1047 lines
603 KiB
Plaintext
1047 lines
603 KiB
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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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"colab_type": "text",
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"id": "view-in-github"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch/blob/master/Chapter07/Training_RCNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 140,
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"resources": {
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"http://localhost:8080/nbextensions/google.colab/files.js": {
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"data": "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"headers": [
|
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[
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"content-type",
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"application/javascript"
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]
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],
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"ok": true,
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"status": 200,
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"status_text": ""
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}
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}
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},
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"id": "pIOo-A9m3_Th",
|
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"outputId": "440b254e-3d29-4ef1-fd7e-6d6deb3661ba"
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},
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"outputs": [],
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"source": [
|
|||
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"!pip install -q --upgrade selectivesearch torch_snippets\n",
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"from torch_snippets import *\n",
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"import selectivesearch\n",
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"#from google.colab import files\n",
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"#files.upload() # upload kaggle.json file which you can get \n",
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"# by clicking on Create New API token in your personal account\n",
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"#!mkdir -p ~/.kaggle\n",
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"#!mv kaggle.json ~/.kaggle/\n",
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"#!ls ~/.kaggle\n",
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"#!chmod 600 /root/.kaggle/kaggle.json\n",
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"#!kaggle datasets download -d sixhky/open-images-bus-trucks/\n",
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"#!unzip -qq open-images-bus-trucks.zip\n",
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"from torchvision import transforms, models, datasets\n",
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"from torch_snippets import Report\n",
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"from torchvision.ops import nms\n",
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"device = 'cuda' if torch.cuda.is_available() else '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": 2,
|
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"metadata": {},
|
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"outputs": [
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{
|
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"data": {
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"text/html": [
|
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|
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">cpu\n",
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"</pre>\n"
|
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],
|
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"text/plain": [
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"cpu\n"
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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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|
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"print(device)\n"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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|
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"execution_count": 5,
|
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|
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"metadata": {
|
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|
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"colab": {
