Unet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Unet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "a366aab09bcc415db27c0b14894f7b57": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_fd4c2eae411a44ebaf6cfd30cb84b660",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_0a89d180780b4ee5ab28078fa82a1665",
              "IPY_MODEL_d435bb59191746b2abf8828ab105c04b"
            ]
          }
        },
        "fd4c2eae411a44ebaf6cfd30cb84b660": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "0a89d180780b4ee5ab28078fa82a1665": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_31ef05df67bb4aae960e07447ba9717c",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 100,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 100,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_f0fe3872ca39443d8424944a2bfac1f5"
          }
        },
        "d435bb59191746b2abf8828ab105c04b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_982063bdba2f46d8ae10b4447c8a0e2d",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 100/100 [00:28&lt;00:00,  3.53it/s, train_loss=0.404]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_023bde5ac63d4d89afe4583491a7f43f"
          }
        },
        "31ef05df67bb4aae960e07447ba9717c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "f0fe3872ca39443d8424944a2bfac1f5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "982063bdba2f46d8ae10b4447c8a0e2d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "023bde5ac63d4d89afe4583491a7f43f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "0a7f5fa1fc9947dbbd5f309e0a786578": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_d12a44cbddf84316b44b4538b3943698",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_2afce2ea38fc404385f29c228e25f011",
              "IPY_MODEL_a9e548d83afb4884ad62504b656e1e51"
            ]
          }
        },
        "d12a44cbddf84316b44b4538b3943698": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "2afce2ea38fc404385f29c228e25f011": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_ad44526226b340c1a3c82295d9d15cb1",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 99,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 99,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_860692611ee046358d26431a059895f6"
          }
        },
        "a9e548d83afb4884ad62504b656e1e51": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_9ecc39817eac484490b0d1ec585ed186",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 99/99 [00:18&lt;00:00,  5.21it/s, valid_loss=0.527, dice_score=[0.32189575 0.4468914  0.9358416 ]]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_2bc8ea36d83145808aaa66cca8cce307"
          }
        },
        "ad44526226b340c1a3c82295d9d15cb1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "860692611ee046358d26431a059895f6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "9ecc39817eac484490b0d1ec585ed186": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "2bc8ea36d83145808aaa66cca8cce307": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "b4a566801360447e88ea0782acc4efa8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_f672cf27aa4c4b1a8ed12bc9fb1bfd42",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_9b1efd2d321849709f9c2175c0b4a0ce",
              "IPY_MODEL_6da4fd55503b45e7b547f1f4dad2375e"
            ]
          }
        },
        "f672cf27aa4c4b1a8ed12bc9fb1bfd42": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "9b1efd2d321849709f9c2175c0b4a0ce": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_33e8b10b60ff4f6ba7d74e9dbc5c1c4f",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 100,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 100,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d32e063442f64da89ae728a8d7d3c47b"
          }
        },
        "6da4fd55503b45e7b547f1f4dad2375e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_5d1fa6b6254f4ee5ac102bcb161aefa0",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 100/100 [00:14&lt;00:00,  7.14it/s, train_loss=0.583]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_1f265bf9d01c463e975d20b468a045e9"
          }
        },
        "33e8b10b60ff4f6ba7d74e9dbc5c1c4f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "d32e063442f64da89ae728a8d7d3c47b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "5d1fa6b6254f4ee5ac102bcb161aefa0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "1f265bf9d01c463e975d20b468a045e9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "4f66f3a1a9294a93914bf15f1fd9ddc6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_a6f0255d20534317b6e355054e4f8474",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_93c04a08541a4d17a4188f4e6d4fefe7",
              "IPY_MODEL_ad8336c95d7241858f1f3c91835b97f9"
            ]
          }
        },
        "a6f0255d20534317b6e355054e4f8474": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "93c04a08541a4d17a4188f4e6d4fefe7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_e3fb24ac56924b7bb57b54cdc6427013",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 99,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 99,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_6494cab95a47424c930b98a409dd72ca"
          }
        },
        "ad8336c95d7241858f1f3c91835b97f9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_8e2d8671bc6749fa9936f93cd5c65311",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 99/99 [00:05&lt;00:00, 18.76it/s, valid_loss=0.42, dice_score=[0.4572085 0.5520201 0.9511922]]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_68823c67611f4fd790c4e725f1a8e0fd"
          }
        },
        "e3fb24ac56924b7bb57b54cdc6427013": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "6494cab95a47424c930b98a409dd72ca": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "8e2d8671bc6749fa9936f93cd5c65311": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "68823c67611f4fd790c4e725f1a8e0fd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "74024cb7d3224b8091b81be718ac40e1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "state": {
            "_view_name": "VBoxView",
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_name": "VBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_cd865169a19d4b438b65ee18b8999cc5",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_dfd267a0d97848459fa6f514da15d1fd",
              "IPY_MODEL_8c64857e931343df906d066a8a7026b7"
            ]
          }
        },
        "cd865169a19d4b438b65ee18b8999cc5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "dfd267a0d97848459fa6f514da15d1fd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "state": {
            "_options_labels": [
              "0",
              "1",
              "2",
              "3",
              "4",
              "5",
              "6",
              "7",
              "8",
              "9",
              "10",
              "11",
              "12",
              "13",
              "14",
              "15",
              "16",
              "17",
              "18",
              "19",
              "20",
              "21",
              "22",
              "23",
