TpRQ7HLI78+Rj8zyedDrNTqhSqThGYiPqcjxa65sA9kpjv3K0wxFaAbEX4bCIzbQXqpGHlCmlkgCyAFINe9P66EXzjQk4+nGNaK33Pq2sCWliewGa02aOY0xOs5kMrI2nzUYz2gtwQnNMQx0PACilbjTbImAzjglo3nE1kmb9GzTjuJpxTI2mWf8GMi470qtNEARBaCjieARBEISGchKO550TeM+DaMYxAc07rkbSrH+DZhxXM46p0TTr30DGZdDwNR5BEAShvRGpTRAEQWgo4ngEQRCEhtIwx6OU+qdKqQdKqcmdszVOBKXUsFLqH5VS40qpu0qp7+48/h+VUotKqZs7/946gbHNKKVu77z/jZ3HYkqpXyulJna+djd6XCeB2EtdYxN7MWgGmxF7qXMsjVjjUUq5ATwE8LuwGgJ+AOAbWut7x/7mT44lDiCutf5IKRWG1YL96wD+CMC21vq/NnpMxthmAFzVWqeMx/4LgHXjMKxurXVLH4Yl9lL32GYg9gKgeWxG7KU+GpXxvApgUmv9SGtdAvAzWIc8NRyt9bLW+qOd6wyAcQCnT2IsddKOh2GJvXx22tFegCaxGbGX+miU4zkNYN74fgFNcDOUUqMAXgZA7di/o5S6pZT68QlJFBrA/1NKfaisM0aAXYdhAWiHw7DEXupD7OUxTWczYi/70yjHo/Z47ETruJVSIQB/C+DPtNZbAP4ngPMAXgKwDOC/ncCwvqC1fgXAPwPwb5VSb57AGJoBsZf6EHt5TFPZjNjLp9Mox7MAYNj4fgjAUoPe+wmUUl5YRvFTrfXfAYDWOqG1rmqtawD+F6zUvaForZd2vq4C+PudMdR1GFaLIfZSB2IvNprGZsReDqZRjucDAGNKqbPKOm/jjwH8qkHvbUMppQD8CMC41vovjMfjxtN+H8CdBo+rc2cxEkqpTgD/ZGcMv4J1CBbQJodhQeylnnGJvdhpCpsRe6mPeg+Ceyq01hWl1HcAvAvADeDHWuu7jXjvPfgCgG8CuK2Uurnz2A8AfEMp9RKs9HwGwL9p8LgGAPy9ZbfwAPg/Wut/UEp9gDY6DAsQe6kTsReDJrIZsZc6kJY5giAIQkORzgWCIAhCQxHHIwiCIDQUcTyCIAhCQxHHIwiCIDQUcTyCIAhCQxHHIwiCIDQUcTyCIAhCQ/n/AJO00bejxD8AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(3, figsize=(6,6), hide_axis=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at another example from [`vision.data`](/vision.data.html#vision.data) with the planet dataset. This time, it's a multiclassification problem with the labels in a csv file and no given split between valid and train data, so we use a random split. The factory method is:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "planet = untar_data(URLs.PLANET_TINY)\n", "planet_tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_csv(planet, folder='train', size=128, suffix='.jpg', label_delim = ' ', ds_tfms=planet_tfms)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the data block API we can rewrite this like that:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = (ImageList.from_csv(planet, 'labels.csv', folder='train', suffix='.jpg')\n", " #Where to find the data? -> in planet 'train' folder\n", " .split_by_rand_pct()\n", " #How to split in train/valid? -> randomly with the default 20% in valid\n", " .label_from_df(label_delim=' ')\n", " #How to label? -> use the csv file\n", " .transform(planet_tfms, size=128)\n", " #Data augmentation? -> use tfms with a size of 128\n", " .databunch()) \n", " #Finally -> use the defaults for conversion to databunch" ] }, { "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=2, figsize=(9,7))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data block API also allows you to get your data together in problems for which there is no direct [`ImageDataBunch`](/vision.data.html#ImageDataBunch) factory method. For a segmentation task, for instance, we can use it to quickly get a [`DataBunch`](/basic_data.html#DataBunch). Let's take the example of the [camvid dataset](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/). The images are in an 'images' folder and their corresponding mask is in a 'labels' folder." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "camvid = untar_data(URLs.CAMVID_TINY)\n", "path_lbl = camvid/'labels'\n", "path_img = camvid/'images'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have a file that gives us the names of the classes (what each code inside the masks corresponds to: a pedestrian, a tree, a road...)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Animal', 'Archway', 'Bicyclist', 'Bridge', 'Building', 'Car', 'CartLuggagePram', 'Child', 'Column_Pole',\n", " 'Fence', 'LaneMkgsDriv', 'LaneMkgsNonDriv', 'Misc_Text', 'MotorcycleScooter', 'OtherMoving', 'ParkingBlock',\n", " 'Pedestrian', 'Road', 'RoadShoulder', 'Sidewalk', 'SignSymbol', 'Sky', 'SUVPickupTruck', 'TrafficCone',\n", " 'TrafficLight', 'Train', 'Tree', 'Truck_Bus', 'Tunnel', 'VegetationMisc', 'Void', 'Wall'], dtype='" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(rows=2, figsize=(7,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another example for object detection. We use our tiny sample of the [COCO dataset](http://cocodataset.org/#home) here. There is a helper function in the library that reads the annotation file and returns the list of images names with the list of labelled bboxes associated to it. We convert it to a dictionary that maps image names with their bboxes and then write the function that will give us the target for each image filename." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "coco = untar_data(URLs.COCO_TINY)\n", "images, lbl_bbox = get_annotations(coco/'train.json')\n", "img2bbox = dict(zip(images, lbl_bbox))\n", "get_y_func = lambda o:img2bbox[o.name]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code is very similar to what we saw before. The only new addition is the use of a special function to collate the samples in batches. This comes from the fact that our images may have multiple bounding boxes, so we need to pad them to the largest number of bounding boxes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = (ObjectItemList.from_folder(coco)\n", " #Where are the images? -> in coco\n", " .split_by_rand_pct() \n", " #How to split in train/valid? -> randomly with the default 20% in valid\n", " .label_from_func(get_y_func)\n", " #How to find the labels? -> use get_y_func\n", " .transform(get_transforms(), tfm_y=True)\n", " #Data augmentation? -> Standard transforms with tfm_y=True\n", " .databunch(bs=16, collate_fn=bb_pad_collate)) \n", " #Finally we convert to a DataBunch and we use bb_pad_collate" ] }, { "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.show_batch(rows=2, ds_type=DatasetType.Valid, figsize=(6,6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But vision isn't the only application where the data block API works. It can also be used for text and tabular data. With our sample of the IMDB dataset (labelled texts in a csv file), here is how to get the data together for a language model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from fastai.text import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imdb = untar_data(URLs.IMDB_SAMPLE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_lm = (TextList.from_csv(imdb, 'texts.csv', cols='text')\n", " #Where are the inputs? Column 'text' of this csv\n", " .split_by_rand_pct()\n", " #How to split it? Randomly with the default 20%\n", " .label_for_lm()\n", " #Label it for a language model\n", " .databunch())" ] }, { "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", "
idxtext
0! ! ! xxmaj finally this was directed by the guy who did xxmaj big xxmaj xxunk ? xxmaj must be a replay of xxmaj jonestown - hollywood style . xxmaj xxunk ! xxbos xxmaj this is a extremely well - made film . xxmaj the acting , script and camera - work are all first - rate . xxmaj the music is good , too , though it is
1us into the hearts of these two xxunk , and it is indeed a grand xxunk for the audience as well as the two principals . xxmaj the imagery throughout is impressive , especially the final scenes in xxmaj xxunk . xxmaj it xxunk for me once again how much different the world can be , but also at the same time , how similar . xxmaj the same was
2acting xxunk this episode , with a touching performance by xxmaj xxunk xxmaj xxunk as a woman xxunk to the xxmaj ice xxmaj age , and xxmaj ian xxmaj wolfe as the xxunk xxmaj librarian . xxmaj somewhat reminiscent of the classic episode xxmaj city xxmaj on xxmaj the xxmaj edge of xxmaj forever , this time travel story is a rich and compelling finale to the series , which
3it seems positively silly . i have no sympathy for people who have neglected to read one of the xxunk works in xxmaj english literature , so let 's get right to the chase . xxmaj the aliens are destroyed through catching an xxmaj earth disease , against which they have no xxunk . xxmaj if that 's a spoiler , so be it ; after a book and 3
4.. and of course xxmaj andrew xxmaj davis directed it ... xxmaj xxunk xxmaj xxunk gives a great performance for his first film ... the storyline is very cool and interesting ... there 's humor , heart and intensity ... it is very similar to the book .. i find this film to be not the least bit boring ... i absolutely loved it ... and i encourage anyone to
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_lm.show_batch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a classification problem, we just have to change the way labelling is done. Here we use the csv column `label`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_clas = (TextList.from_csv(imdb, 'texts.csv', cols='text')\n", " .split_from_df(col='is_valid')\n", " .label_from_df(cols='label')\n", " .databunch())" ] }, { "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", "
texttarget
xxbos xxmaj raising xxmaj victor xxmaj vargas : a xxmaj review \\n\\n xxmaj you know , xxmaj raising xxmaj victor xxmaj vargas is like sticking your hands into a big , xxunk bowl of xxunk . xxmaj it 's warm and gooey , but you 're not sure if it feels right . xxmaj try as i might , no matter how warm and gooey xxmaj raising xxmaj victor xxmajnegative
xxbos xxup the xxup shop xxup around xxup the xxup corner is one of the xxunk and most feel - good romantic comedies ever made . xxmaj there 's just no getting around that , and it 's hard to actually put one 's feeling for this film into words . xxmaj it 's not one of those films that tries too hard , nor does it come up withpositive
xxbos xxmaj now that xxmaj che(2008 ) has finished its relatively short xxmaj australian cinema run ( extremely limited xxunk screen in xxmaj xxunk , after xxunk ) , i can xxunk join both xxunk of \" xxmaj at xxmaj the xxmaj movies \" in taking xxmaj steven xxmaj soderbergh to task . \\n\\n xxmaj it 's usually satisfying to watch a film director change his style / subject ,negative
xxbos xxmaj this film sat on my xxmaj xxunk for weeks before i watched it . i xxunk a self - indulgent xxunk flick about relationships gone bad . i was wrong ; this was an xxunk xxunk into the screwed - up xxunk of xxmaj new xxmaj xxunk . \\n\\n xxmaj the format is the same as xxmaj max xxmaj xxunk ' \" xxmaj la xxmaj xxunk , \"positive
xxbos xxmaj many neglect that this is n't just a classic due to the fact that it 's the first xxup 3d game , or even the first xxunk - up . xxmaj it 's also one of the first xxunk games , one of the xxunk definitely the first ) truly claustrophobic games , and just a pretty well - xxunk gaming experience in general . xxmaj with graphicspositive
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_clas.show_batch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, for tabular data, we just have to pass the name of our categorical and continuous variables as an extra argument. We also add some [`PreProcessor`](/data_block.html#PreProcessor)s that are going to be applied to our data once the splitting and labelling is done." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from fastai.tabular import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "adult = untar_data(URLs.ADULT_SAMPLE)\n", "df = pd.read_csv(adult/'adult.csv')\n", "dep_var = 'salary'\n", "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']\n", "cont_names = ['education-num', 'hours-per-week', 'age', 'capital-loss', 'fnlwgt', 'capital-gain']\n", "procs = [FillMissing, Categorify, Normalize]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = (TabularList.from_df(df, path=adult, cat_names=cat_names, cont_names=cont_names, procs=procs)\n", " .split_by_idx(valid_idx=range(800,1000))\n", " .label_from_df(cols=dep_var)\n", " .databunch())" ] }, { "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", " \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", " \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", "
workclasseducationmarital-statusoccupationrelationshipracesexnative-countryeducation-num_naeducation-numhours-per-weekagecapital-lossfnlwgtcapital-gaintarget
Private HS-grad Never-married Craft-repair Unmarried Asian-Pac-Islander Male VietnamFalse-0.4224-0.0356-0.6294-0.21640.7476-0.1459<50k
Private 9th Married-civ-spouse Farming-fishing Wife White Female United-StatesFalse-1.98690.1264-0.5561-0.21641.9847-0.1459<50k
Private Some-college Married-civ-spouse Transport-moving Husband White Male United-StatesFalse-0.0312-0.03560.3968-0.21640.1973-0.1459<50k
Self-emp-not-inc Bachelors Married-civ-spouse Prof-specialty Husband White Male United-StatesFalse1.1422-0.03561.7894-0.2164-0.6119-0.1459>=50k
? HS-grad Never-married ? Own-child Other Female United-StatesFalse-0.4224-0.0356-1.5090-0.21641.8018-0.1459<50k
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.show_batch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Provide inputs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The basic class to get your inputs into is the following one. It's also the same class that will contain all of your labels (hence the name [`ItemList`](/data_block.html#ItemList))." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ItemList[source][test]

