136 lines
3.6 KiB
Plaintext
136 lines
3.6 KiB
Plaintext
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"collapsed_sections": [
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"0-LmeiloOQFI"
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],
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"gpuType": "T4"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"## Download COCO_2017 and sample 20k images (highest resolution ones)"
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],
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"metadata": {
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"id": "sA8GqeiqlF99"
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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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"id": "xU5fwAjdJ1m1"
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},
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"outputs": [],
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"source": [
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"%cd /content\n",
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"!git clone --recursive https://github.com/verlab/accelerated_features.git\n",
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"!pip install kornia==0.6.12 --no-deps\n",
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"!pip install -r accelerated_features/requirements.txt --no-deps\n",
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"\n",
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"#Download precomputed coco-20k used in the paper.\n",
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"#Please refer to https://cocodataset.org/#home for the original, complete dataset.\n",
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"!gdown '1ijYsPq7dtLQSl-oEsUOGH1fAy21YLc7H'\n",
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"!unzip -qq coco_20k.zip"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## If you want to build COCO_20k from scratch, try this below."
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],
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"metadata": {
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"id": "0-LmeiloOQFI"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# !wget http://images.cocodataset.org/zips/train2017.zip #18 GB images\n",
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"# !unzip train2017.zip\n",
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"# !rm train2017.zip\n",
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"\n",
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"# import glob\n",
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"# import random\n",
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"# import cv2\n",
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"# import os\n",
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"# import tqdm\n",
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"\n",
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"# files = sorted(glob.glob('/content/train2017/*.jpg'))\n",
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"\n",
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"# !mkdir -p /content/coco_20k\n",
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"\n",
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"# random.seed(1111)\n",
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"# random.shuffle(files)\n",
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"\n",
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"# max_imgs = 20_000\n",
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"# cnt = 0\n",
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"\n",
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"# for f in files:\n",
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"# with tqdm.tqdm(total=max_imgs) as pbar:\n",
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"# im = cv2.imread(f)\n",
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"# h, w = im.shape[:2]\n",
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"# if w >= 600 and h >= 400:\n",
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"# cv2.imwrite('/content/coco_20k/' + os.path.basename(f), im)\n",
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"# cnt+=1\n",
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"# pbar.update(1)\n",
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"\n",
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"# if cnt >= max_imgs:\n",
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"# break\n"
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],
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"metadata": {
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"id": "miB7u24KKJXh"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Train XFeat on COCO_20k (Synthetic training)\n",
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"For demonstration purposes we start to train XFeat from scratch with synthetic data only."
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],
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"metadata": {
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"id": "VDSpQtdZlQk8"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Load the TensorBoard notebook extension\n",
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"%load_ext tensorboard\n",
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"%tensorboard --logdir /content/ckpts"
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],
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"metadata": {
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"id": "z3U4MZXqlPmO"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"%cd accelerated_features\n",
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"!python3 -m modules.training.train --training_type xfeat_synthetic --synthetic_root_path /content/coco_20k --ckpt_save_path /content/ckpts"
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],
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"metadata": {
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"id": "nOP_BRppLrWm"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}
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