              "24",
              "25",
              "26",
              "27",
              "28",
              "29",
              "30",
              "31",
              "32",
              "33",
              "34",
              "35",
              "36",
              "37",
              "38",
              "39",
              "40",
              "41",
              "42",
              "43",
              "44",
              "45",
              "46",
              "47",
              "48",
              "49",
              "50",
              "51",
              "52",
              "53",
              "54",
              "55",
              "56",
              "57",
              "58",
              "59",
              "60",
              "61",
              "62",
              "63",
              "64",
              "65",
              "66",
              "67",
              "68",
              "69",
              "70",
              "71",
              "72",
              "73",
              "74",
              "75",
              "76",
              "77",
              "78",
              "79",
              "80",
              "81",
              "82",
              "83",
              "84",
              "85",
              "86",
              "87",
              "88",
              "89",
              "90",
              "91",
              "92",
              "93",
              "94",
              "95",
              "96",
              "97",
              "98",
              "99",
              "100",
              "101",
              "102",
              "103",
              "104",
              "105",
              "106",
              "107",
              "108",
              "109",
              "110",
              "111",
              "112",
              "113",
              "114",
              "115",
              "116",
              "117",
              "118",
              "119",
              "120",
              "121",
              "122",
              "123",
              "124",
              "125",
              "126",
              "127",
              "128",
              "129",
              "130",
              "131",
              "132",
              "133",
              "134",
              "135",
              "136",
              "137",
              "138",
              "139",
              "140",
              "141",
              "142",
              "143",
              "144",
              "145",
              "146",
              "147",
              "148",
              "149",
              "150",
              "151",
              "152",
              "153",
              "154",
              "155",
              "156",
              "157",
              "158",
              "159",
              "160",
              "161",
              "162",
              "163",
              "164",
              "165",
              "166",
              "167",
              "168",
              "169",
              "170",
              "171",
              "172",
              "173",
              "174",
              "175",
              "176",
              "177",
              "178",
              "179",
              "180",
              "181",
              "182",
              "183",
              "184",
              "185",
              "186",
              "187",
              "188",
              "189",
              "190",
              "191",
              "192",
              "193",
              "194",
              "195",
              "196",
              "197",
              "198",
              "199",
              "200",
              "201",
              "202",
              "203",
              "204",
              "205",
              "206",
              "207",
              "208",
              "209",
              "210",
              "211",
              "212",
              "213",
              "214",
              "215",
              "216",
              "217",
              "218",
              "219",
              "220",
              "221",
              "222",
              "223",
              "224",
              "225",
              "226",
              "227",
              "228",
              "229",
              "230",
              "231",
              "232",
              "233",
              "234",
              "235",
              "236",
              "237",
              "238",
              "239",
              "240",
              "241",
              "242",
              "243",
              "244",
              "245",
              "246",
              "247",
              "248",
              "249",
              "250",
              "251",
              "252",
              "253",
              "254",
              "255",
              "256",
              "257",
              "258",
              "259",
              "260",
              "261",
              "262",
              "263",
              "264",
              "265",
              "266",
              "267",
              "268",
              "269",
              "270",
              "271",
              "272",
              "273",
              "274",
              "275",
              "276",
              "277",
              "278",
              "279",
              "280",
              "281",
              "282",
              "283",
              "284",
              "285",
              "286",
              "287",
              "288",
              "289",
              "290",
              "291",
              "292",
              "293",
              "294",
              "295",
              "296",
              "297",
              "298",
              "299",
              "300",
              "301",
              "302",
              "303",
              "304",
              "305",
              "306",
              "307",
              "308",
              "309",
              "310",
              "311",
              "312",
              "313",
              "314",
              "315",
              "316",
              "317",
              "318",
              "319",
              "320",
              "321",
              "322",
              "323",
              "324",
              "325",
              "326",
              "327",
              "328",
              "329",
              "330",
              "331",
              "332",
              "333",
              "334",
              "335",
              "336",
              "337",
              "338",
              "339",
              "340",
              "341",
              "342",
              "343",
              "344",
              "345",
              "346",
              "347",
              "348",
              "349",
              "350",
              "351",
              "352",
              "353",
              "354",
              "355",
              "356",
              "357",
              "358",
              "359",
              "360",
              "361",
              "362",
              "363",
              "364",
              "365",
              "366",
              "367",
              "368",