\n", "\n", "> ItemList(**`items`**:`Iterator`\\[`T_co`\\], **`path`**:`PathOrStr`=***`'.'`***, **`label_cls`**:`Callable`=***`None`***, **`inner_df`**:`Any`=***`None`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **`x`**:`ItemList`=***`None`***, **`ignore_empty`**:`bool`=***`False`***)\n", "\n", "
×

Tests found for ItemList:

Related tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category [source]
  • pytest -sv tests/test_data_block.py::test_splitdata_datasets [source]
  • pytest -sv tests/test_data_block.py::test_split_subsets [source]
  • pytest -sv tests/test_data_block.py::test_regression [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_multi_category [source]

To run tests please refer to this guide.

\n", "\n", "A collection of items with `__len__` and `__getitem__` with `ndarray` indexing semantics. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "This class regroups the inputs for our model in `items` and saves a `path` attribute which is where it will look for any files (image files, csv file with labels...). `create_func` is applied to `items` to get the final output. `label_cls` will be called to create the labels from the result of the label function, `xtra` contains additional information (usually an underlying dataframe) and `processor` is to be applied to the inputs after the splitting and labelling." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It has multiple subclasses depending on the type of data you're handling. Here is a quick list:\n", " - [`CategoryList`](/data_block.html#CategoryList) for labels in classification\n", " - [`MultiCategoryList`](/data_block.html#MultiCategoryList) for labels in a multi classification problem\n", " - [`FloatList`](/data_block.html#FloatList) for float labels in a regression problem\n", " - [`ImageList`](/vision.data.html#ImageList) for data that are images\n", " - [`SegmentationItemList`](/vision.data.html#SegmentationItemList) like [`ImageList`](/vision.data.html#ImageList) but will default labels to [`SegmentationLabelList`](/vision.data.html#SegmentationLabelList)\n", " - [`SegmentationLabelList`](/vision.data.html#SegmentationLabelList) for segmentation masks\n", " - [`ObjectItemList`](/vision.data.html#ObjectItemList) like [`ImageList`](/vision.data.html#ImageList) but will default labels to `ObjectLabelList`\n", " - `ObjectLabelList` for object detection\n", " - [`PointsItemList`](/vision.data.html#PointsItemList) for points (of the type [`ImagePoints`](/vision.image.html#ImagePoints))\n", " - [`ImageImageList`](/vision.data.html#ImageImageList) for image to image tasks\n", " - [`TextList`](/text.data.html#TextList) for text data\n", " - [`TextList`](/text.data.html#TextList) for text data stored in files\n", " - [`TabularList`](/tabular.data.html#TabularList) for tabular data\n", " - [`CollabList`](/collab.html#CollabList) for collaborative filtering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have selected the class that is suitable, you can instantiate it with one of the following factory methods" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_folder[source][test]

\n", "\n", "> from_folder(**`path`**:`PathOrStr`, **`extensions`**:`StrList`=***`None`***, **`recurse`**:`bool`=***`True`***, **`include`**:`OptStrList`=***`None`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

Tests found for from_folder:

Related tests:

  • pytest -sv tests/test_data_block.py::test_wrong_order [source]

To run tests please refer to this guide.

\n", "\n", "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the filenames that have a suffix in `extensions`. `recurse` determines if we search subfolders. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.from_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_df[source][test]

\n", "\n", "> from_df(**`df`**:`DataFrame`, **`path`**:`PathOrStr`=***`'.'`***, **`cols`**:`IntsOrStrs`=***`0`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

Tests found for from_df:

Related tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category [source]
  • pytest -sv tests/test_data_block.py::test_regression [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_multi_category [source]

To run tests please refer to this guide.

\n", "\n", "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the inputs in the `cols` of `df`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.from_df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_csv[source][test]

\n", "\n", "> from_csv(**`path`**:`PathOrStr`, **`csv_name`**:`str`, **`cols`**:`IntsOrStrs`=***`0`***, **`delimiter`**:`str`=***`None`***, **`header`**:`str`=***`'infer'`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for from_csv. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the inputs in the `cols` of `path/csv_name` " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.from_csv)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optional step: filter your data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The factory method may have grabbed too many items. For instance, if you were searching sub folders with the `from_folder` method, you may have gotten files you don't want. To remove those, you can use one of the following methods." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

filter_by_func[source][test]

\n", "\n", "> filter_by_func(**`func`**:`Callable`) → `ItemList`\n", "\n", "
×

No tests found for filter_by_func. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Only keep elements for which `func` returns `True`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.filter_by_func)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

filter_by_folder[source][test]

\n", "\n", "> filter_by_folder(**`include`**=***`None`***, **`exclude`**=***`None`***)\n", "\n", "
×

No tests found for filter_by_folder. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Only keep filenames in `include` folder or reject the ones in `exclude`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.filter_by_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

filter_by_rand[source][test]

\n", "\n", "> filter_by_rand(**`p`**:`float`, **`seed`**:`int`=***`None`***)\n", "\n", "
×

No tests found for filter_by_rand. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Keep random sample of `items` with probability `p` and an optional `seed`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.filter_by_rand)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

to_text[source][test]

\n", "\n", "> to_text(**`fn`**:`str`)\n", "\n", "
×

No tests found for to_text. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Save `self.items` to `fn` in `self.path`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.to_text)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

use_partial_data[source][test]

\n", "\n", "> use_partial_data(**`sample_pct`**:`float`=***`0.01`***, **`seed`**:`int`=***`None`***) → `ItemList`\n", "\n", "
×

No tests found for use_partial_data. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Use only a sample of `sample_pct`of the full dataset and an optional `seed`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.use_partial_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Writing your own [`ItemList`](/data_block.html#ItemList)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First check if you can't easily customize one of the existing subclass by:\n", "- subclassing an existing one and replacing the `get` method (or the `open` method if you're dealing with images)\n", "- applying a custom `processor` (see step 4)\n", "- changing the default `label_cls` for the label creation\n", "- adding a default [`PreProcessor`](/data_block.html#PreProcessor) with the `_processor` class variable\n", "\n", "If this isn't the case and you really need to write your own class, there is a [full tutorial](/tutorial.itemlist) that explains how to proceed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

analyze_pred[source][test]

\n", "\n", "> analyze_pred(**`pred`**:`Tensor`)\n", "\n", "
×

No tests found for analyze_pred. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Called on `pred` before `reconstruct` for additional preprocessing. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.analyze_pred)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**) → `Any`\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.get)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

new[source][test]

\n", "\n", "> new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a new [`ItemList`](/data_block.html#ItemList) from `items`, keeping the same attributes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.new)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll never need to subclass this normally, just don't forget to add to `self.copy_new` the names of the arguments that needs to be copied each time `new` is called in `__init__`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**:`Tensor`, **`x`**:`Tensor`=***`None`***)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.reconstruct)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Split the data between the training and the validation set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This step is normally straightforward, you just have to pick oe of the following functions depending on what you need." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_none[source][test]

\n", "\n", "> split_none()\n", "\n", "
×

No tests found for split_none. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Don't split the data and create an empty validation set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_none)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_rand_pct[source][test]

\n", "\n", "> split_by_rand_pct(**`valid_pct`**:`float`=***`0.2`***, **`seed`**:`int`=***`None`***) → `ItemLists`\n", "\n", "
×

Tests found for split_by_rand_pct:

  • pytest -sv tests/test_data_block.py::test_splitdata_datasets [source]

Related tests:

  • pytest -sv tests/test_data_block.py::test_regression [source]

To run tests please refer to this guide.

\n", "\n", "Split the items randomly by putting `valid_pct` in the validation set, optional `seed` can be passed. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_rand_pct)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

split_subsets[source][test]

\n", "\n", "> split_subsets(**`train_size`**:`float`, **`valid_size`**:`float`, **`seed`**=***`None`***) → `ItemLists`\n", "\n", "
×

Tests found for split_subsets:

  • pytest -sv tests/test_data_block.py::test_split_subsets [source]

To run tests please refer to this guide.

\n", "\n", "Split the items into train set with size `train_size * n` and valid set with size `valid_size * n`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_subsets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function is handy if you want to work with subsets of specific sizes, e.g., you want to use 20% of the data for the validation dataset, but you only want to train on a small subset of the rest of the data: `split_subsets(train_size=0.08, valid_size=0.2)`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_files[source][test]

\n", "\n", "> split_by_files(**`valid_names`**:`ItemList`) → `ItemLists`\n", "\n", "
×

No tests found for split_by_files. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data by using the names in `valid_names` for validation. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_files)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_fname_file[source][test]

\n", "\n", "> split_by_fname_file(**`fname`**:`PathOrStr`, **`path`**:`PathOrStr`=***`None`***) → `ItemLists`\n", "\n", "
×

No tests found for split_by_fname_file. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data by using the names in `fname` for the validation set. `path` will override `self.path`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_fname_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_folder[source][test]

\n", "\n", "> split_by_folder(**`train`**:`str`=***`'train'`***, **`valid`**:`str`=***`'valid'`***) → `ItemLists`\n", "\n", "
×

Tests found for split_by_folder:

Related tests:

  • pytest -sv tests/test_data_block.py::test_wrong_order [source]

To run tests please refer to this guide.

\n", "\n", "Split the data depending on the folder (`train` or `valid`) in which the filenames are. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Note: This method looks at the folder immediately after `self.path` for `valid` and `train`.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_note(\"This method looks at the folder immediately after `self.path` for `valid` and `train`.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_idx[source][test]

\n", "\n", "> split_by_idx(**`valid_idx`**:`Collection`\\[`int`\\]) → `ItemLists`\n", "\n", "
×

Tests found for split_by_idx:

Related tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_multi_category [source]

To run tests please refer to this guide.

\n", "\n", "Split the data according to the indexes in `valid_idx`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_idx)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_idxs[source][test]

\n", "\n", "> split_by_idxs(**`train_idx`**, **`valid_idx`**)\n", "\n", "
×

No tests found for split_by_idxs. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data between `train_idx` and `valid_idx`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_idxs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_list[source][test]

\n", "\n", "> split_by_list(**`train`**, **`valid`**)\n", "\n", "
×

No tests found for split_by_list. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data between `train` and `valid`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_valid_func[source][test]

\n", "\n", "> split_by_valid_func(**`func`**:`Callable`) → `ItemLists`\n", "\n", "
×

No tests found for split_by_valid_func. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data by result of `func` (which returns `True` for validation set). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_valid_func)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_from_df[source][test]