|
|||
|
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"base_uri": "https://localhost:8080/",
|
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|
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"height": 153
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},
|
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"id": "14-PnjIp4Le_",
|
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"outputId": "1e839b05-4bf8-4b64-e4e3-b2f8be285772",
|
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|
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"scrolled": true
|
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|
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},
|
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"> ImageID Source LabelName Confidence XMin XMax \\\n",
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"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> 0000599864fd15b3 xclick Bus <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.343750</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.908750</span> \n",
|
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|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> 00006bdb1eb5cd74 xclick Truck <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.276667</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.697500</span> \n",
|
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|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> 00006bdb1eb5cd74 xclick Truck <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.702500</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.999167</span> \n",
|
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|
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"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> 00010bf498b64bab xclick Bus <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.156250</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.371250</span> \n",
|
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|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span> 00013f14dd4e168f xclick Bus <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.287500</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.999375</span> \n",
|
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"\n",
|
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|
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" YMin YMax IsOccluded IsTruncated <span style=\"color: #808000; text-decoration-color: #808000\">...</span> IsDepiction IsInside \\\n",
|
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|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.156162</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.650047</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> \n",
|
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|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.141604</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.437343</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> \n",
|
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|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.204261</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.409774</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> \n",
|
|||
|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.269188</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.705228</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> \n",
|
|||
|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.194184</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.999062</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> \n",
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|
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"\n",
|
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|
|
" XClick1X XClick2X XClick3X XClick4X XClick1Y XClick2Y XClick3Y \\\n",
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|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.421875</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.343750</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.795000</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.908750</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.156162</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.512700</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.650047</span> \n",
|
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|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.299167</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.276667</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.697500</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.659167</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.141604</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.241855</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.352130</span> \n",
|
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|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.849167</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.702500</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.906667</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.999167</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.204261</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.398496</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.409774</span> \n",
|
|||
|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.274375</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.371250</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.311875</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.156250</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.269188</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.493882</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.705228</span> \n",
|
|||
|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.920000</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.999375</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.648750</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.287500</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.194184</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.303940</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.999062</span> \n",
|
|||
|
|
"\n",
|
|||
|
|
" XClick4Y \n",
|
|||
|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.457197</span> \n",
|
|||
|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.437343</span> \n",
|
|||
|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.295739</span> \n",
|
|||
|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.521691</span> \n",
|
|||
|
|
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.523452</span> \n",
|
|||
|
|
"\n",
|
|||
|
|
"<span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> rows x <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21</span> columns<span style=\"font-weight: bold\">]</span>\n",
|
|||
|
|
"</pre>\n"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" ImageID Source LabelName Confidence XMin XMax \\\n",
|
|||
|
|
"\u001b[1;36m0\u001b[0m 0000599864fd15b3 xclick Bus \u001b[1;36m1\u001b[0m \u001b[1;36m0.343750\u001b[0m \u001b[1;36m0.908750\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m1\u001b[0m 00006bdb1eb5cd74 xclick Truck \u001b[1;36m1\u001b[0m \u001b[1;36m0.276667\u001b[0m \u001b[1;36m0.697500\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m2\u001b[0m 00006bdb1eb5cd74 xclick Truck \u001b[1;36m1\u001b[0m \u001b[1;36m0.702500\u001b[0m \u001b[1;36m0.999167\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m3\u001b[0m 00010bf498b64bab xclick Bus \u001b[1;36m1\u001b[0m \u001b[1;36m0.156250\u001b[0m \u001b[1;36m0.371250\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m4\u001b[0m 00013f14dd4e168f xclick Bus \u001b[1;36m1\u001b[0m \u001b[1;36m0.287500\u001b[0m \u001b[1;36m0.999375\u001b[0m \n",
|
|||
|
|
"\n",
|
|||
|
|
" YMin YMax IsOccluded IsTruncated \u001b[33m...\u001b[0m IsDepiction IsInside \\\n",
|
|||
|
|
"\u001b[1;36m0\u001b[0m \u001b[1;36m0.156162\u001b[0m \u001b[1;36m0.650047\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;36m0\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;36m0\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m1\u001b[0m \u001b[1;36m0.141604\u001b[0m \u001b[1;36m0.437343\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;36m0\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;36m0\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m2\u001b[0m \u001b[1;36m0.204261\u001b[0m \u001b[1;36m0.409774\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;36m1\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;36m0\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m3\u001b[0m \u001b[1;36m0.269188\u001b[0m \u001b[1;36m0.705228\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;36m0\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;36m0\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m4\u001b[0m \u001b[1;36m0.194184\u001b[0m \u001b[1;36m0.999062\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;36m1\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;36m0\u001b[0m \n",
|
|||
|
|
"\n",
|
|||
|
|
" XClick1X XClick2X XClick3X XClick4X XClick1Y XClick2Y XClick3Y \\\n",
|
|||
|
|