              "369",
              "370",
              "371",
              "372",
              "373",
              "374",
              "375",
              "376",
              "377",
              "378",
              "379",
              "380",
              "381",
              "382",
              "383",
              "384",
              "385",
              "386",
              "387",
              "388",
              "389",
              "390",
              "391",
              "392",
              "393",
              "394",
              "395",
              "396",
              "397"
            ],
            "_view_name": "DropdownView",
            "style": "IPY_MODEL_cf747ce4245c4db6915b34a971d66eb4",
            "_dom_classes": [],
            "description": "instance_id",
            "_model_name": "DropdownModel",
            "index": 0,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "disabled": false,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_f2641d7631ab48d1a81cbb0dc1f67fd2"
          }
        },
        "8c64857e931343df906d066a8a7026b7": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "state": {
            "_view_name": "OutputView",
            "msg_id": "",
            "_dom_classes": [],
            "_model_name": "OutputModel",
            "outputs": [
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": [],
                  "needs_background": "light"
                },
                "image/png": "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\n",
                "text/plain": "<Figure size 432x288 with 1 Axes>"
              },
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": []
                },
                "application/vnd.jupyter.widget-view+json": {
                  "model_id": "3cc37680308c45a994bc9379d861cea8",
                  "version_minor": 0,
                  "version_major": 2
                },
                "text/plain": "interactive(children=(Checkbox(value=False, description='show_all_modules'), Output()), _dom_classes=('widget-…"
              }
            ],
            "_view_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_view_count": null,
            "_view_module_version": "1.0.0",
            "layout": "IPY_MODEL_04feb2df37a24367ae21b3e34d9a94b5",
            "_model_module": "@jupyter-widgets/output"
          }
        },
        "cf747ce4245c4db6915b34a971d66eb4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "f2641d7631ab48d1a81cbb0dc1f67fd2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "3cc37680308c45a994bc9379d861cea8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "state": {
            "_view_name": "VBoxView",
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_name": "VBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_ca585240a93c4ff09eef2fce2f37eceb",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_224179086ae746bdb89914160c9048e5",
              "IPY_MODEL_580d8a1b7db74618886b6667080664f6"
            ]
          }
        },
        "04feb2df37a24367ae21b3e34d9a94b5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "ca585240a93c4ff09eef2fce2f37eceb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "224179086ae746bdb89914160c9048e5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "CheckboxModel",
          "state": {
            "_view_name": "CheckboxView",
            "style": "IPY_MODEL_0e71d00e405f44aeb542321efa2f3c29",
            "_dom_classes": [],
            "description": "show_all_modules",
            "_model_name": "CheckboxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": false,
            "_view_count": null,
            "disabled": false,
            "_view_module_version": "1.5.0",
            "indent": true,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_be3ca20ac1cc4b4eb552e8e0e1f0dee7"
          }
        },
        "580d8a1b7db74618886b6667080664f6": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "state": {
            "_view_name": "OutputView",
            "msg_id": "",
            "_dom_classes": [],
            "_model_name": "OutputModel",
            "outputs": [
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": []
                },
                "application/vnd.jupyter.widget-view+json": {
                  "model_id": "35a189612e6c452d8319da89a9901d14",
                  "version_minor": 0,
                  "version_major": 2
                },
                "text/plain": "interactive(children=(Dropdown(description='module', options=(('', UNet(\n  (inc): DoubleConv(\n    (res_conv): …"
              }
            ],
            "_view_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_view_count": null,
            "_view_module_version": "1.0.0",
            "layout": "IPY_MODEL_2e856c88861549c29327e302ad3a4991",
            "_model_module": "@jupyter-widgets/output"
          }
        },
        "0e71d00e405f44aeb542321efa2f3c29": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "be3ca20ac1cc4b4eb552e8e0e1f0dee7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "35a189612e6c452d8319da89a9901d14": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "state": {
            "_view_name": "VBoxView",
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_name": "VBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_f16b124bf8ce43a184ea22d46e74919b",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_2d6b52d63da14432a89fb7c27451af85",
              "IPY_MODEL_74d47bde6e5b45feaa910c2e433d8034"
            ]
          }
        },
        "2e856c88861549c29327e302ad3a4991": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "f16b124bf8ce43a184ea22d46e74919b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "2d6b52d63da14432a89fb7c27451af85": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "state": {
            "_options_labels": [
              "",
              "inc",
              "down1",
              "down2",
              "down3",
              "down4",
              "up1",
              "up2",
              "up3",
              "up4",
              "outc"
            ],
            "_view_name": "DropdownView",
            "style": "IPY_MODEL_e626b08471344aa6a81b51b1c8095b28",
            "_dom_classes": [],
            "description": "module",
            "_model_name": "DropdownModel",
            "index": 0,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "disabled": false,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_7a6b30e58d134646a0c43d0052b5642d"
          }
        },
        "74d47bde6e5b45feaa910c2e433d8034": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "state": {
            "_view_name": "OutputView",
            "msg_id": "",
            "_dom_classes": [],
            "_model_name": "OutputModel",
            "outputs": [
              {
                "output_type": "stream",
                "metadata": {
                  "tags": []
                },
                "text": "(1, 3, 128, 128)\n",
                "stream": "stdout"
              },
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": []
                },
                "application/vnd.jupyter.widget-view+json": {
