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johschmidt42 · Jan 17, 2021 · 14996bd · 14996bd
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diff --git a/Part1.ipynb b/Part1.ipynb
@@ -0,0 +1,213 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "name": "#%% Carvana dataset example\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from transformations import Compose, Resize, DenseTarget\n",
+    "from transformations import MoveAxis, Normalize01\n",
+    "from customdatasets import SegmentationDataSet\n",
+    "from torch.utils.data import DataLoader\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pathlib\n",
+    "\n",
+    "# root directory\n",
+    "root = pathlib.Path.cwd() / 'Carvana'\n",
+    "def get_filenames_of_path(path: pathlib.Path, ext: str = '*'):\n",
+    "    \"\"\"Returns a list of files in a directory/path. Uses pathlib.\"\"\"\n",
+    "    filenames = [file for file in path.glob(ext) if file.is_file()]\n",
+    "    return filenames\n",
+    "\n",
+    "# input and target files\n",
+    "inputs = get_filenames_of_path(root / 'Input')\n",
+    "targets = get_filenames_of_path(root / 'Target')\n",
+    "\n",
+    "# training transformations and augmentations\n",
+    "transforms = Compose([\n",
+    "    DenseTarget(),\n",
+    "    MoveAxis(),\n",
+    "    Normalize01()\n",
+    "])\n",
+    "\n",
+    "# random seed\n",
+    "random_seed = 42\n",
+    "\n",
+    "# split dataset into training set and validation set\n",
+    "train_size = 0.8  # 80:20 split\n",
+    "\n",
+    "inputs_train, inputs_valid = train_test_split(\n",
+    "    inputs,\n",
+    "    random_state=random_seed,\n",
+    "    train_size=train_size,\n",
+    "    shuffle=True)\n",
+    "\n",
+    "targets_train, targets_valid = train_test_split(\n",
+    "    targets,\n",
+    "    random_state=random_seed,\n",
+    "    train_size=train_size,\n",
+    "    shuffle=True)\n",
+    "\n",
+    "# dataset training\n",
+    "dataset_train = SegmentationDataSet(inputs=inputs_train,\n",
+    "                                    targets=targets_train,\n",
+    "                                    transform=transforms)\n",
+    "\n",
+    "# dataset validation\n",
+    "dataset_valid = SegmentationDataSet(inputs=inputs_valid,\n",
+    "                                    targets=targets_valid,\n",
+    "                                    transform=transforms)\n",
+    "\n",
+    "# dataloader training\n",
+    "dataloader_training = DataLoader(dataset=dataset_train,\n",
+    "                                 batch_size=2,\n",
+    "                                 shuffle=True)\n",
+    "\n",
+    "# dataloader validation\n",
+    "dataloader_validation = DataLoader(dataset=dataset_valid,\n",
+    "                                   batch_size=2,\n",
+    "                                   shuffle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = shape: torch.Size([2, 3, 1280, 1918]); type: torch.float32\n",
+      "x = min: 0.0; max: 1.0\n",
+      "y = shape: torch.Size([2, 1280, 1918]); class: tensor([0, 1]); type: torch.int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "x, y = next(iter(dataloader_training))\n",
+    "\n",
+    "print(f'x = shape: {x.shape}; type: {x.dtype}')\n",
+    "print(f'x = min: {x.min()}; max: {x.max()}')\n",
+    "print(f'y = shape: {y.shape}; class: {y.unique()}; type: {y.dtype}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<napari.viewer.Viewer at 0x21663894a00>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%gui qt\n",
+    "from visual import Input_Target_Pair_Generator\n",
+    "from visual import show_input_target_pair_napari\n",
+    "gen = Input_Target_Pair_Generator(dataloader_training, rgb=True)\n",
+    "show_input_target_pair_napari(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import albumentations\n",
+    "from transformations import Compose, AlbuSeg2d, DenseTarget, MoveAxis, Normalize01\n",
+    "# training transformations and augmentations\n",
+    "transforms_training = Compose([\n",
+    "    # Resize(input_size=(128, 128, 3), target_size=(128, 128)),\n",
+    "    AlbuSeg2d(albu=albumentations.HorizontalFlip(p=0.5)),\n",
+    "    DenseTarget(),\n",
+    "    MoveAxis(),\n",
+    "    Normalize01()\n",
+    "])\n",
+    "\n",
+    "# validation transformations\n",
+    "transforms_validation = Compose([\n",
+    "    # Resize(input_size=(128, 128, 3), target_size=(128, 128)),\n",
+    "    DenseTarget(),\n",
+    "    MoveAxis(),\n",
+    "    Normalize01()\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataset training\n",
+    "dataset_train = SegmentationDataSet(inputs=inputs_train,\n",
+    "                                    targets=targets_train,\n",
+    "                                    transform=transforms_training)\n",
+    "\n",
+    "# dataset validation\n",
+    "dataset_valid = SegmentationDataSet(inputs=inputs_valid,\n",
+    "                                    targets=targets_valid,\n",
+    "                                    transform=transforms_validation)\n",
+    "\n",
+    "# dataloader training\n",
+    "dataloader_training = DataLoader(dataset=dataset_train,\n",
+    "                                 batch_size=2,\n",
+    "                                 shuffle=True)\n",
+    "\n",
+    "# dataloader validation\n",
+    "dataloader_validation = DataLoader(dataset=dataset_valid,\n",
+    "                                   batch_size=2,\n",
+    "                                   shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%gui qt\n",
+    "from visual import Input_Target_Pair_Generator\n",
+    "from visual import show_input_target_pair_napari\n",
+    "gen_train = Input_Target_Pair_Generator(dataloader_training, rgb=True)\n",
+    "gen_valid = Input_Target_Pair_Generator(dataloader_validation, rgb=True)\n",
+    "show_input_target_pair_napari(gen_train, gen_valid)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}