\n", "\n", "> split_from_df(**`col`**:`IntsOrStrs`=***`2`***)\n", "\n", "
×

No tests found for split_from_df. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data from the `col` in the dataframe in `self.inner_df`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_from_df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Warning: This method assumes the data has been created from a csv file or a dataframe.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_warn(\"This method assumes the data has been created from a csv file or a dataframe.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Label the inputs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To label your inputs, use one of the following functions. Note that even if it's not in the documented arguments, you can always pass a `label_cls` that will be used to create those labels (the default is the one from your input [`ItemList`](/data_block.html#ItemList), and if there is none, it will go to [`CategoryList`](/data_block.html#CategoryList), [`MultiCategoryList`](/data_block.html#MultiCategoryList) or [`FloatList`](/data_block.html#FloatList) depending on the type of the labels). This is implemented in the following function:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_label_cls[source][test]

\n", "\n", "> get_label_cls(**`labels`**, **`label_cls`**:`Callable`=***`None`***, **`label_delim`**:`str`=***`None`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for get_label_cls. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Return `label_cls` or guess one from the first element of `labels`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.get_label_cls)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first example in these docs created labels as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path = untar_data(URLs.MNIST_TINY)\n", "ll = ImageList.from_folder(path).split_by_folder().label_from_folder().train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to save the data necessary to recreate your [`LabelList`](/data_block.html#LabelList) (not including saving the actual image/text/etc files), you can use `to_df` or `to_csv`:\n", "\n", "```python\n", "ll.train.to_csv('tmp.csv')\n", "```\n", "\n", "Or just grab a `pd.DataFrame` directly:" ] }, { "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", "
xy
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\n", "
" ], "text/plain": [ " x y\n", "0 train/3/9932.png 3\n", "1 train/3/7189.png 3\n", "2 train/3/8498.png 3\n", "3 train/3/8888.png 3\n", "4 train/3/9004.png 3" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ll.to_df().head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_empty[source][test]

\n", "\n", "> label_empty(**\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for label_empty. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Label every item with an [`EmptyLabel`](/core.html#EmptyLabel). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_empty)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_df[source][test]

\n", "\n", "> label_from_df(**`cols`**:`IntsOrStrs`=***`1`***, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**)\n", "\n", "
×

Tests found for label_from_df:

Related tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category [source]
  • pytest -sv tests/test_data_block.py::test_regression [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_multi_category [source]

To run tests please refer to this guide.

\n", "\n", "Label `self.items` from the values in `cols` in `self.inner_df`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_from_df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Warning: This method only works with data objects created with either `from_csv` or `from_df` methods.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_warn(\"This method only works with data objects created with either `from_csv` or `from_df` methods.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_const[source][test]

\n", "\n", "> label_const(**`const`**:`Any`=***`0`***, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

Tests found for label_const:

Related tests:

  • pytest -sv tests/test_data_block.py::test_splitdata_datasets [source]
  • pytest -sv tests/test_data_block.py::test_split_subsets [source]

To run tests please refer to this guide.

\n", "\n", "Label every item with `const`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_const)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_folder[source][test]

\n", "\n", "> label_from_folder(**`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

Tests found for label_from_folder:

  • pytest -sv tests/test_text_data.py::test_from_folder [source]
  • pytest -sv tests/test_text_data.py::test_filter_classes [source]

Related tests:

  • pytest -sv tests/test_data_block.py::test_wrong_order [source]

To run tests please refer to this guide.

\n", "\n", "Give a label to each filename depending on its folder. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_from_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Note: This method looks at the last subfolder in the path to determine the classes.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_note(\"This method looks at the last subfolder in the path to determine the classes.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_func[source][test]

\n", "\n", "> label_from_func(**`func`**:`Callable`, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

No tests found for label_from_func. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Apply `func` to every input to get its label. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_from_func)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_re[source][test]

\n", "\n", "> label_from_re(**`pat`**:`str`, **`full_path`**:`bool`=***`False`***, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

No tests found for label_from_re. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Apply the re in `pat` to determine the label of every filename. If `full_path`, search in the full name. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_from_re)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class CategoryList[source][test]

\n", "\n", "> CategoryList(**`items`**:`Iterator`\\[`T_co`\\], **`classes`**:`Collection`\\[`T_co`\\]=***`None`***, **`label_delim`**:`str`=***`None`***, **\\*\\*`kwargs`**) :: [`CategoryListBase`](/data_block.html#CategoryListBase)\n", "\n", "
×

No tests found for CategoryList. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Basic [`ItemList`](/data_block.html#ItemList) for single classification labels. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[`ItemList`](/data_block.html#ItemList) suitable for storing labels in `items` belonging to `classes`. If `None` are passed, `classes` will be determined by the unique different labels. `processor` will default to [`CategoryProcessor`](/data_block.html#CategoryProcessor)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class MultiCategoryList[source][test]

\n", "\n", "> MultiCategoryList(**`items`**:`Iterator`\\[`T_co`\\], **`classes`**:`Collection`\\[`T_co`\\]=***`None`***, **`label_delim`**:`str`=***`None`***, **`one_hot`**:`bool`=***`False`***, **\\*\\*`kwargs`**) :: [`CategoryListBase`](/data_block.html#CategoryListBase)\n", "\n", "
×

No tests found for MultiCategoryList. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Basic [`ItemList`](/data_block.html#ItemList) for multi-classification labels. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It will store list of labels in `items` belonging to `classes`. If `None` are passed, `classes` will be determined by the unique different labels. `sep` is used to split the content of `items` in a list of tags.\n", "\n", "If `one_hot=True`, the items contain the labels one-hot encoded. In this case, it is mandatory to pass a list of `classes` (as we can't use the different labels)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class FloatList[source][test]

\n", "\n", "> FloatList(**`items`**:`Iterator`\\[`T_co`\\], **`log`**:`bool`=***`False`***, **`classes`**:`Collection`\\[`T_co`\\]=***`None`***, **\\*\\*`kwargs`**) :: [`ItemList`](/data_block.html#ItemList)\n", "\n", "
×

No tests found for FloatList. To contribute a test please refer to this guide and this discussion.