                  "model_id": "e3ceea5902834f5c973d41ed0a6332a4",
                  "version_minor": 0,
                  "version_major": 2
                },
                "text/plain": "interactive(children=(Dropdown(description='cmap', options=('gray', 'viridis', 'plasma', 'inferno', 'magma', '…"
              }
            ],
            "_view_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_view_count": null,
            "_view_module_version": "1.0.0",
            "layout": "IPY_MODEL_7664795123c749c4b3d64b18621cc3cf",
            "_model_module": "@jupyter-widgets/output"
          }
        },
        "e626b08471344aa6a81b51b1c8095b28": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "7a6b30e58d134646a0c43d0052b5642d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "e3ceea5902834f5c973d41ed0a6332a4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "state": {
            "_view_name": "VBoxView",
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_name": "VBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_90ba180f20944976add84edd6d24b547",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_508b650e14264cdda10b393ffa51fdd9",
              "IPY_MODEL_f5d1d17f815549d29a3c99f2fc756b77",
              "IPY_MODEL_2de8f778469440c3946e8a4071bc4da5",
              "IPY_MODEL_42fff0fa0119489687b0c863f845099d",
              "IPY_MODEL_51ebd4624a504b9292b39f3a3ac497fd"
            ]
          }
        },
        "7664795123c749c4b3d64b18621cc3cf": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "90ba180f20944976add84edd6d24b547": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "508b650e14264cdda10b393ffa51fdd9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "state": {
            "_options_labels": [
              "gray",
              "viridis",
              "plasma",
              "inferno",
              "magma",
              "cividis"
            ],
            "_view_name": "DropdownView",
            "style": "IPY_MODEL_2c25a5d881ac467092d72e45c5bfe979",
            "_dom_classes": [],
            "description": "cmap",
            "_model_name": "DropdownModel",
            "index": 0,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "disabled": false,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_c6795eb462bf49228824041081351dff"
          }
        },
        "f5d1d17f815549d29a3c99f2fc756b77": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntSliderModel",
          "state": {
            "_view_name": "IntSliderView",
            "style": "IPY_MODEL_7725729e21074831b9ea2cd9f8166dcb",
            "_dom_classes": [],
            "description": "channel",
            "step": 1,
            "_model_name": "IntSliderModel",
            "orientation": "horizontal",
            "max": 2,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "disabled": false,
            "_view_module_version": "1.5.0",
            "min": 0,
            "continuous_update": true,
            "readout_format": "d",
            "description_tooltip": null,
            "readout": true,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_2fac9540f2974106acd560e23c884007"
          }
        },
        "2de8f778469440c3946e8a4071bc4da5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "CheckboxModel",
          "state": {
            "_view_name": "CheckboxView",
            "style": "IPY_MODEL_5a17ccac655745e6a65b47c6f31a1c48",
            "_dom_classes": [],
            "description": "show_gradient",
            "_model_name": "CheckboxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": false,
            "_view_count": null,
            "disabled": false,
            "_view_module_version": "1.5.0",
            "indent": true,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_944bcbd16ad54e48b72877704c56091d"
          }
        },
        "42fff0fa0119489687b0c863f845099d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatSliderModel",
          "state": {
            "_view_name": "FloatSliderView",
            "style": "IPY_MODEL_8bafbd1e77dc48049bc076b9b05a0d6b",
            "_dom_classes": [],
            "description": "range",
            "step": 0.1,
            "_model_name": "FloatSliderModel",
            "orientation": "horizontal",
            "max": 10,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 5.5,
            "_view_count": null,
            "disabled": false,
            "_view_module_version": "1.5.0",
            "min": 1,
            "continuous_update": true,
            "readout_format": ".2f",
            "description_tooltip": null,
            "readout": true,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e27043497c204bda92c9a55f7532a3a8"
          }
        },
        "51ebd4624a504b9292b39f3a3ac497fd": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "state": {
            "_view_name": "OutputView",
            "msg_id": "",
            "_dom_classes": [],
            "_model_name": "OutputModel",
            "outputs": [
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": [],
                  "needs_background": "light"
                },
                "image/png": "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\n",
                "text/plain": "<Figure size 864x864 with 2 Axes>"
              }
            ],
            "_view_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_view_count": null,
            "_view_module_version": "1.0.0",
            "layout": "IPY_MODEL_6eb08760e54844108efc1380b224458d",
            "_model_module": "@jupyter-widgets/output"
          }
        },
        "2c25a5d881ac467092d72e45c5bfe979": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "c6795eb462bf49228824041081351dff": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "7725729e21074831b9ea2cd9f8166dcb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "state": {
            "_view_name": "StyleView",
            "handle_color": null,
            "_model_name": "SliderStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "2fac9540f2974106acd560e23c884007": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "5a17ccac655745e6a65b47c6f31a1c48": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "944bcbd16ad54e48b72877704c56091d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "8bafbd1e77dc48049bc076b9b05a0d6b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "state": {
            "_view_name": "StyleView",
            "handle_color": null,
            "_model_name": "SliderStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "e27043497c204bda92c9a55f7532a3a8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "6eb08760e54844108efc1380b224458d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/guptavasu1213/glaucoma-diagnosis/blob/master/Unet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lznmlk1Sp63A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e4b9fd88-89b7-4b6f-b8f8-f9be477d6ada"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YdQ_-eYqDm_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5BbpT4mqGcL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%load_ext tensorboard\n",
