updated Reparameterization weight path & added steps for doing Reparameterization ,also fixed few typos (WongKinYiu#552)

* Update reparameterization.ipynb

* Update reparameterization.ipynb

* Update reparameterization.ipynb

* Update reparameterization.ipynb

* Update reparameterization.ipynb

* Add files via upload

* Update userdata.sh

fix typo of yolov7

Author: akashAD98

tools/reparameterization.ipynb

@@ -8,6 +8,30 @@
     "# Reparameterization"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9725e211",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### What is Reparameterization ?\n",
+    "Reparameterization is used to reduce trainable BoF modules into deploy model for fast inference. For example merge BN to conv, merge YOLOR to conv, ..etc\n",
+    "However, before reparameterization, the model has more parameters and computation cost.reparameterized model (cfg/deploy) used for deployment purpose\n",
+    "\n",
+    "\n",
+    "\n",
+    "### Steps required for model conversion.\n",
+    "1.train custom model & you will get your own weight i.e custom_weight.pt / use (pretrained weight which is available i.e yolov7_traing.pt)\n",
+    "\n",
+    "2.Converting this weight using  Reparameterization  method.\n",
+    "\n",
+    "3.Trained model (cfg/training) and reparameterized model (cfg/deploy) will get same prediction results.\n",
+    "However, before reparameterization, the model has more parameters and computation cost.\n",
+    "\n",
+    "4.Convert reparameterized weight into onnx & tensorrt\n",
+    "For faster inference & deployment purpose."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "13393b70",
@@ -32,7 +56,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7_training.pt', map_location=device)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -94,7 +118,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7x.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7x_trainig.pt', map_location=device)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7x.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -156,7 +180,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7-w6.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7-w6_trainig.pt', map_location=device)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7-w6.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -328,7 +352,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7-d6.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7-d6_trainig.pt', map_location=device)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7-d6.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -414,7 +438,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7-e6e.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7-e6e_trainig.pt', map_location=device)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7-e6e.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -487,7 +511,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.0 ('py37')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -501,7 +525,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.9.7"
   },
   "vscode": {
    "interpreter": {

utils/aws/userdata.sh

@@ -7,8 +7,8 @@
 cd home/ubuntu
 if [ ! -d yolor ]; then
   echo "Running first-time script." # install dependencies, download COCO, pull Docker
-  git clone -b paper https://github.com/WongKinYiu/yolor && sudo chmod -R 777 yolor
-  cd yolor
+  git clone -b main https://github.com/WongKinYiu/yolov7 && sudo chmod -R 777 yolov7
+  cd yolov7
   bash data/scripts/get_coco.sh && echo "Data done." &
   sudo docker pull nvcr.io/nvidia/pytorch:21.08-py3 && echo "Docker done." &
   python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &