Model file update

load_lightning_SD_to_Usual_SD.ipynb

@@ -124,7 +124,7 @@
     {
      "data": {
       "text/html": [
-       "Run data is saved locally in <code>c:\\Users\\vaibh\\OneDrive\\Desktop\\Work\\BigVision\\BLOG_POSTS\\Medical_segmentation\\GRADIO_APP\\UWMGI_Medical_Image_Segmentation\\wandb\\run-20230719_111329-pfqbikbe</code>"
+       "Run data is saved locally in <code>c:\\Users\\vaibh\\OneDrive\\Desktop\\Work\\BigVision\\BLOG_POSTS\\Medical_segmentation\\GRADIO_APP\\UWMGI_Medical_Image_Segmentation\\wandb\\run-20230719_204221-w5qu5rqw</code>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -136,7 +136,7 @@
     {
      "data": {
       "text/html": [
-       "Syncing run <strong><a href='https://wandb.ai/veb-101/UWMGI_Medical_Image_Segmentation/runs/pfqbikbe' target=\"_blank\">generous-music-2</a></strong> to <a href='https://wandb.ai/veb-101/UWMGI_Medical_Image_Segmentation' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+       "Syncing run <strong><a href='https://wandb.ai/veb-101/UWMGI_Medical_Image_Segmentation/runs/w5qu5rqw' target=\"_blank\">ethereal-bush-2</a></strong> to <a href='https://wandb.ai/veb-101/UWMGI_Medical_Image_Segmentation' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -160,7 +160,7 @@
     {
      "data": {
       "text/html": [
-       " View run at <a href='https://wandb.ai/veb-101/UWMGI_Medical_Image_Segmentation/runs/pfqbikbe' target=\"_blank\">https://wandb.ai/veb-101/UWMGI_Medical_Image_Segmentation/runs/pfqbikbe</a>"
+       " View run at <a href='https://wandb.ai/veb-101/UWMGI_Medical_Image_Segmentation/runs/w5qu5rqw' target=\"_blank\">https://wandb.ai/veb-101/UWMGI_Medical_Image_Segmentation/runs/w5qu5rqw</a>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -173,16 +173,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact model-jsr2fn8v:v0, 977.89MB. 1 files... \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact model-fpgquxev:v0, 977.89MB. 1 files... \n",
       "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:3.9\n"
+      "Done. 0:1:5.3\n"
      ]
     }
    ],
    "source": [
     "import wandb\n",
+    "\n",
     "run = wandb.init()\n",
-    "artifact = run.use_artifact(r'veb-101/UM_medical_segmentation/model-jsr2fn8v:v0', type='model')\n",
+    "artifact = run.use_artifact(\"veb-101/UM_medical_segmentation/model-fpgquxev:v0\", type=\"model\")\n",
     "artifact_dir = artifact.download()"
    ]
   },
@@ -280,25 +281,32 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "torch.save(model.state_dict(), \"Segformer_best_state_dict.ckpt\")"
+    "# torch.save(model.state_dict(), \"Segformer_best_state_dict.ckpt\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "model.save_pretrained(\"segformer_trained_weights\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To load the saved model, we simply need to pass the path to the directory \"segformer_trained_weights\"."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = get_model(model_path=os.path.join(os.getcwd(), \"segformer_trained_weights\"), num_classes=Configs.NUM_CLASSES)"
+    "# model = get_model(model_path=os.path.join(os.getcwd(), \"segformer_trained_weights\"), num_classes=Configs.NUM_CLASSES)"
    ]
   }
  ],

segformer_trained_weights/pytorch_model.bin

@@ -1,3 +1,3 @@
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+oid sha256:423ff60b52bdbc5c0ea00f1a5648c42eccf2bdfbab550304bc95e28eb594cf0e
 size 256300245