"\u001b[1;36m0\u001b[0m \u001b[1;36m0.421875\u001b[0m \u001b[1;36m0.343750\u001b[0m \u001b[1;36m0.795000\u001b[0m \u001b[1;36m0.908750\u001b[0m \u001b[1;36m0.156162\u001b[0m \u001b[1;36m0.512700\u001b[0m \u001b[1;36m0.650047\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m1\u001b[0m \u001b[1;36m0.299167\u001b[0m \u001b[1;36m0.276667\u001b[0m \u001b[1;36m0.697500\u001b[0m \u001b[1;36m0.659167\u001b[0m \u001b[1;36m0.141604\u001b[0m \u001b[1;36m0.241855\u001b[0m \u001b[1;36m0.352130\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m2\u001b[0m \u001b[1;36m0.849167\u001b[0m \u001b[1;36m0.702500\u001b[0m \u001b[1;36m0.906667\u001b[0m \u001b[1;36m0.999167\u001b[0m \u001b[1;36m0.204261\u001b[0m \u001b[1;36m0.398496\u001b[0m \u001b[1;36m0.409774\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m3\u001b[0m \u001b[1;36m0.274375\u001b[0m \u001b[1;36m0.371250\u001b[0m \u001b[1;36m0.311875\u001b[0m \u001b[1;36m0.156250\u001b[0m \u001b[1;36m0.269188\u001b[0m \u001b[1;36m0.493882\u001b[0m \u001b[1;36m0.705228\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m4\u001b[0m \u001b[1;36m0.920000\u001b[0m \u001b[1;36m0.999375\u001b[0m \u001b[1;36m0.648750\u001b[0m \u001b[1;36m0.287500\u001b[0m \u001b[1;36m0.194184\u001b[0m \u001b[1;36m0.303940\u001b[0m \u001b[1;36m0.999062\u001b[0m \n",
|
|||
|
|
"\n",
|
|||
|
|
" XClick4Y \n",
|
|||
|
|
"\u001b[1;36m0\u001b[0m \u001b[1;36m0.457197\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m1\u001b[0m \u001b[1;36m0.437343\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m2\u001b[0m \u001b[1;36m0.295739\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m3\u001b[0m \u001b[1;36m0.521691\u001b[0m \n",
|
|||
|
|
"\u001b[1;36m4\u001b[0m \u001b[1;36m0.523452\u001b[0m \n",
|
|||
|
|
"\n",
|
|||
|
|
"\u001b[1m[\u001b[0m\u001b[1;36m5\u001b[0m rows x \u001b[1;36m21\u001b[0m columns\u001b[1m]\u001b[0m\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"IMAGE_ROOT = 'images/images'\n",
|
|||
|
|
"DF_RAW = pd.read_csv('images/df.csv')\n",
|
|||
|
|
"print(DF_RAW.head())"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 404
|
|||
|
|
},
|
|||
|
|
"id": "P-kZg83t5rwY",
|
|||
|
|
"outputId": "0ab8b4fd-4e2a-400b-ff81-577fef95d9c9"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x1000 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"class OpenImages(Dataset):\n",
|
|||
|
|
" def __init__(self, df, image_folder=IMAGE_ROOT):\n",
|
|||
|
|
" self.root = image_folder\n",
|
|||
|
|
" self.df = df\n",
|
|||
|
|
" self.unique_images = df['ImageID'].unique()\n",
|
|||
|
|
" def __len__(self): return len(self.unique_images)\n",
|
|||
|
|
" def __getitem__(self, ix):\n",
|
|||
|
|
" image_id = self.unique_images[ix]\n",
|
|||
|
|
" image_path = f'{self.root}/{image_id}.jpg'\n",
|
|||
|
|
" image = cv2.imread(image_path, 1)[...,::-1] # conver BGR to RGB\n",
|
|||
|
|
" h, w, _ = image.shape\n",
|
|||
|
|
" df = self.df.copy()\n",
|
|||
|
|
" df = df[df['ImageID'] == image_id]\n",
|
|||
|
|
" boxes = df['XMin,YMin,XMax,YMax'.split(',')].values\n",
|
|||
|
|
" boxes = (boxes * np.array([w,h,w,h])).astype(np.uint16).tolist()\n",
|
|||
|
|
" classes = df['LabelName'].values.tolist()\n",
|
|||
|
|
" return image, boxes, classes, image_path\n",
|
|||
|
|
"ds = OpenImages(df=DF_RAW)\n",
|
|||
|
|
"im, bbs, clss, _ = ds[9]\n",
|
|||
|
|
"show(im, bbs=bbs, texts=clss, sz=10)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "bt_LSq5_55TZ"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def extract_candidates(img):\n",
|
|||
|
|
" img_lbl, regions = selectivesearch.selective_search(img, scale=200, min_size=100)\n",
|
|||
|
|
" img_area = np.prod(img.shape[:2])\n",
|
|||
|
|
" candidates = []\n",
|
|||
|
|
" for r in regions:\n",
|
|||
|
|
" if r['rect'] in candidates: continue\n",
|
|||
|
|
" if r['size'] < (0.05*img_area): continue\n",
|
|||
|
|
" if r['size'] > (1*img_area): continue\n",
|
|||
|
|
" x, y, w, h = r['rect']\n",
|
|||
|
|
" candidates.append(list(r['rect']))\n",
|
|||
|
|
" return candidates\n",
|
|||
|
|
"def extract_iou(boxA, boxB, epsilon=1e-5):\n",
|
|||
|
|
" x1 = max(boxA[0], boxB[0])\n",
|
|||
|
|
" y1 = max(boxA[1], boxB[1])\n",
|
|||
|
|
" x2 = min(boxA[2], boxB[2])\n",
|
|||
|
|
" y2 = min(boxA[3], boxB[3])\n",
|
|||
|
|
" width = (x2 - x1)\n",
|
|||
|
|
" height = (y2 - y1)\n",
|
|||
|
|
" if (width<0) or (height <0):\n",
|
|||
|
|
" return 0.0\n",
|
|||
|
|
" area_overlap = width * height\n",
|
|||
|
|
" area_a = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])\n",
|
|||
|
|
" area_b = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])\n",
|
|||
|
|
" area_combined = area_a + area_b - area_overlap\n",
|
|||
|
|
" iou = area_overlap / (area_combined+epsilon)\n",
|
|||
|
|
" return iou"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "TtCQPF8J6CGB"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"FPATHS, GTBBS, CLSS, DELTAS, ROIS, IOUS = [], [], [], [], [], []\n",
|
|||
|
|
"N = 500\n",
|
|||
|
|
"for ix, (im, bbs, labels, fpath) in enumerate(ds):\n",
|
|||
|
|
