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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [
"-tNVQkHnZfrs"
]
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"gpuClass": "standard"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"## Setup"
],
"metadata": {
"id": "-tNVQkHnZfrs"
}
},
{
"cell_type": "code",
"source": [
"%cd /content/\n",
"%rm -rf semantic-segmentation\n",
"!git clone https://github.com/hb0313/semantic-segmentation\n",
"%cd semantic-segmentation\n",
"%pip install -e .\n",
"%pip install -U gdown"
],
"metadata": {
"id": "pzBeWQDQZdic",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "30620197-c859-44ec-d7aa-eeccd032cdcc"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content\n",
"Cloning into 'semantic-segmentation'...\n",
"remote: Enumerating objects: 792, done.\u001b[K\n",
"remote: Counting objects: 100% (39/39), done.\u001b[K\n",
"remote: Compressing objects: 100% (28/28), done.\u001b[K\n",
"remote: Total 792 (delta 11), reused 31 (delta 11), pack-reused 753\u001b[K\n",
"Receiving objects: 100% (792/792), 55.00 MiB | 19.25 MiB/s, done.\n",
"Resolving deltas: 100% (462/462), done.\n",
"/content/semantic-segmentation\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Obtaining file:///content/semantic-segmentation\n",
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"Installing collected packages: semseg\n",
" Running setup.py develop for semseg\n",
"Successfully installed semseg-0.4.1\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Defination for loading model and checkpoints"
],
"metadata": {
"id": "L-WE4q6_ZHdQ"
}
},
{
"cell_type": "code",
"source": [
"import gdown\n",
"from pathlib import Path\n",
"import torch\n",
"from torchvision import io\n",
"from torchvision import transforms as T\n",
"from PIL import Image\n",
"from semseg.models import *\n",
"from google.colab import files\n",
"# from IPython.display import Image\n",
"\n",
"\n",
"def get_checkpoints():\n",
" ckpt = Path('./checkpoints/pretrained/segformer')\n",
" ckpt.mkdir(exist_ok=True, parents=True)\n",
"\n",
" url = 'https://huggingface.co/hashb/semantic-segmentation-segformer/resolve/main/segformer.b3.ade.pth'\n",
" output = './checkpoints/pretrained/segformer/segformer.b3.ade.pth'\n",
" gdown.download(url, output, quiet=False)\n",
"\n",
"def show_image(image):\n",
" if image.shape[2] != 3: image = image.permute(1, 2, 0)\n",
" image = Image.fromarray(image.numpy())\n",
" # image.save(\"result.png\")\n",
" return image\n",
"\n",
"def load_model():\n",
" model = eval('SegFormer')(\n",
" backbone='MiT-B3',\n",
" num_classes=150\n",
" )\n",
"\n",
" try:\n",
" model.load_state_dict(torch.load('checkpoints/pretrained/segformer/segformer.b3.ade.pth', map_location='cpu'))\n",
" except:\n",
" print(\"Download a pretrained model's weights from the result table.\")\n",
" model.eval()\n",
" return model\n",
"\n",
" print('Loaded Model')"
],
"metadata": {
"id": "QnfB4lrzjo33"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"get_checkpoints()\n",
"model = load_model()"
],
"metadata": {
"id": "L1YCKFKJKP1H",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0b5b2aeb-0978-46fd-8638-bb4976431c60"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Downloading...\n",
"From: https://huggingface.co/hashb/semantic-segmentation-segformer/resolve/main/segformer.b3.ade.pth\n",
"To: /content/semantic-segmentation/checkpoints/pretrained/segformer/segformer.b3.ade.pth\n",
"100%|██████████| 190M/190M [00:00<00:00, 206MB/s]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Upload image file"
],
"metadata": {
"id": "FRDvSMmvoK_0"
}
},
{
"cell_type": "code",
"source": [
"uploaded = files.upload()\n",
"for i in uploaded:\n",
" image_path = i\n",
"image = io.read_image(image_path)"
],
"metadata": {
"colab": {
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": "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",
"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 73
},
"id": "k2cOX2CUaZuK",
"outputId": "10aa9d41-cedf-4ebe-e39e-501ce52060f9"
},
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-588ecd48-c3e3-4199-8af1-4f923a689620\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-588ecd48-c3e3-4199-8af1-4f923a689620\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving pexels-photo-1485031.jpeg to pexels-photo-1485031.jpeg\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# resize\n",
"image = T.CenterCrop((512, 512))(image)\n",
"# scale to [0.0, 1.0]\n",
"image = image.float() / 255\n",
"# normalize\n",
"image = T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))(image)\n",
"# add batch size\n",
"image = image.unsqueeze(0)\n",
"image.shape"
],
"metadata": {
"id": "Mnfbjt3vjzmI",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e3255e3f-c3ed-47f6-f8db-c72c679af496"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"torch.Size([1, 3, 512, 512])"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"with torch.inference_mode():\n",
" seg = model(image)\n",
"seg.shape"
],
"metadata": {
"id": "lJ6xNAwzj17M",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7253e889-2b43-4f5c-ebcd-731001f4dd17"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"torch.Size([1, 150, 512, 512])"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"seg = seg.softmax(1).argmax(1).to(int)\n",
"seg.unique()"
],
"metadata": {
"id": "EqbG_qokj30-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ad1c2e60-777b-45ee-f27f-44fff275fabb"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([ 0, 1, 4, 12])"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"from semseg.datasets import *\n",
"\n",
"palette = eval('ADE20K').PALETTE"
],
"metadata": {
"id": "dyj4dPoMj7aq"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"seg_map = palette[seg].squeeze().to(torch.uint8)\n",
"show_image(seg_map)"
],
"metadata": {
"id": "02ptBGPAj8_g",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 529
},
"outputId": "0df8f3b6-7bfe-4ad3-a5e3-f794363abdbe"
},
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<PIL.Image.Image image mode=RGB size=512x512 at 0x7FBF0969DB10>"
],
"image/png": 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\n"
},
"metadata": {},
"execution_count": 13
}
]
}
]
} |