\n", "\n", "[`ItemList`](/data_block.html#ItemList) suitable for storing the floats in items for regression. Will add a `log` if this flag is `True`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(FloatList, title_level=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class EmptyLabelList[source][test]

\n", "\n", "> EmptyLabelList(**`items`**:`Iterator`\\[`T_co`\\], **`path`**:`PathOrStr`=***`'.'`***, **`label_cls`**:`Callable`=***`None`***, **`inner_df`**:`Any`=***`None`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **`x`**:`ItemList`=***`None`***, **`ignore_empty`**:`bool`=***`False`***) :: [`ItemList`](/data_block.html#ItemList)\n", "\n", "
×

No tests found for EmptyLabelList. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Basic [`ItemList`](/data_block.html#ItemList) for dummy labels. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(EmptyLabelList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Invisible step: preprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This isn't seen here in the API, but if you passed a `processor` (or a list of them) in your initial [`ItemList`](/data_block.html#ItemList) during step 1, it will be applied here. If you didn't pass any processor, a list of them might still be created depending on what is in the `_processor` variable of your class of items (this can be a list of [`PreProcessor`](/data_block.html#PreProcessor) classes).\n", "\n", "A processor is a transformation that is applied to all the inputs once at initialization, with a state computed on the training set that is then applied without modification on the validation set (and maybe the test set). For instance, it can be processing texts to tokenize then numericalize them. In that case we want the validation set to be numericalized with exactly the same vocabulary as the training set.\n", "\n", "Another example is in tabular data, where we fill missing values with (for instance) the median computed on the training set. That statistic is stored in the inner state of the [`PreProcessor`](/data_block.html#PreProcessor) and applied on the validation set.\n", "\n", "This is the generic class for all processors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class PreProcessor[source][test]

\n", "\n", "> PreProcessor(**`ds`**:`Collection`\\[`T_co`\\]=***`None`***)\n", "\n", "
×

No tests found for PreProcessor. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Basic class for a processor that will be applied to items at the end of the data block API. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(PreProcessor, title_level=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

process_one[source][test]

\n", "\n", "> process_one(**`item`**:`Any`)\n", "\n", "
×

Tests found for process_one:

Related tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(PreProcessor.process_one)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Process one `item`. This method needs to be written in any subclass." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process(**`ds`**:`Collection`\\[`T_co`\\])\n", "\n", "
×

Tests found for process:

Direct tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(PreProcessor.process)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Process a dataset. This default to apply `process_one` on every `item` of `ds`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class CategoryProcessor[source][test]

\n", "\n", "> CategoryProcessor(**`ds`**:[`ItemList`](/data_block.html#ItemList)) :: [`PreProcessor`](/data_block.html#PreProcessor)\n", "\n", "
×

No tests found for CategoryProcessor. To contribute a test please refer to this guide and this discussion.

\n", "\n", "[`PreProcessor`](/data_block.html#PreProcessor) that create `classes` from `ds.items` and handle the mapping. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor, title_level=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

generate_classes[source][test]

\n", "\n", "> generate_classes(**`items`**)\n", "\n", "
×

No tests found for generate_classes. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Generate classes from `items` by taking the sorted unique values. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor.generate_classes)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class MultiCategoryProcessor[source][test]

\n", "\n", "> MultiCategoryProcessor(**`ds`**:[`ItemList`](/data_block.html#ItemList), **`one_hot`**:`bool`=***`False`***) :: [`CategoryProcessor`](/data_block.html#CategoryProcessor)\n", "\n", "
×

No tests found for MultiCategoryProcessor. To contribute a test please refer to this guide and this discussion.

\n", "\n", "[`PreProcessor`](/data_block.html#PreProcessor) that create `classes` from `ds.items` and handle the mapping. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryProcessor, title_level=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

generate_classes[source][test]

\n", "\n", "> generate_classes(**`items`**)\n", "\n", "
×

No tests found for generate_classes. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Generate classes from `items` by taking the sorted unique values. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryProcessor.generate_classes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optional steps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add transforms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Transforms differ from processors in the sense they are applied on the fly when we grab one item. They also may change each time we ask for the same item in the case of random transforms." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform[source][test]

\n", "\n", "> transform(**`tfms`**:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=***`(None, None)`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set `tfms` to be applied to the xs of the train and validation set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.transform)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is primary for the vision application. The `kwargs` arguments are the ones expected by the type of transforms you pass. `tfm_y` is among them and if set to `True`, the transforms will be applied to input and target.\n", "\n", "For examples see: [vision.transforms](vision.transform.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add a test set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To add a test set, you can use one of the two following methods." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

add_test[source][test]

\n", "\n", "> add_test(**`items`**:`Iterator`\\[`T_co`\\], **`label`**:`Any`=***`None`***)\n", "\n", "
×

No tests found for add_test. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Add test set containing `items` with an arbitrary `label`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.add_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Note: Here `items` can be an `ItemList` or a collection.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_note(\"Here `items` can be an `ItemList` or a collection.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

add_test_folder[source][test]

\n", "\n", "> add_test_folder(**`test_folder`**:`str`=***`'test'`***, **`label`**:`Any`=***`None`***)\n", "\n", "
×