" if(ix==N):\n",
|
|||
|
|
" break\n",
|
|||
|
|
" H, W, _ = im.shape\n",
|
|||
|
|
" candidates = extract_candidates(im)\n",
|
|||
|
|
" candidates = np.array([(x,y,x+w,y+h) for x,y,w,h in candidates])\n",
|
|||
|
|
" ious, rois, clss, deltas = [], [], [], []\n",
|
|||
|
|
" ious = np.array([[extract_iou(candidate, _bb_) for candidate in candidates] for _bb_ in bbs]).T\n",
|
|||
|
|
" for jx, candidate in enumerate(candidates):\n",
|
|||
|
|
" cx,cy,cX,cY = candidate\n",
|
|||
|
|
" candidate_ious = ious[jx]\n",
|
|||
|
|
" best_iou_at = np.argmax(candidate_ious)\n",
|
|||
|
|
" best_iou = candidate_ious[best_iou_at]\n",
|
|||
|
|
" best_bb = _x,_y,_X,_Y = bbs[best_iou_at]\n",
|
|||
|
|
" if best_iou > 0.3: clss.append(labels[best_iou_at])\n",
|
|||
|
|
" else : clss.append('background')\n",
|
|||
|
|
" delta = np.array([_x-cx, _y-cy, _X-cX, _Y-cY]) / np.array([W,H,W,H])\n",
|
|||
|
|
" deltas.append(delta)\n",
|
|||
|
|
" rois.append(candidate / np.array([W,H,W,H]))\n",
|
|||
|
|
" FPATHS.append(fpath)\n",
|
|||
|
|
" IOUS.append(ious)\n",
|
|||
|
|
" ROIS.append(rois)\n",
|
|||
|
|
" CLSS.append(clss)\n",
|
|||
|
|
" DELTAS.append(deltas)\n",
|
|||
|
|
" GTBBS.append(bbs)\n",
|
|||
|
|
"FPATHS = [f'{IMAGE_ROOT}/{stem(f)}.jpg' for f in FPATHS] \n",
|
|||
|
|
"FPATHS, GTBBS, CLSS, DELTAS, ROIS = [item for item in [FPATHS, GTBBS, CLSS, DELTAS, ROIS]]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "yxzZs0Gs7bQt"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"targets = pd.DataFrame(flatten(CLSS), columns=['label'])\n",
|
|||
|
|
"label2target = {l:t for t,l in enumerate(targets['label'].unique())}\n",
|
|||
|
|
"target2label = {t:l for l,t in label2target.items()}\n",
|
|||
|
|
"background_class = label2target['background']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "vEtHtZOO725v"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
|
|||
|
|
" std=[0.229, 0.224, 0.225])\n",
|
|||
|
|
"def preprocess_image(img):\n",
|
|||
|
|
" img = torch.tensor(img).permute(2,0,1)\n",
|
|||
|
|
" img = normalize(img)\n",
|
|||
|
|
" return img.to(device).float()\n",
|
|||
|
|
"def decode(_y):\n",
|
|||
|
|
" _, preds = _y.max(-1)\n",
|
|||
|
|
" return preds\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "4vLi9hII7-WS"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"class RCNNDataset(Dataset):\n",
|
|||
|
|
" def __init__(self, fpaths, rois, labels, deltas, gtbbs):\n",
|
|||
|
|
" self.fpaths = fpaths\n",
|
|||
|
|
" self.gtbbs = gtbbs\n",
|
|||
|
|
" self.rois = rois\n",
|
|||
|
|
" self.labels = labels\n",
|
|||
|
|
" self.deltas = deltas\n",
|
|||
|
|
" def __len__(self): return len(self.fpaths)\n",
|
|||
|
|
" def __getitem__(self, ix):\n",
|
|||
|
|
" fpath = str(self.fpaths[ix])\n",
|
|||
|
|
" image = cv2.imread(fpath, 1)[...,::-1]\n",
|
|||
|
|
" H, W, _ = image.shape\n",
|
|||
|
|
" sh = np.array([W,H,W,H])\n",
|
|||
|
|
" gtbbs = self.gtbbs[ix]\n",
|
|||
|
|
" rois = self.rois[ix]\n",
|
|||
|
|
" bbs = (np.array(rois)*sh).astype(np.uint16)\n",
|
|||
|
|
" labels = self.labels[ix]\n",
|
|||
|
|
" deltas = self.deltas[ix]\n",
|
|||
|
|
" crops = [image[y:Y,x:X] for (x,y,X,Y) in bbs]\n",
|
|||
|
|
" return image, crops, bbs, labels, deltas, gtbbs, fpath\n",
|
|||
|
|
" def collate_fn(self, batch):\n",
|
|||
|
|
" input, rois, rixs, labels, deltas = [], [], [], [], []\n",
|
|||
|
|
" for ix in range(len(batch)):\n",
|
|||
|
|
" image, crops, image_bbs, image_labels, image_deltas, image_gt_bbs, image_fpath = batch[ix]\n",
|
|||
|
|
" crops = [cv2.resize(crop, (224,224)) for crop in crops]\n",
|
|||
|
|
" crops = [preprocess_image(crop/255.)[None] for crop in crops]\n",
|
|||
|
|
" input.extend(crops)\n",
|
|||
|
|
" labels.extend([label2target[c] for c in image_labels])\n",
|
|||
|
|
" deltas.extend(image_deltas)\n",
|
|||
|
|
" input = torch.cat(input).to(device)\n",
|
|||
|
|
" labels = torch.Tensor(labels).long().to(device)\n",
|
|||
|
|
" deltas = torch.Tensor(deltas).float().to(device)\n",
|
|||
|
|
" return input, labels, deltas\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "dzwT5C-J8G0j"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"n_train = 9*len(FPATHS)//10\n",
|
|||
|
|
"train_ds = RCNNDataset(FPATHS[:n_train], ROIS[:n_train], CLSS[:n_train], DELTAS[:n_train], GTBBS[:n_train])\n",
|
|||
|
|
"test_ds = RCNNDataset(FPATHS[n_train:], ROIS[n_train:], CLSS[n_train:], DELTAS[n_train:], GTBBS[n_train:])\n",
|
|||
|
|
"\n",
|
|||
|
|
"from torch.utils.data import TensorDataset, DataLoader\n",
|
|||
|
|
"train_loader = DataLoader(train_ds, batch_size=2, collate_fn=train_ds.collate_fn, drop_last=True)\n",
|
|||
|
|
"test_loader = DataLoader(test_ds, batch_size=2, collate_fn=test_ds.collate_fn, drop_last=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 732,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"a929d30788fe4772ab578390ce90597e",
|
|||
|
|
"4f05bb580a544d788143bd4fdea8150a",
|
|||
|
|
"804c3bc6fa9e48d9b59ce11e855b0999",
|
|||
|
|
"aaf77f8c6ddc496b8975b648830878e6",
|
|||
|
|
"2bde1618e6a244dba48a884d32e3dcd4",
|
|||
|
|
"9610567dd5d1460f99516aac12d77581",
|
|||
|
|
"1051fab2a4524a0a897e4af2f8186e0d",
|
|||
|
|
"0de938505a4445caa4bff6402a9efa56"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "QcLxxBJm8HUz",
|
|||
|
|
"outputId": "18905158-d20e-4f9a-fc90-d63b444d2708"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"C:\\Users\\frakt\\anaconda3\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
|
|||
|
|
" warnings.warn(\n",
|
|||
|
|
"C:\\Users\\frakt\\anaconda3\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
|
|||
|
|
" warnings.warn(msg)\n",
|
|||
|
|
"Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to C:\\Users\\frakt/.cache\\torch\\hub\\checkpoints\\vgg16-397923af.pth\n",
|
|||
|
|
"100%|███████████████████████████████████████████████████████████████████████████████| 528M/528M [01:04<00:00, 8.64MB/s]\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"VGG(\n",
|
|||
|
|
" (features): Sequential(\n",
|
|||
|
|
" (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (1): ReLU(inplace=True)\n",
|
|||
|
|
" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (3): ReLU(inplace=True)\n",
|
|||
|
|
" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
|||
|
|
" (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (6): ReLU(inplace=True)\n",
|
|||
|
|
" (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (8): ReLU(inplace=True)\n",
|
|||
|
|
" (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
|||
|
|
" (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (11): ReLU(inplace=True)\n",
|
|||
|
|
" (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (13): ReLU(inplace=True)\n",
|
|||
|
|
" (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (15): ReLU(inplace=True)\n",
|
|||
|
|
" (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
|||
|
|
" (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (18): ReLU(inplace=True)\n",
|
|||
|
|
" (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (20): ReLU(inplace=True)\n",
|
|||
|
|
" (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (22): ReLU(inplace=True)\n",
|
|||
|
|
" (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
|||
|
|
" (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (25): ReLU(inplace=True)\n",
|
|||
|
|
" (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (27): ReLU(inplace=True)\n",
|
|||
|
|
" (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
|||
|
|
" (29): ReLU(inplace=True)\n",
|
|||
|
|
" (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
|
|||
|
|
" (classifier): Sequential()\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"vgg_backbone = models.vgg16(pretrained=True)\n",
|
|||
|
|
"vgg_backbone.classifier = nn.Sequential()\n",
|
|||
|
|
"for param in vgg_backbone.parameters():\n",
|
|||
|
|
" param.requires_grad = False\n",
|
|||
|
|
"vgg_backbone.eval().to(device)"
|
|||
|
|
]
|
|||
|
|
},
|
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|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Fs3XfT418aGk"
|
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|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"class RCNN(nn.Module):\n",
|
|||
|
|
" def __init__(self):\n",
|
|||
|
|
" super().__init__()\n",
|
|||
|
|
" feature_dim = 25088\n",
|
|||
|
|
" self.backbone = vgg_backbone\n",
|
|||
|
|
" self.cls_score = nn.Linear(feature_dim, len(label2target))\n",
|
|||
|
|
" self.bbox = nn.Sequential(\n",
|
|||
|
|
" nn.Linear(feature_dim, 512),\n",
|
|||
|
|
" nn.ReLU(),\n",
|
|||
|
|
" nn.Linear(512, 4),\n",
|
|||
|
|
" nn.Tanh(),\n",
|
|||
|
|
" )\n",
|
|||
|
|
" self.cel = nn.CrossEntropyLoss()\n",
|
|||
|
|
" self.sl1 = nn.L1Loss()\n",
|
|||
|
|
" def forward(self, input):\n",
|
|||
|
|
" feat = self.backbone(input)\n",
|
|||
|
|
" cls_score = self.cls_score(feat)\n",
|
|||
|
|
" bbox = self.bbox(feat)\n",
|
|||
|
|
" return cls_score, bbox\n",
|
|||
|
|
" def calc_loss(self, probs, _deltas, labels, deltas):\n",
|
|||
|
|
" detection_loss = self.cel(probs, labels)\n",
|
|||
|
|
" ixs, = torch.where(labels != 0)\n",
|
|||
|
|
" _deltas = _deltas[ixs]\n",
|
|||
|
|
" deltas = deltas[ixs]\n",
|
|||
|
|
" self.lmb = 10.0\n",
|
|||
|
|
" if len(ixs) > 0:\n",
|
|||
|
|
" regression_loss = self.sl1(_deltas, deltas)\n",
|
|||
|
|
" return detection_loss + self.lmb * regression_loss, detection_loss.detach(), regression_loss.detach()\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" regression_loss = 0\n",
|
|||
|
|
