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master_thesis_models

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focus-prediction
microscopy
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Hannes Kuchelmeister commited on
Commit
ef10223
·
1 Parent(s): 447c563

add better contour plot using argmin and abs

Browse files
Files changed (4) hide show
  1. .gitattributes +1 -0
  2. notebooks/1.0-hfk-datamodules-exploration.ipynb +3 -204
  3. notebooks/2.0-hfk-convolutional_testing.ipynb +3 -203
  4. notebooks/3.0-hfk-model-performance-visualisation.ipynb +0 -0
.gitattributes CHANGED
@@ -28,3 +28,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.ckpt filter=lfs diff=lfs merge=lfs -text
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  data/**/*.json filter=lfs diff=lfs merge=lfs -text
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  data/**/*.csv filter=lfs diff=lfs merge=lfs -text
 
 
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  *.ckpt filter=lfs diff=lfs merge=lfs -text
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  data/**/*.json filter=lfs diff=lfs merge=lfs -text
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  data/**/*.csv filter=lfs diff=lfs merge=lfs -text
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+ *.ipynb filter=lfs diff=lfs merge=lfs -text
notebooks/1.0-hfk-datamodules-exploration.ipynb CHANGED
@@ -1,204 +1,3 @@
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- {
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- "cells": [
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- {
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- "cell_type": "markdown",
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- "metadata": {},
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- "source": [
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- "## Exploring Code for Data"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "import pandas as pd"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "metadata = pd.read_csv(\"../data/focus/metadata.csv\")"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "metadata"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "idx = 0\n",
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- "# File Path\n",
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- "metadata.iloc[idx, 1]"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "# Focus Value\n",
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- "metadata.iloc[idx, 5]"
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- ]
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- },
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- {
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- "cell_type": "markdown",
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- "metadata": {},
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- "source": [
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- "## Testing FocusDataSet"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "from importlib.machinery import SourceFileLoader\n",
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- "\n",
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- "focus_datamodule = SourceFileLoader(\"focus_datamodule\", \"../src/datamodules/focus_datamodule.py\").load_module()\n",
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- "from focus_datamodule import FocusDataSet\n",
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- "\n",
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- "ds = FocusDataSet(\"../data/focus/metadata.csv\", \"../data/focus/\")\n",
77
- "\n",
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- "for d in ds:\n",
79
- " break\n",
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- "\n",
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- "d"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "from focus_datamodule import FocusDataModule\n",
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- "\n",
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- "datamodule = FocusDataModule(data_dir=\"../data/focus\", csv_file=\"../data/focus/metadata.csv\")\n",
93
- "datamodule.setup()"
94
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "for data in datamodule.test_dataloader():\n",
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- " break\n",
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- "\n",
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- "len(data[\"focus_value\"])"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "import types\n",
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- "import importlib.machinery\n",
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- "focus_module = SourceFileLoader('focus_module', '../src/models/focus_module.py').load_module()\n",
117
- "from focus_module import FocusLitModule\n",
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- "\n",
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- "model = FocusLitModule()\n",
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- "\n",
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- "model.step(data)"
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- ]
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- },
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- {
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- "cell_type": "markdown",
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- "metadata": {},
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- "source": [
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- "## Benchmark in-memory and from disk"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "import time\n",
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- "\n",
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- "iterations = 10"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "datamodule = FocusDataModule(data_dir=\"../data/focus150\", csv_file=\"../data/focus150/metadata.csv\")\n",
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- "datamodule.setup()\n",
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- "\n",
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- "\n",
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- "start = time.perf_counter()\n",
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- "counter = 0\n",
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- "for i in range(iterations):\n",
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- " for data in datamodule.train_dataloader():\n",
156
- " counter += 1\n",
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- "\n",
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- "print(time.perf_counter() - start)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "datamodule = FocusDataModule(data_dir=\"../data/focus150\", csv_file=\"../data/focus150/metadata.csv\", in_memory=False)\n",
168
- "datamodule.setup()\n",
169
- "\n",
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- "start = time.perf_counter()\n",
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- "counter = 0\n",
172
- "for i in range(iterations):\n",
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- " for data in datamodule.train_dataloader():\n",
174
- " counter += 1\n",
175
- "print(time.perf_counter() - start)"
176
- ]
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- }
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- ],
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- "metadata": {
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- "interpreter": {
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- "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac"
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- },
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- "kernelspec": {
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- "display_name": "Python 3.9.7 64-bit",
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- "language": "python",
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- "name": "python3"
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- },
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- "language_info": {
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- "codemirror_mode": {
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- "name": "ipython",
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- "version": 3
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- },
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- "file_extension": ".py",
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- "mimetype": "text/x-python",
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- "name": "python",
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- "nbconvert_exporter": "python",
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- "pygments_lexer": "ipython3",
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- "version": "3.8.10"
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- },
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- "orig_nbformat": 4
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- },
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- "nbformat": 4,
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- "nbformat_minor": 2
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- }
 
