121 lines
2.6 KiB
Plaintext
121 lines
2.6 KiB
Plaintext
{
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"cells": [
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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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"!mkdir ../checkpoints\n",
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"!wget https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth -P ../checkpoints"
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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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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import matplotlib.pyplot as plt\n",
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"from mmengine.model.utils import revert_sync_batchnorm\n",
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"from mmseg.apis import init_model, inference_model, show_result_pyplot"
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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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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"config_file = '../configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'\n",
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"checkpoint_file = '../checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'"
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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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"# build the model from a config file and a checkpoint file\n",
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"model = init_model(config_file, checkpoint_file, device='cpu')"
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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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"# test a single image\n",
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"img = 'demo.png'\n",
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"if not torch.cuda.is_available():\n",
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" model = revert_sync_batchnorm(model)\n",
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"result = inference_model(model, img)"
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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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"# show the results\n",
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"vis_result = show_result_pyplot(model, img, result, show=False)\n",
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"plt.imshow(vis_result)"
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "pt1.13",
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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": "3.10.11"
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},
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"pycharm": {
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"stem_cell": {
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"cell_type": "raw",
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"metadata": {
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"collapsed": false
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},
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"source": []
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}
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},
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"vscode": {
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"interpreter": {
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"hash": "f61d5b8fecdd960739697f6c2860080d7b76a5be5d896cb034bdb275ab3ddda0"
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"nbformat": 4,
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"nbformat_minor": 4
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}
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