ImageRegressionDataset for fastai

fastai-ImageRegressionDataset-dev.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "import fastai; fastai.__version__",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 1,
          "data": {
            "text/plain": "'1.0.19'"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from fastai import *\nfrom fastai.vision import *",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "class ImageRegressionDataset(ImageClassificationBase):\n    def __init__(self, fns:FilePathList, y:Collection[Number]):\n        super().__init__(fns, classes=[])\n        self.y = np.array(y, dtype=np.float32)[:, None]\n        self.c = 1\n        self.loss_func = F.mse_loss",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pdata = Path('/data/cifar10/train/airplane/')\n\nlist(pdata.glob('*.png'))[:3]",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "[PosixPath('/data/cifar10/train/airplane/17015_airplane.png'),\n PosixPath('/data/cifar10/train/airplane/44932_airplane.png'),\n PosixPath('/data/cifar10/train/airplane/43160_airplane.png')]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "n = 100\nn_val = 20\nfns = list(pdata.glob('*.png'))[:n]\ny = np.random.randn(n)\n\nds_trn = ImageRegressionDataset(fns[:-n_val], y[:-n_val])\nds_val = ImageRegressionDataset(fns[-n_val:], y[-n_val:])\n\nimg, y = ds_val[0]\nimg.show(title=y[0])",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 216x216 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = ImageDataBunch.create(ds_trn, ds_val)\n\nlearn = create_cnn(data, models.resnet18)\n\nlearn.fit(1)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Total time: 00:00\nepoch  train_loss  valid_loss\n1      1.250102    1.324627    (00:00)\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = ImageDataBunch.create(ds_trn, ds_val)\n\nlearn = create_cnn(data, models.resnet18, loss_func=F.l1_loss)\n\nlearn.fit(1)",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Total time: 00:00\nepoch  train_loss  valid_loss\n1      1.430220    1.078725    (00:00)\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "conda-env-fastaiv1-py",
      "display_name": "Python [conda env:fastaiv1]",
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    "gist": {
      "id": "",
      "data": {
        "description": "ImageRegressionDataset for fastai ",
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      }
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