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notebooks/2.0-hfk-convolutional_testing.ipynb CHANGED
@@ -1,203 +1,3 @@
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- {
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- "cells": [
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- {
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- "cell_type": "markdown",
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- "metadata": {},
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- "source": [
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- "## Testing FocusDataSet"
8
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 1,
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- "metadata": {},
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- "text": [
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- "2450\n"
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- {
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- "data": {
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- "text/plain": [
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- "{'image': array([[[211, 185, 62],\n",
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- " [162, 164, 37],\n",
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- " [172, 175, 46]]], dtype=uint8),\n",
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- " 'focus_value': tensor(0.5450)}"
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- ]
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- },
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- "execution_count": 1,
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- "metadata": {},
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- "output_type": "execute_result"
80
- }
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- ],
82
- "source": [
83
- "from importlib.machinery import SourceFileLoader\n",
84
- "\n",
85
- "focus_datamodule = SourceFileLoader(\"focus_datamodule\", \"../src/datamodules/focus_datamodule.py\").load_module()\n",
86
- "from focus_datamodule import FocusDataSet\n",
87
- "\n",
88
- "ds = FocusDataSet(\"../data/focus150/metadata.csv\", \"../data/focus150/\")\n",
89
- "\n",
90
- "counter = 0\n",
91
- "for d in ds:\n",
92
- " counter += 1\n",
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- "\n",
94
- "print(counter)\n",
95
- "\n",
96
- "d"
97
- ]
98
- },
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- {
100
- "cell_type": "code",
101
- "execution_count": 81,
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- "metadata": {},
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- "outputs": [
104
- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "14\n"
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- ]
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- },
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- {
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- "data": {
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- "text/plain": [
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- "torch.Size([64, 1])"
115
- ]
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- },
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- "execution_count": 81,
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- "metadata": {},
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- "output_type": "execute_result"
120
- }
121
- ],
122
- "source": [
123
- "from focus_datamodule import FocusDataModule\n",
124
- "\n",
125
- "datamodule = FocusDataModule(data_dir=\"../data/focus150\", csv_file=\"../data/focus150/metadata.csv\")\n",
126
- "datamodule.setup()\n",
127
- "\n",
128
- "for data in datamodule.test_dataloader():\n",
129
- " break\n",
130
- "\n",
131
- "len(data[\"focus_value\"])\n",
132
- "\n",
133
- "# https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html\n",
134
- "import torch\n",
135
- "import torch.nn as nn\n",
136
- "import torch.nn.functional as F\n",
137
- "\n",
138
- "class Net(nn.Module):\n",
139
- " def __init__(self):\n",
140
- " super().__init__()\n",
141
- " pool_size = 3\n",
142
- " \n",
143
- " conv1_size = 5\n",
144
- " conv1_out = 6\n",
145
- " conv2_size = 5\n",
146
- " conv2_out = 16\n",
147
- " size_img = 150\n",
148
- "\n",
149
- " size_img -= conv1_size - 1\n",
150
- " size_img = int( (size_img) / pool_size)\n",
151
- " size_img -= conv2_size - 1\n",
152
- " size_img = int(size_img / pool_size)\n",
153
- "\n",
154
- " print(size_img)\n",
155
- "\n",
156
- " self.model = nn.Sequential(\n",
157
- " nn.Conv2d(3, conv1_out, conv1_size),\n",
158
- " nn.MaxPool2d(pool_size, pool_size),\n",
159
- " nn.Conv2d(conv1_out, conv2_out, conv2_size),\n",
160
- " nn.MaxPool2d(pool_size, pool_size),\n",
161
- " nn.Flatten(),\n",
162
- " nn.Linear(conv2_out * size_img * size_img, 120), # 16 * 34 * 34 or [64, 16, 15, 15]\n",
163
- " nn.Linear(120, 84),\n",
164
- " nn.Linear(84, 1)\n",
165
- " )\n",
166
- "\n",
167
- " def forward(self, x):\n",
168
- " x = self.model(x)\n",
169
- " return x\n",
170
- "\n",
171
- "\n",
172
- "net = Net()\n",
173
- "\n",
174
- "net(data[\"image\"]).shape"
175
- ]
176
- }
177
- ],
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- "metadata": {
179
- "interpreter": {
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- "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac"
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- },
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- "language": "python",
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- "codemirror_mode": {
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- "name": "ipython",
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- },
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- "file_extension": ".py",
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- "mimetype": "text/x-python",
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- "name": "python",
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- "nbconvert_exporter": "python",
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- "pygments_lexer": "ipython3",
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- "version": "3.8.10"
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- },
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- "orig_nbformat": 4
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- },
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- "nbformat": 4,
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- "nbformat_minor": 2
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- }
 
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notebooks/3.0-hfk-model-performance-visualisation.ipynb CHANGED
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