" return detection_loss + self.lmb * regression_loss, detection_loss.detach(), regression_loss"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "MiMmezgp9E-s"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def train_batch(inputs, model, optimizer, criterion):\n",
|
|||
|
|
" input, clss, deltas = inputs\n",
|
|||
|
|
" model.train()\n",
|
|||
|
|
" optimizer.zero_grad()\n",
|
|||
|
|
" _clss, _deltas = model(input)\n",
|
|||
|
|
" loss, loc_loss, regr_loss = criterion(_clss, _deltas, clss, deltas)\n",
|
|||
|
|
" accs = clss == decode(_clss)\n",
|
|||
|
|
" loss.backward()\n",
|
|||
|
|
" optimizer.step()\n",
|
|||
|
|
" return loss.detach(), loc_loss, regr_loss, accs.cpu().numpy()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "vNBqA98I9G6O"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"@torch.no_grad()\n",
|
|||
|
|
"def validate_batch(inputs, model, criterion):\n",
|
|||
|
|
" input, clss, deltas = inputs\n",
|
|||
|
|
" with torch.no_grad():\n",
|
|||
|
|
" model.eval()\n",
|
|||
|
|
" _clss,_deltas = model(input)\n",
|
|||
|
|
" loss, loc_loss, regr_loss = criterion(_clss, _deltas, clss, deltas)\n",
|
|||
|
|
" _, _clss = _clss.max(-1)\n",
|
|||
|
|
" accs = clss == _clss\n",
|
|||
|
|
" return _clss, _deltas, loss.detach(), loc_loss, regr_loss, accs.cpu().numpy()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "JIobqz0W9I6s"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"rcnn = RCNN().to(device)\n",
|
|||
|
|
"criterion = rcnn.calc_loss\n",
|
|||
|
|
"optimizer = optim.SGD(rcnn.parameters(), lr=1e-3)\n",
|
|||
|
|
"n_epochs = 5\n",
|
|||
|
|
"log = Report(n_epochs)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 514
|
|||
|
|
},
|
|||
|
|
"id": "hReU_vnH9Kk0",
|
|||
|
|
"outputId": "b5f5094d-eeb6-4aec-fbcd-0ca419b8d0ee"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"C:\\Users\\frakt\\AppData\\Local\\Temp\\ipykernel_27780\\4275183504.py:32: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\torch\\csrc\\utils\\tensor_new.cpp:248.)\n",
|
|||
|
|
" deltas = torch.Tensor(deltas).float().to(device)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"EPOCH: 0.409 trn_loss: 2.199 trn_loc_loss: 0.554 trn_regr_loss: 0.164 trn_acc: 0.792 (821.90s - 9228.49s remaining))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"ename": "KeyboardInterrupt",
|
|||
|
|
"evalue": "",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_27780\\1533349013.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0m_n\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m loss, loc_loss, regr_loss, accs = train_batch(inputs, rcnn, \n\u001b[0m\u001b[0;32m 6\u001b[0m optimizer, criterion)\n\u001b[0;32m 7\u001b[0m \u001b[0mpos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0m_n\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_27780\\1507503000.py\u001b[0m in \u001b[0;36mtrain_batch\u001b[1;34m(inputs, model, optimizer, criterion)\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0m_clss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_deltas\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc_loss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mregr_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_clss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_deltas\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdeltas\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0maccs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclss\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_clss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1499\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1501\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1502\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_27780\\2122026028.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msl1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mL1Loss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mfeat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackbone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 17\u001b[0m \u001b[0mcls_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcls_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[0mbbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1499\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1501\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1502\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torchvision\\models\\vgg.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mavgpool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1499\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1501\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1502\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\container.