        "logs_base_dir = \"drive/Shared drives/Capstone Summer 2020/Models/Unet_From_Scratch/runs\"\n",
        "os.makedirs(logs_base_dir, exist_ok=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Anf6lgMcqINP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d32b4aba-79b5-465d-d8a2-7268f954f9de"
      },
      "source": [
        "from PIL import Image\n",
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import defaultdict\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from torch.optim import lr_scheduler\n",
        "from torch.utils.data import Dataset as BaseDataset\n",
        "from torchvision.transforms import functional\n",
        "from torchvision import transforms\n",
        "from tqdm.notebook import tqdm \n",
        "import time\n",
        "\n",
        "import albumentations as A\n",
        "from albumentations.augmentations import transforms as a_transforms\n",
        "import random\n",
        "random.seed(0xDEADBEEF)\n",
        "torch.manual_seed(0xDEADBEEF)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fc902744228>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAdb2LROqQ9s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ROOT_PATH = 'drive/Shared drives/Capstone Summer 2020/Data/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-HCHXXhXqUHA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !rm drive/Shared\\ drives/Capstone\\ Summer\\ 2020/Models/Unet_From_Scratch/runs/events*"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtgjKLwjF7dM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from ipywidgets import interact, interactive, fixed, interact_manual\n",
        "import ipywidgets as widgets\n",
        "\n",
        "class VisModel:\n",
        "    def __init__(self, model, criterion, optimizer, device, dataloaders, dataset_sizes):\n",
        "        self.model = model\n",
        "        self.criterion = criterion\n",
        "        self.optimizer = optimizer\n",
        "        self.device = device\n",
        "        self.dataloaders = dataloaders\n",
        "        self.dataset_sizes = dataset_sizes\n",
        "        interact(self.choose_instance, instance_id=range(dataset_sizes['Training']))\n",
        "            \n",
        "    def choose_instance(self, instance_id):\n",
        "        self.original_image = self.dataloaders['Training'][instance_id][0]\n",
        "        self.mask = self.dataloaders['Training'][instance_id][1]\n",
        "        \n",
        "        plt.imshow(transforms.ToPILImage()(self.original_image.detach().cpu()), interpolation=\"bicubic\")\n",
        "\n",
        "        interact(self.filter_modules, show_all_modules=False)\n",
        "        \n",
        "    def filter_modules(self, show_all_modules):\n",
        "        modules = []\n",
        "        for name, module in self.model.named_modules():\n",
        "            if not show_all_modules and '.' in name:\n",
        "                continue\n",
        "            modules.append((name, module))\n",
        "        interact(self.select_module, module=modules)\n",
        "        \n",
        "    def forward_module_hook(self, module, input, output):\n",
        "        if isinstance(output, tuple):\n",
        "            activations = []\n",
        "            for activation in output:\n",
        "                print(activation.shape)\n",
        "                activations.append(np.flip(activation.detach().cpu().numpy(), axis=2).squeeze())\n",
        "            self.activation = np.expand_dims(np.array(activations), 0)\n",
        "        else:\n",
        "            self.activation = np.flip(output.detach().cpu().numpy(), axis=2)\n",
        "\n",
        "    def backward_module_hook(self, module, input, output):\n",
        "        output = output[0]\n",
        "        self.gradient = np.flip(output.detach().cpu().numpy(), axis=2)\n",
        "\n",
        "    def select_module(self, module):\n",
        "        forward_hook_handle = module.register_forward_hook(self.forward_module_hook)\n",
        "        backward_hook_handle = module.register_backward_hook(self.backward_module_hook)\n",
        "        \n",
        "        self.original_image = self.original_image.to(self.device)\n",
        "        self.mask = self.mask.to(self.device)\n",
        "\n",
        "        output = self.model(self.original_image.unsqueeze(0))\n",
        "        loss = self.criterion(output, self.mask.squeeze(1).type(torch.long))\n",
        "        self.optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "\n",
        "        forward_hook_handle.remove()\n",
        "        backward_hook_handle.remove()\n",
        "        \n",
        "        print(self.activation.shape)\n",
        "        C = self.activation.shape[1]\n",
        "        cmaps = ['gray', \"viridis\", 'plasma', 'inferno', 'magma', 'cividis']\n",
        "        interact(self.activation_plot, cmap=cmaps, channel=(0, C - 1), show_gradient=False, range=(1.0, 10.0))\n",
        "        \n",
        "    def activation_plot(self, cmap, channel, show_gradient, range):\n",
        "        if show_gradient:\n",
        "            image = self.gradient\n",
        "        else:\n",
        "            image = self.activation\n",
        "\n",
        "        plt.figure(figsize=(12, 12))\n",
        "        plt.tick_params(which='both', bottom=False, top=False, left=False, labelbottom=False, labelleft=False)\n",
        "        mean = np.mean(image[0, channel])\n",
        "        std = np.std(image[0, channel])\n",
        "        plt.imshow(image[0, channel], cmap=cmap, vmin=mean-std * range, vmax=mean + std * range, interpolation='nearest', origin='lower')\n",
        "        plt.colorbar(shrink=0.5)\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4q843Cxc7nL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import the necessary packages\n",
        "from imutils import contours\n",
        "from skimage import measure\n",
        "\n",
        "import argparse\n",
        "import imutils\n",
        "\n",
        "# from google.colab.patches import cv2_imshow\n",
        "\n",
        "\n",
        "\n",
        "def point_check(pt):\n",
        "  newpt = [0,0]\n",
        "  newpt[0] = 0 if pt[0] < 0 else pt[0]\n",
        "  newpt[1] = 0 if pt[1] < 0 else pt[1]\n",
        "  return tuple(newpt)\n",
        "\n",
        "output_dim = 512\n",
        "def cropONH(img_path):\n",
        "\n",
        "\timage = PIL.Image.open(img_path)\n",
        "\tim_w, im_h = image.size\n",
        "\tvar = round(0.15 * im_w)\n",
        "\tstarting_threshold = 250\n",
        "\twhile True:\n",
        "\t\t# load the image, convert it to grayscale, and blur it\n",
        "\t\timage = cv2.imread(img_path)\n",
        "\n",
        "\t\t# image=cv2.resize(image,(800,615))\n",
        "\t\tgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "\t\tblur = cv2.bilateralFilter(gray,9,75,75)\n",
        "\t\tmedian=cv2.medianBlur(gray,5)\n",
        "\n",
        "\t\t# cv2_imshow(median)\n",
        "\n",
        "\t\t# threshold the image to reveal light regions in the\n",
