Update get_coco.sh

EAutodet.ipynb

@@ -0,0 +1,165 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "private_outputs": true,
+      "provenance": [],
+      "machine_shape": "hm",
+      "gpuType": "A100",
+      "authorship_tag": "ABX9TyMvTTM6qJIF9fbb5iHAFlMx",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/aashikrasool/EAutoDet/blob/master/EAutodet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9eP4BfF8OdIX"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/aashikrasool/EAutoDet.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cd EAutoDet"
+      ],
+      "metadata": {
+        "id": "qbCniNOHOnDH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ls\n"
+      ],
+      "metadata": {
+        "id": "gm3JophGOxeE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install thop"
+      ],
+      "metadata": {
+        "id": "NMBr0rxhY_sy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install wandb"
+      ],
+      "metadata": {
+        "id": "Dio2_oXWboTt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!bash data/scripts/get_coco.sh"
+      ],
+      "metadata": {
+        "id": "8Lg26SaJO99O"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!touch logs/EAutoDet-s_test1.log\n"
+      ],
+      "metadata": {
+        "id": "M6YrDoKPcI0z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!bash scripts/search.sh 0"
+      ],
+      "metadata": {
+        "id": "8hnodOlxPFOI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python -u train_search.py --data $data --cfg $cfg --weights '' --epochs 50 --batch-size $BATCHSIZE > $LOG_DIR/${cfg_file}_test1.log 2>&1 &\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "LruE8zGJPNuY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!!python -u train_search.py --data $data --cfg $cfg --weights '' --epochs 50 --batch-size $BATCHSIZE > $LOG_DIR/${cfg_file}_test1.log 2>&1\n"
+      ],
+      "metadata": {
+        "id": "Yq7qlZBtRrpq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!bash scripts/full_train.sh 0"
+      ],
+      "metadata": {
+        "id": "2xK2W_B9SFw7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "RRnUZSD4SlNK"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

data/scripts/get_coco.sh

@@ -8,20 +8,20 @@
 #     /yolov5
 
 # Download/unzip labels
-d='../' # unzip directory
+d='./' # unzip directory
 url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
+f='coco2017labels-segments.zip' # or 'coco2017labels.zip', 68 MB
 echo 'Downloading' $url$f ' ...'
 curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
 
-## Download/unzip images
-#d='../coco/images' # unzip directory
-#url=http://images.cocodataset.org/zips/
-#f1='train2017.zip' # 19G, 118k images
-#f2='val2017.zip'   # 1G, 5k images
-#f3='test2017.zip'  # 7G, 41k images (optional)
-#for f in $f1 $f2; do
-#  echo 'Downloading' $url$f '...'
-#  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
-#done
+# Download/unzip images
+d='./coco/images' # unzip directory
+url=http://images.cocodataset.org/zips/
+f1='train2017.zip' # 19G, 118k images
+f2='val2017.zip'   # 1G, 5k images
+f3='test2017.zip'  # 7G, 41k images (optional)
+for f in $f1 $f2 $f3; do
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
+done
 wait # finish background tasks

train.py

@@ -680,4 +680,3 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         plot_evolution(yaml_file)
         print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
               f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
-