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 217\u001b[1;33m \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 218\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1499\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1501\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1502\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 461\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 462\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 463\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 465\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m 457\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 458\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[1;32m--> 459\u001b[1;33m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[0;32m 460\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[0;32m 461\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for epoch in range(n_epochs):\n",
|
|||
|
|
"\n",
|
|||
|
|
" _n = len(train_loader)\n",
|
|||
|
|
" for ix, inputs in enumerate(train_loader):\n",
|
|||
|
|
" loss, loc_loss, regr_loss, accs = train_batch(inputs, rcnn, \n",
|
|||
|
|
" optimizer, criterion)\n",
|
|||
|
|
" pos = (epoch + (ix+1)/_n)\n",
|
|||
|
|
" log.record(pos, trn_loss=loss.item(), trn_loc_loss=loc_loss, \n",
|
|||
|
|
" trn_regr_loss=regr_loss, \n",
|
|||
|
|
" trn_acc=accs.mean(), end='\\r')\n",
|
|||
|
|
" \n",
|
|||
|
|
" _n = len(test_loader)\n",
|
|||
|
|
" for ix,inputs in enumerate(test_loader):\n",
|
|||
|
|
" _clss, _deltas, loss, \\\n",
|
|||
|
|
" loc_loss, regr_loss, accs = validate_batch(inputs, \n",
|
|||
|
|
" rcnn, criterion)\n",
|
|||
|
|
" pos = (epoch + (ix+1)/_n)\n",
|
|||
|
|
" log.record(pos, val_loss=loss.item(), val_loc_loss=loc_loss, \n",
|
|||
|
|
" val_regr_loss=regr_loss, \n",
|
|||
|
|
" val_acc=accs.mean(), end='\\r')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Plotting training and validation metrics\n",
|
|||
|
|
"log.plot_epochs('trn_loss,val_loss'.split(','))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "qksBAgNJ9NhK"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def test_predictions(filename, show_output=True):\n",
|
|||
|
|
" img = np.array(cv2.imread(filename, 1)[...,::-1])\n",
|
|||
|
|
" candidates = extract_candidates(img)\n",
|
|||
|
|
" candidates = [(x,y,x+w,y+h) for x,y,w,h in candidates]\n",
|
|||
|
|
" input = []\n",
|
|||
|
|
" for candidate in candidates:\n",
|
|||
|
|
" x,y,X,Y = candidate\n",
|
|||
|
|
" crop = cv2.resize(img[y:Y,x:X], (224,224))\n",
|
|||
|
|
" input.append(preprocess_image(crop/255.)[None])\n",
|
|||
|
|
" input = torch.cat(input).to(device)\n",
|
|||
|
|
" with torch.no_grad():\n",
|
|||
|
|
" rcnn.eval()\n",
|
|||
|
|
" probs, deltas = rcnn(input)\n",
|
|||
|
|
" probs = torch.nn.functional.softmax(probs, -1)\n",
|
|||
|
|
" confs, clss = torch.max(probs, -1)\n",
|
|||
|
|
" candidates = np.array(candidates)\n",
|
|||
|
|
" confs, clss, probs, deltas = [tensor.detach().cpu().numpy() for tensor in [confs, clss, probs, deltas]]\n",
|
|||
|
|
"\n",
|
|||
|
|
" ixs = clss!=background_class\n",
|
|||
|
|
" confs, clss, probs, deltas, candidates = [tensor[ixs] for tensor in [confs, clss, probs, deltas, candidates]]\n",
|
|||
|
|
" bbs = (candidates + deltas).astype(np.uint16)\n",
|
|||
|
|
" ixs = nms(torch.tensor(bbs.astype(np.float32)), torch.tensor(confs), 0.05)\n",
|
|||
|
|
" confs, clss, probs, deltas, candidates, bbs = [tensor[ixs] for tensor in [confs, clss, probs, deltas, candidates, bbs]]\n",
|
|||
|
|
" if len(ixs) == 1:\n",
|
|||
|
|
" confs, clss, probs, deltas, candidates, bbs = [tensor[None] for tensor in [confs, clss, probs, deltas, candidates, bbs]]\n",
|
|||
|
|
" if len(confs) == 0 and not show_output:\n",
|
|||
|
|
" return (0,0,224,224), 'background', 0\n",
|
|||
|
|
" if len(confs) > 0:\n",
|
|||
|
|
" best_pred = np.argmax(confs)\n",
|
|||
|
|
" best_conf = np.max(confs)\n",
|
|||
|
|
" best_bb = bbs[best_pred]\n",
|
|||
|
|
" x,y,X,Y = best_bb\n",
|
|||
|
|
" _, ax = plt.subplots(1, 2, figsize=(20,10))\n",
|
|||
|
|
" show(img, ax=ax[0])\n",
|
|||
|
|
" ax[0].grid(False)\n",
|
|||
|
|
" ax[0].set_title('Original image')\n",
|
|||
|
|
" if len(confs) == 0:\n",
|
|||
|
|
" ax[1].imshow(img)\n",
|
|||
|
|
" ax[1].set_title('No objects')\n",
|
|||
|
|
" plt.show()\n",
|
|||
|
|
" return\n",
|
|||
|
|
" ax[1].set_title(target2label[clss[best_pred]])\n",
|
|||
|
|
" show(img, bbs=bbs.tolist(), texts=[target2label[c] for c in clss.tolist()], ax=ax[1], title='predicted bounding box and class')\n",
|
|||
|
|
" plt.show()\n",
|
|||
|
|
" return (x,y,X,Y),target2label[clss[best_pred]],best_conf"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 350
|
|||
|
|
},
|
|||
|
|
"id": "0qpUkdh4E_zl",
|
|||
|
|
"outputId": "0237ce9e-ddc5-4560-ca48-fd999ca80a52"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
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