        "\t\t# blurred image\n",
        "\t\tthresh = cv2.threshold(median, starting_threshold, 255, cv2.THRESH_BINARY)[1]\n",
        "\t\t# cv2_imshow(thresh)\n",
        "\n",
        "\t\t# perform a series of erosions and dilations to remove\n",
        "\t\t# any small blobs of noise from the thresholded image\n",
        "\t\tthresh = cv2.erode(thresh, None, iterations=2)\n",
        "\t\tthresh = cv2.dilate(thresh, None, iterations=4)\n",
        "\n",
        "\t\t# perform a connected component analysis on the thresholded\n",
        "\t\t# image, then initialize a mask to store only the \"large\"\n",
        "\t\t# components\n",
        "\t\tlabels = measure.label(thresh, connectivity=2, background=0)\n",
        "\t\tmask = np.zeros(thresh.shape, dtype=\"uint8\")\n",
        "\t\t# return\n",
        "\t\t# print(labels)\n",
        "\t\t# print(np.unique(labels))\n",
        "\t\t\n",
        "\t\tlargest_blob = 0\n",
        "\t\t# loop over the unique components\n",
        "\t\tfor label in np.unique(labels):\n",
        "\t\t\t# if this is the background label, ignore it\n",
        "\t\t\tif label == 0:\n",
        "\t\t\t\tcontinue\n",
        "\t\t\t# print(\"Creating mask\")\n",
        "\t\t\t# otherwise, construct the label mask and count the\n",
        "\t\t\t# number of pixels \n",
        "\t\t\tlabelMask = np.zeros(thresh.shape, dtype=\"uint8\")\n",
        "\t\t\tlabelMask[labels == label] = 255\n",
        "\t\t\tnumPixels = cv2.countNonZero(labelMask)\n",
        "\t\t\t# cv2_imshow(labelMask)\n",
        "\t\t\t# print('==============')\n",
        "\t\t\t# if the number of pixels in the component is sufficiently\n",
        "\t\t\t# large, then add it to our mask of \"large blobs\"\n",
        "\t\t\tif numPixels > largest_blob:\n",
        "\t\t\t\tlargest_blob = numPixels \n",
        "\t\t\t\tmask = labelMask\n",
        "\t\t\t# if numPixels > 100:\n",
        "\t\t\t# \tmask = cv2.add(mask, labelMask)\n",
        "\n",
        "\t\t# cv2_imshow(mask)\n",
        "\t\t# return\n",
        "\t\t# find the contours in the mask, then sort them from left to\n",
        "\t\t# right\n",
        "\t\tcnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,\n",
        "\t\t\tcv2.CHAIN_APPROX_SIMPLE)\n",
        "\t\t# cv2.drawContours(image, cnts, -1, (0,255,0), 3)\n",
        "\t\t# return\n",
        "\t\tcnts = imutils.grab_contours(cnts)\n",
        "\t\n",
        "\t\t#If there is nothing found for the image\n",
        "\t\tif cnts == []:\n",
        "\t\t\tstarting_threshold -= 10\n",
        "\t\t\t# print(\"Decreasing threshold by 10. Now:\", starting_threshold)\n",
        "\t\t\tcontinue\n",
        "\t\t# print(cnts)\n",
        "\n",
        "\t\t# cv2_imshow(mask)\n",
        "\t\tcnts = contours.sort_contours(cnts)[0]\n",
        "\t\t# loop over the contours\n",
        "\t\tfor (i, c) in enumerate(cnts):\n",
        "\t\t\t# ellipse = cv2.fitEllipse(c)\n",
        "\t\t\t(x, y, w, h) = cv2.boundingRect(c)\n",
        "\n",
        "\t\t\tcenter = (round(x+(w/2)), round(y+(h/2)))\n",
        "\t\t\t\n",
        "\t\t\tcv2.putText(image, \"O\", center,\n",
        "\t\t\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)\n",
        "   \n",
        "\t\t\tbox_radius = output_dim//2\n",
        "\n",
        "\t\t\ttl_pt = [center[0]-box_radius, center[1]-box_radius]\n",
        "\t\t\tbr_pt = [center[0]+box_radius, center[1]+box_radius]\n",
        "\n",
        "\t\t\t#Check if TL point is out of bounds\n",
        "\t\t\tif tl_pt[0] < 0:\n",
        "\t\t\t\tneg = tl_pt[0] * -1\n",
        "\t\t\t\ttl_pt[0] = 0\n",
        "\t\t\t\tbr_pt[0] += neg\n",
        "\n",
        "\t\t\tif tl_pt[1] < 0:\n",
        "\t\t\t\tneg = tl_pt[1] * -1\n",
        "\t\t\t\ttl_pt[1] = 0\n",
        "\t\t\t\tbr_pt[1] += neg\n",
        "\n",
        "\t\t\t#Check if BR point is out of bounds\n",
        "\t\t\tif br_pt[0] > im_w:\n",
        "\t\t\t\tpos = im_w-br_pt[0]\n",
        "\t\t\t\tbr_pt[0] = im_w\n",
        "\t\t\t\ttl_pt[0] -= pos\n",
        "\t\t\tif br_pt[1] > im_h:\n",
        "\t\t\t\tpos = im_h-br_pt[1]\n",
        "\t\t\t\tbr_pt[1] = im_h\n",
        "\t\t\t\ttl_pt[1] -= pos\n",
        "\n",
        "\n",
        "\t\t\tbreak\n",
        "\t\tbreak\n",
        "\treturn tl_pt[0], tl_pt[1], br_pt[0], br_pt[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZEe8IOZZ2Auw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# inputWidth, inputHeight = 640, 640"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIUacg1HcZiL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def crop(img_path, img):\n",
        "  x1, y1, x2, y2 = cropONH(img_path)\n",
        "  return img.crop((x1, y1, x2, y2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQ8bUrtzqVO6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# this dataset already contains the transforms\n",
        "def fix_mask(mask):\n",
        "  mask[mask == 128] = 1\n",
        "  mask[mask == 255] = 2 # This should swap the black and white values\n",
        "  return mask\n",
        "\n",
        "\n",
        "def normalize(data):\n",
        "  m = torch.mean(data)\n",
        "  s = torch.std(data)\n",
        "  return (data - m) / s\n",
        "\n",
        "def augment():\n",
        "  return A.Compose([a_transforms.RandomBrightnessContrast(),\n",
        "                    a_transforms.RGBShift(),\n",
        "                    a_transforms.RandomContrast(),\n",
        "                    a_transforms.RandomGamma(),\n",
        "                    a_transforms.RandomRotate90(),\n",
        "                    a_transforms.Transpose(),\n",
        "                    a_transforms.ShiftScaleRotate()\n",
        "                    ])\n",
        "\n",
        "class Dataset(BaseDataset):\n",
        "  def __init__(self, root_dir, is_train=True):\n",
        "    self.root_dir = root_dir\n",
        "    self.images_dir = os.path.join(root_dir, 'Images')\n",
        "    self.masks_dir = os.path.join(root_dir, 'Masks')\n",
        "    self.ids = os.listdir(self.images_dir)\n",
        "    self.maskids = os.listdir(self.masks_dir)\n",
        "    self.is_train = is_train\n",
        "\n",
        "  @classmethod\n",
        "  def preprocess(cls, pil_img):\n",
        "    # pil_img = pil_img.resize((128, 128), resample=Image.NEAREST)\n",
        "\n",
        "    # img_nd = np.array(pil_img)\n",
        "    img_nd = pil_img\n",
        "\n",
        "    if len(img_nd.shape) == 2:\n",
        "      img_nd = np.expand_dims(img_nd, axis=2)\n",
        "  \n",
        "    # HWC to CHW\n",
        "    img_trans = img_nd.transpose((2, 0, 1))\n",
        "    if img_trans.max() > 1:\n",
        "      img_trans = img_trans / 255\n",
        "\n",
        "    return img_trans\n",
        "  \n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    #load images and masks\n",
        "    img_path = os.path.join(self.images_dir, self.ids[idx])\n",
        "    mask_path = os.path.join(self.masks_dir, self.maskids[idx])\n",
        "    img = Image.open(img_path).convert(\"RGB\").resize((128, 128), resample=Image.NEAREST)\n",