No tests found for add_test_folder. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Add test set containing items from `test_folder` and an arbitrary `label`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.add_test_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Warning: In fastai the test set is unlabeled! So no labels will be collected even if they are available.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_warn(\"In fastai the test set is unlabeled! No labels will be collected even if they are available.\")" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "Instead, either the passed `label` argument or an empty label will be used for all entries of this dataset (this is required by the internal pipeline of fastai). \n", "\n", "In the `fastai` framework `test` datasets have no labels - this is the unknown data to be predicted. If you want to validate your model on a `test` dataset with labels, you probably need to use it as a validation set, as in:\n", "\n", "```\n", "data_test = (ImageList.from_folder(path)\n", " .split_by_folder(train='train', valid='test')\n", " .label_from_folder()\n", " ...)\n", "```\n", "\n", "Another approach, where you do use a normal validation set, and then when the training is over, you just want to validate the test set w/ labels as a validation set, you can do this:\n", "\n", "```\n", "tfms = []\n", "path = Path('data').resolve()\n", "data = (ImageList.from_folder(path)\n", " .split_by_pct()\n", " .label_from_folder()\n", " .transform(tfms)\n", " .databunch()\n", " .normalize() ) \n", "learn = cnn_learner(data, models.resnet50, metrics=accuracy)\n", "learn.fit_one_cycle(5,1e-2)\n", "\n", "# now replace the validation dataset entry with the test dataset as a new validation dataset: \n", "# everything is exactly the same, except replacing `split_by_pct` w/ `split_by_folder` \n", "# (or perhaps you were already using the latter, so simply switch to valid='test')\n", "data_test = (ImageList.from_folder(path)\n", " .split_by_folder(train='train', valid='test')\n", " .label_from_folder()\n", " .transform(tfms)\n", " .databunch()\n", " .normalize()\n", " ) \n", "learn.validate(data_test.valid_dl)\n", "```\n", "Of course, your data block can be totally different, this is just an example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: convert to a [`DataBunch`](/basic_data.html#DataBunch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This last step is usually pretty straightforward. You just have to include all the arguments we pass to [`DataBunch.create`](/basic_data.html#DataBunch.create) (`bs`, `num_workers`, `collate_fn`). The class called to create a [`DataBunch`](/basic_data.html#DataBunch) is set in the `_bunch` attribute of the inputs of the training set if you need to modify it. Normally, the various subclasses we showed before handle that for you." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

databunch[source][test]

\n", "\n", "> databunch(**`path`**:`PathOrStr`=***`None`***, **`bs`**:`int`=***`64`***, **`val_bs`**:`int`=***`None`***, **`num_workers`**:`int`=***`8`***, **`dl_tfms`**:`Optional`\\[`Collection`\\[`Callable`\\]\\]=***`None`***, **`device`**:[`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch-device)=***`None`***, **`collate_fn`**:`Callable`=***`'data_collate'`***, **`no_check`**:`bool`=***`False`***, **\\*\\*`kwargs`**) → `DataBunch`\n", "\n", "
×

Tests found for databunch:

  • pytest -sv tests/test_vision_data.py::test_vision_datasets [source]

Related tests:

  • pytest -sv tests/test_data_block.py::test_regression [source]

To run tests please refer to this guide.

\n", "\n", "Create an [`DataBunch`](/basic_data.html#DataBunch) from self, `path` will override `self.path`, `kwargs` are passed to [`DataBunch.create`](/basic_data.html#DataBunch.create). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.databunch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inner classes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class LabelList[source][test]

\n", "\n", "> LabelList(**`x`**:[`ItemList`](/data_block.html#ItemList), **`y`**:[`ItemList`](/data_block.html#ItemList), **`tfms`**:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=***`None`***, **`tfm_y`**:`bool`=***`False`***, **\\*\\*`kwargs`**) :: [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)\n", "\n", "
×

No tests found for LabelList. To contribute a test please refer to this guide and this discussion.

\n", "\n", "A list of inputs `x` and labels `y` with optional `tfms`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optionally apply `tfms` to `y` if `tfm_y` is `True`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

export[source][test]

\n", "\n", "> export(**`fn`**:`PathOrStr`, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for export. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Export the minimal state and save it in `fn` to load an empty version for inference. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.export)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform_y[source][test]

\n", "\n", "> transform_y(**`tfms`**:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=***`None`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform_y. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set `tfms` to be applied to the targets only. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.transform_y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_state[source][test]

\n", "\n", "> get_state(**\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for get_state. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Return the minimal state for export. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.get_state)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

load_empty[source][test]

\n", "\n", "> load_empty(**`path`**:`PathOrStr`, **`fn`**:`PathOrStr`)\n", "\n", "
×

No tests found for load_empty. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Load the state in `fn` to create an empty [`LabelList`](/data_block.html#LabelList) for inference. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.load_empty)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

load_state[source][test]

\n", "\n", "> load_state(**`path`**:`PathOrStr`, **`state`**:`dict`) → `LabelList`\n", "\n", "
×

No tests found for load_state. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a [`LabelList`](/data_block.html#LabelList) from `state`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.load_state)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process(**`xp`**:[`PreProcessor`](/data_block.html#PreProcessor)=***`None`***, **`yp`**:[`PreProcessor`](/data_block.html#PreProcessor)=***`None`***, **`name`**:`str`=***`None`***)\n", "\n", "
×

Tests found for process:

Direct tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

\n", "\n", "Launch the processing on `self.x` and `self.y` with `xp` and `yp`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.process)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

set_item[source][test]

\n", "\n", "> set_item(**`item`**)\n", "\n", "
×

No tests found for set_item. To contribute a test please refer to this guide and this discussion.

\n", "\n", "For inference, will briefly replace the dataset with one that only contains `item`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.set_item)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

to_df[source][test]

\n", "\n", "> to_df()\n", "\n", "
×

No tests found for to_df. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create `pd.DataFrame` containing `items` from `self.x` and `self.y`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.to_df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

to_csv[source][test]

\n", "\n", "> to_csv(**`dest`**:`str`)\n", "\n", "
×

No tests found for to_csv. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Save `self.to_df()` to a CSV file in `self.path`/`dest`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.to_csv)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform[source][test]

\n", "\n", "> transform(**`tfms`**:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], **`tfm_y`**:`bool`=***`None`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set the `tfms` and `tfm_y` value to be applied to the inputs and targets. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.transform)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ItemLists[source][test]

\n", "\n", "> ItemLists(**`path`**:`PathOrStr`, **`train`**:[`ItemList`](/data_block.html#ItemList), **`valid`**:[`ItemList`](/data_block.html#ItemList))\n", "\n", "
×

No tests found for ItemLists. To contribute a test please refer to this guide and this discussion.

