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embetter mobilenet
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‎README.md

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@@ -91,6 +91,7 @@ A collection of various deep learning architectures, models, and tips for Tensor
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| MobileNet-v2 on Cifar-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-mobilenet-v2-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-mobilenet-v2-cifar10.ipynb) |
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| MobileNet-v3 small on Cifar-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-mobilenet-v3-small-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-mobilenet-v3-small-cifar10.ipynb) |
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| MobileNet-v3 large on Cifar-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-mobilenet-v3-large-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-mobilenet-v3-large-cifar10.ipynb) |
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| MobileNet-v3 large on MNIST via Embetter | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-embetter-mobilenet.ipynb) |
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‎pytorch_ipynb/cnn/cnn-embetter-mobilenet.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4936d1e6-5e7d-4e22-ae35-8e888927ce2d",
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"metadata": {},
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"source": [
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"# Use Pre-trained CNN as feature extractor"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bf9e9fb5-7383-475a-93e1-decdbd59c247",
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"metadata": {},
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"source": [
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"Use MobileNetv3 as a feature extractor via the [embetter](https://github.com/koaning/embetter) scikit-learn library and [timm](https://github.com/rwightman/pytorch-image-models). Train a logistic regression classifier in scikit-learn on the embeddings."
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]
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},
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{
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"cell_type": "markdown",
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"id": "96b717c7-54c9-40dc-ba80-0fb47da2c0bd",
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"metadata": {},
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"source": [
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"![](images/feature-extractor.png)"
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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": 1,
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"id": "64d1dd64-c45b-4092-84d1-1bfcd0998f15",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# pip install gitpython\n",
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"from git import Repo\n",
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"\n",
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"if not os.path.exists(\"mnist-pngs\"):\n",
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" Repo.clone_from(\"https://github.com/rasbt/mnist-pngs\", \"mnist-pngs\")"
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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": 2,
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"id": "3a892538-8d9b-4420-9525-26d1a4b37ae3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"\n",
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"for name in (\"train\", \"test\"):\n",
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"\n",
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" df = pd.read_csv(f\"mnist-pngs/{name}.csv\")\n",
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" df[\"filepath\"] = df[\"filepath\"].apply(lambda x: \"mnist-pngs/\" + x)\n",
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" df = df.sample(frac=1, random_state=123).reset_index(drop=True)\n",
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" df.to_csv(f\"mnist-pngs/{name}_shuffled.csv\", index=None)"
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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": 3,
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"id": "5885e9bb-d43f-46ca-83ae-e2d63edcbb37",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1fba0fcb2b1f408f85013da0d1694dd3",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/60 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from sklearn.pipeline import make_pipeline\n",
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"from sklearn.linear_model import SGDClassifier\n",
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"from tqdm.notebook import tqdm\n",
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"\n",
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"# pip install \"embetter[vision]\"\n",
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"from embetter.vision import ImageLoader, TimmEncoder\n",
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"\n",
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"\n",
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"embed = make_pipeline(\n",
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" ImageLoader(),\n",
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" TimmEncoder(name=\"mobilenetv3_large_100\")\n",
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")\n",
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"\n",
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"model = SGDClassifier(loss='log_loss', n_jobs=-1, shuffle=True)\n",
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"\n",
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"chunksize = 1000\n",
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"train_labels, train_predict = [], []\n",
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"\n",
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"for df in tqdm(pd.read_csv(\"mnist-pngs/train_shuffled.csv\", chunksize=chunksize, iterator=True), total=60):\n",
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" \n",
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" embedded = embed.transform(df[\"filepath\"])\n",
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" model.partial_fit(embedded, df[\"label\"], classes=list(range(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": 4,
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"id": "999a24ea-be5d-425f-923c-266372c66b5d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "157302965ac8460c97c77935cc08e1fc",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/60 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"train_labels, train_predict = [], []\n",
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"\n",
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"for df in tqdm(pd.read_csv(\"mnist-pngs/train.csv\", chunksize=chunksize, iterator=True), total=60):\n",
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" df[\"filepath\"] = df[\"filepath\"].apply(lambda x: \"mnist-pngs/\" + x)\n",
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"\n",
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" embedded = embed.transform(df[\"filepath\"])\n",
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" train_predict.extend(model.predict(embedded))\n",
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" train_labels.extend(list(df[\"label\"].values))"
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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": 5,
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"id": "c816cd7b-ed3a-4cb2-8aa6-400068a2e414",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "7869826407314279a2806bf602a796a8",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/10 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"test_labels, test_predict = [], []\n",
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"\n",
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"for df in tqdm(pd.read_csv(\"mnist-pngs/test_shuffled.csv\", chunksize=chunksize, iterator=True), total=10):\n",
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"\n",
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" embedded = embed.transform(df[\"filepath\"])\n",
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" test_predict.extend(model.predict(embedded))\n",
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" test_labels.extend(list(df[\"label\"].values))"
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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": 6,
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"id": "4a78add1-7f93-40fc-b119-9dbbe0aa55b4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Train accuracy: 0.92\n",
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"Test accuracy: 0.92\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"print(f\"Train accuracy: {accuracy_score(train_labels, train_predict):.2f}\")\n",
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"print(f\"Test accuracy: {accuracy_score(test_labels, test_predict):.2f}\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎pytorch_ipynb/cnn/images/feature-extractor.png

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