        "    mask = Image.open(mask_path).resize((128, 128), resample=Image.NEAREST)\n",
        "\n",
        "    # pil_img = pil_img.resize((128, 128), resample=Image.NEAREST)\n",
        "\n",
        "    img = np.array(img)\n",
        "    mask = np.array(mask)\n",
        "    if self.is_train:\n",
        "      augmented = augment()(image=img, mask=mask)\n",
        "      img = augmented['image']\n",
        "      mask = augmented['mask']\n",
        "\n",
        "    label = 0 if self.ids[idx][0] == 'n' else 1 # this isn't being used at the moment\n",
        "\n",
        "    img = self.preprocess(img)\n",
        "    mask = self.preprocess(mask)*2\n",
        "\n",
        "    # print(img.min(), img.max())\n",
        "    img = torch.from_numpy(img).type(torch.FloatTensor)\n",
        "    mask = torch.from_numpy(mask).long()\n",
        "    img = normalize(img)\n",
        "    # print(img.min(), img.max())\n",
        "    return img, mask \n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.ids)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qKmpy1xqjPG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "98d7c2ae-74a1-4771-b3ea-49bc0343bed4"
      },
      "source": [
        "batch_size = 4\n",
        "datasets = {'Training': Dataset(os.path.join(ROOT_PATH, 'Training')),\n",
        "            'Validation': Dataset(os.path.join(ROOT_PATH, 'Validation'), is_train=False)}\n",
        "dataloaders = {x: DataLoader(datasets[x], batch_size=batch_size, shuffle=True, num_workers=4) for x in ['Training', 'Validation']}\n",
        "dataset_sizes = {x: len(datasets[x]) for x in ['Training', 'Validation'] }\n",
        "n_train = dataset_sizes['Training']\n",
        "n_val = dataset_sizes['Validation']\n",
        "\n",
        "print(n_val)\n",
        "# testing the gt\n",
        "d = datasets['Training'][0][0]\n",
        "# print(np.unique(d))\n",
        "print(d.shape[0])\n",
        "\n",
        "plt.imshow(d[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "396\n",
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc89cf817b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BRSr4mCYWuG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# batch_size = 4\n",
        "# dataset = Dataset(os.path.join(ROOT_PATH, 'Training'))\n",
        "# n_train = round(len(dataset)*0.85)\n",
        "# n_val = len(dataset) - n_train\n",
        "# print(n_train,n_val)\n",
        "# dataset_sizes = {'Training': n_train, 'Validation': n_val}\n",
        "\n",
        "# train, val = torch.utils.data.random_split(dataset, [n_train, n_val])\n",
        "# dataloaders = {'Training': DataLoader(train, batch_size=batch_size, shuffle=True, num_workers=4),\n",
        "#                'Validation': DataLoader(val, batch_size=batch_size, shuffle=True, num_workers=4)}\n",
        "# print(len(train))\n",
        "# plt.imshow(train[0][0][0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JG9itM7Uq1zW",
        "colab_type": "text"
      },
      "source": [
        "#Unet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7m2Y577sq0cx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\" Parts of the U-Net model \"\"\"\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class DoubleConv(nn.Module):\n",
        "  \"\"\"(convolution => [BN] => ReLU) * 2\"\"\"\n",
        "\n",
        "  def __init__(self, in_channels, out_channels, mid_channels=None, residual=True):\n",
        "    super().__init__()\n",
        "    if not mid_channels:\n",
        "      mid_channels = out_channels\n",
        "\n",
        "    self.res_conv = None\n",
        "    if residual:\n",
        "      self.res_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0)\n",
        "    \n",
        "    self.double_conv = nn.Sequential(\n",
        "      nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),\n",
        "      nn.BatchNorm2d(mid_channels),\n",
        "      nn.ReLU(inplace=True),\n",
        "      # nn.LeakyReLU(negative_slope = 0.01, inplace=True),\n",
        "      nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),\n",
        "      nn.BatchNorm2d(out_channels),\n",
        "      nn.ReLU(inplace=True))\n",
        "      # nn.LeakyReLU(negative_slope = 0.01, inplace=True))\n",
        "      # nn.Dropout2d(p=0.25)) #TWEAK HYPERPARAMETER P \n",
        "\n",
        "  def forward(self, x):\n",
        "    x_in = x\n",
        "    x = self.double_conv(x)\n",
        "\n",
        "    if self.res_conv is not None:\n",
        "      x = x + self.res_conv(x_in)\n",
        "\n",
        "    return x\n",
        "\n",
        "\n",
        "class Down(nn.Module):\n",
        "  \"\"\"Downscaling with maxpool then double conv\"\"\"\n",
        "\n",
        "  def __init__(self, in_channels, out_channels):\n",
        "    super().__init__()\n",
        "    self.maxpool_conv = nn.Sequential(\n",
        "      #nn.MaxPool2d(2),\n",
        "      nn.AvgPool2d(2),\n",
        "      DoubleConv(in_channels, out_channels))\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.maxpool_conv(x)\n",
        "\n",
        "\n",
        "class Up(nn.Module):\n",
        "  \"\"\"Upscaling then double conv\"\"\"\n",
        "\n",
        "  def __init__(self, in_channels, out_channels, bilinear=True):\n",
        "    super().__init__()\n",
        "\n",
        "    # if bilinear, use the normal convolutions to reduce the number of channels\n",
        "    if bilinear:\n",
        "      self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)\n",
        "      self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)\n",
        "    else:\n",
        "      self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)\n",
        "      self.conv = DoubleConv(in_channels, out_channels)\n",
        "\n",
        "\n",
        "  def forward(self, x1, x2):\n",
        "    x1 = self.up(x1)\n",
        "    # # input is CHW\n",
        "    # diffY = x2.size()[2] - x1.size()[2]\n",
        "    # diffX = x2.size()[3] - x1.size()[3]\n",
        "\n",
        "    # x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,\n",
        "    #                 diffY // 2, diffY - diffY // 2])\n",
        "    # if you have padding issues, see\n",
        "    # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a\n",
        "    # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd\n",
        "    x = torch.cat([x2, x1], dim=1)\n",
        "    return self.conv(x)\n",
        "\n",
        "\n",
        "class OutConv(nn.Module):\n",
        "  def __init__(self, in_channels, out_channels):\n",
        "    super(OutConv, self).__init__()\n",
        "    self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=5) # try kernel_size to 3 for faster conversions also put padding=1\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.conv(x)\n",
        "\n",
        "class UNet(nn.Module):\n",
        "  def __init__(self, n_channels, n_classes, bilinear=True):\n",
        "    super(UNet, self).__init__()\n",
        "    self.n_channels = n_channels\n",
        "    self.n_classes = n_classes\n",