\n", "\n", "An [`ItemList`](/data_block.html#ItemList) for each of `train` and `valid` (optional `test`). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemLists, title_level=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_lists[source][test]

\n", "\n", "> label_from_lists(**`train_labels`**:`Iterator`\\[`T_co`\\], **`valid_labels`**:`Iterator`\\[`T_co`\\], **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

No tests found for label_from_lists. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Use the labels in `train_labels` and `valid_labels` to label the data. `label_cls` will overwrite the default. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemLists.label_from_lists)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform[source][test]

\n", "\n", "> transform(**`tfms`**:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=***`(None, None)`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set `tfms` to be applied to the xs of the train and validation set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemLists.transform)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform_y[source][test]

\n", "\n", "> transform_y(**`tfms`**:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=***`(None, None)`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform_y. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set `tfms` to be applied to the ys of the train and validation set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemLists.transform_y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class LabelLists[source][test]

\n", "\n", "> LabelLists(**`path`**:`PathOrStr`, **`train`**:[`ItemList`](/data_block.html#ItemList), **`valid`**:[`ItemList`](/data_block.html#ItemList)) :: [`ItemLists`](/data_block.html#ItemLists)\n", "\n", "
×

No tests found for LabelLists. To contribute a test please refer to this guide and this discussion.

\n", "\n", "A [`LabelList`](/data_block.html#LabelList) for each of `train` and `valid` (optional `test`). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists, title_level=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_processors[source][test]

\n", "\n", "> get_processors()\n", "\n", "
×

No tests found for get_processors. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Read the default class processors if none have been set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.get_processors)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

load_empty[source][test]

\n", "\n", "> load_empty(**`path`**:`PathOrStr`, **`fn`**:`PathOrStr`=***`'export.pkl'`***)\n", "\n", "
×

No tests found for load_empty. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a [`LabelLists`](/data_block.html#LabelLists) with empty sets from the serialized file in `path/fn`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.load_empty)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

load_state[source][test]

\n", "\n", "> load_state(**`path`**:`PathOrStr`, **`state`**:`dict`)\n", "\n", "
×

No tests found for load_state. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a [`LabelLists`](/data_block.html#LabelLists) with empty sets from the serialized `state`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.load_state)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process()\n", "\n", "
×

Tests found for process:

Direct tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

\n", "\n", "Process the inner datasets. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.process)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Helper functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_files[source][test]

\n", "\n", "> get_files(**`path`**:`PathOrStr`, **`extensions`**:`StrList`=***`None`***, **`recurse`**:`bool`=***`False`***, **`include`**:`OptStrList`=***`None`***) → `FilePathList`\n", "\n", "
×

No tests found for get_files. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Return list of files in `path` that have a suffix in `extensions`; optionally `recurse`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(get_files)" ] }, { "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": [ "

new[source][test]

\n", "\n", "> new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a new [`ItemList`](/data_block.html#ItemList) from `items`, keeping the same attributes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList.new)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

new[source][test]

\n", "\n", "> new(**`x`**, **`y`**, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.new)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**)\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList.get)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

predict[source][test]

\n", "\n", "> predict(**`res`**)\n", "\n", "
×

No tests found for predict. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Delegates predict call on `res` to `self.y`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.predict)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

new[source][test]

\n", "\n", "> new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a new [`ItemList`](/data_block.html#ItemList) from `items`, keeping the same attributes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.new)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

process_one[source][test]

\n", "\n", "> process_one(**`item`**:[`ItemBase`](/core.html#ItemBase), **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***)\n", "\n", "
×

Tests found for process_one:

Related tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

\n", "\n", "Apply `processor` or `self.processor` to `item`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.process_one)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process(**`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***)\n", "\n", "
×

Tests found for process:

Direct tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

\n", "\n", "Apply `processor` or `self.processor` to `self`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.process)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

process_one[source][test]

\n", "\n", "> process_one(**`item`**)\n", "\n", "
×

Tests found for process_one:

Related tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryProcessor.process_one)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**)\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(FloatList.get)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

process_one[source][test]

\n", "\n", "> process_one(**`item`**)\n", "\n", "
×

Tests found for process_one:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor.process_one)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

create_classes[source][test]

\n", "\n", "> create_classes(**`classes`**)\n", "\n", "
×

No tests found for create_classes. To contribute a test please refer to this guide and this discussion.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor.create_classes)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process(**`ds`**)\n", "\n", "
×

Tests found for process:

Direct tests:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor.process)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**)\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryList.get)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

new[source][test]

\n", "\n", "> new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a new [`ItemList`](/data_block.html#ItemList) from `items`, keeping the same attributes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(FloatList.new)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(FloatList.reconstruct)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

analyze_pred[source][test]

\n", "\n", "> analyze_pred(**`pred`**, **`thresh`**:`float`=***`0.5`***)\n", "\n", "
×

No tests found for analyze_pred. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Called on `pred` before `reconstruct` for additional preprocessing. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryList.analyze_pred)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryList.reconstruct)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList.reconstruct)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

analyze_pred[source][test]

\n", "\n", "> analyze_pred(**`pred`**, **`thresh`**:`float`=***`0.5`***)\n", "\n", "
×

No tests found for analyze_pred. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Called on `pred` before `reconstruct` for additional preprocessing. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList.analyze_pred)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**:`Tensor`, **`x`**:`Tensor`=***`None`***)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(EmptyLabelList.reconstruct)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**)\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(EmptyLabelList.get)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

databunch[source][test]

\n", "\n", "> databunch(**\\*\\*`kwargs`**)\n", "\n", "
×

Tests found for databunch:

Related tests:

  • pytest -sv tests/test_data_block.py::test_regression [source]

To run tests please refer to this guide.

\n", "\n", "To throw a clear error message when the data wasn't split. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.databunch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New Methods - Please document or move to the undocumented section" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

add[source][test]

\n", "\n", "> add(**`items`**:`ItemList`)\n", "\n", "
×

No tests found for add. To contribute a test please refer to this guide and this discussion.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.add)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "jekyll": { "keywords": "fastai", "summary": "The data block API", "title": "data_block" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 }