        "    self.bilinear = bilinear\n",
        "\n",
        "    self.inc = DoubleConv(n_channels, 64)\n",
        "    self.down1 = Down(64, 128)\n",
        "    self.down2 = Down(128, 256)\n",
        "    self.down3 = Down(256, 512)\n",
        "    factor = 2 if bilinear else 1\n",
        "    self.down4 = Down(512, 1024 // factor)\n",
        "    self.up1 = Up(1024, 512 // factor, bilinear)\n",
        "    self.up2 = Up(512, 256 // factor, bilinear)\n",
        "    self.up3 = Up(256, 128 // factor, bilinear)\n",
        "    self.up4 = Up(128, 64, bilinear)\n",
        "    # self.outc = OutConv(64, n_classes)\n",
        "    self.outc = nn.Conv2d(64, n_classes, kernel_size=1, padding = 0) # try kernel_size to 3 for faster conversions also put padding=1\n",
        "\n",
        "  def forward(self, x):\n",
        "    x1 = self.inc(x)\n",
        "    x2 = self.down1(x1)\n",
        "    x3 = self.down2(x2)\n",
        "    x4 = self.down3(x3)\n",
        "    x5 = self.down4(x4)\n",
        "    x = self.up1(x5, x4)\n",
        "    x = self.up2(x, x3)\n",
        "    x = self.up3(x, x2)\n",
        "    x = self.up4(x, x1)\n",
        "    logits = self.outc(x)\n",
        "    return logits"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1EMFpmILGF_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def one_hot(x, num_classes):\n",
        "  return F.one_hot(x, num_classes=num_classes).permute(0, 3, 1, 2).float()\n",
        "\n",
        "def saturate_probabilities(x):\n",
        "  return one_hot(torch.argmax(x, dim=1), num_classes=x.shape[1])\n",
        "\n",
        "def dice_metric(prediction, target):\n",
        "  N, C, H, W = prediction.shape\n",
        "  spatial_dims = (2, 3)\n",
        "\n",
        "  prediction = saturate_probabilities(prediction)\n",
        "  target = one_hot(target.squeeze(1), C)\n",
        "\n",
        "  overlap = torch.sum(prediction * target, dim=spatial_dims)\n",
        "  total = torch.sum(target, dim=spatial_dims) + torch.sum(prediction, dim=spatial_dims)\n",
        "\n",
        "  dice_coeffs = 2 * overlap / total \n",
        "  dice_coeffs = torch.mean(dice_coeffs, dim=0)\n",
        "\n",
        "  return dice_coeffs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HufMLsBbPk3d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class HybridLogisticDiceLoss3D(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def forward(self, prediction, target):\n",
        "    N, C, H, W = prediction.size()\n",
        "    spatial_dims = (2, 3)\n",
        "\n",
        "    target = one_hot(target, C)\n",
        "\n",
        "    overlap = torch.sum(prediction * target, dim=spatial_dims)\n",
        "\n",
        "    total = torch.sum(target * target, dim=spatial_dims) + torch.sum(prediction * prediction, dim=spatial_dims)\n",
        "\n",
        "    dice_coeffs = 2 * overlap / total\n",
        "   \n",
        "    # small eps to avoid blowing up if the model ever starts getting really confident and predicts a 0.0 score\n",
        "    # This was happening around epoch 20 for NestedUNet\n",
        "    eps = 1e-8\n",
        "    logistic = torch.sum(target * (torch.log(prediction + eps) - eps), dim=spatial_dims) / (H * W)\n",
        "\n",
        "    hybrid = -logistic - dice_coeffs + 1\n",
        "\n",
        "    return torch.sum(torch.sum(hybrid, dim=1) / C) / N"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0RU4ALrrz6b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, n_epochs = 3):\n",
        "  start = time.time()\n",
        "  train_loss_list = []\n",
        "  valid_loss_list = []\n",
        "  dice_score_list = []\n",
        "  lr_rate_list = []\n",
        "  valid_loss_min = np.Inf # track change in validation loss\n",
        "\n",
        "  writer = SummaryWriter(log_dir = logs_base_dir)\n",
        "\n",
        "  for epoch in range(1, n_epochs+1):\n",
        "\n",
        "    # keep track of training and validation loss\n",
        "    train_loss = 0.0\n",
        "    valid_loss = 0.0\n",
        "    dice_score = 0.0\n",
        "    ###################\n",
        "    # train the model #\n",
        "    ###################\n",
        "    model.train()\n",
        "    bar = tqdm(dataloaders['Training'], postfix={\"train_loss\":0.0})\n",
        "    for data, target in bar:\n",
        "      # move tensors to GPU if CUDA is available\n",
        "\n",
        "      data, target = data.to(device), target.to(device)\n",
        "\n",
        "      # clear the gradients of all optimized variables\n",
        "      optimizer.zero_grad()\n",
        "      # forward pass: compute predicted outputs by passing inputs to the model\n",
        "\n",
        "      output = model(data)\n",
        "\n",
        "      output = nn.Softmax(dim=1)(output)\n",
        "\n",
        "      # calculate the batch loss\n",
        "      loss = criterion(output, target.squeeze(1).type(torch.long))\n",
        "      \n",
        "      # backward pass: compute gradient of the loss with respect to model parameters\n",
        "      loss.backward()\n",
        "      # perform a single optimization step (parameter update)\n",
        "      optimizer.step()\n",
        "      # update training loss\n",
        "      train_loss += loss.item()*data.size(0)\n",
        "      bar.set_postfix(ordered_dict={\"train_loss\":loss.item()})\n",
        "\n",
        "    ######################    \n",
        "    # validate the model #\n",
        "    ######################\n",
        "    model.eval()\n",
        "    # del data, target\n",
        "    with torch.no_grad():\n",
        "      bar = tqdm(dataloaders['Validation'], postfix={\"valid_loss\":0.0, \"dice_score\":0.0})\n",
        "      for data, target in bar:\n",
        "        # move tensors to GPU if CUDA is available\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        # forward pass: compute predicted outputs by passing inputs to the model\n",
        "        output = model(data)\n",
        "        output = nn.Softmax(dim=1)(output)\n",
        "        # calculate the batch loss\n",
        "        loss = criterion(output, target.squeeze(1).type(torch.long))\n",
        "        # update average validation loss \n",
        "        valid_loss += loss.item()*data.size(0)\n",
        "\n",
        "        # dice_cof = dice_no_threshold(output.cpu(), target.cpu()).item()\n",
        "        dice_cof = dice_metric(output, target).cpu().numpy()\n",
        "        dice_score +=  dice_cof * data.size(0)\n",
        "        bar.set_postfix(ordered_dict={\"valid_loss\":loss.item(), \"dice_score\":dice_cof})\n",
        "    \n",
        "    # calculate average losses\n",
        "    train_loss = train_loss/n_train\n",
        "    valid_loss = valid_loss/n_val\n",
        "    dice_score = dice_score/n_val\n",
        "    train_loss_list.append(train_loss)\n",
        "    valid_loss_list.append(valid_loss)\n",
        "    dice_score_list.append(dice_score)\n",
        "    lr_rate_list.append([param_group['lr'] for param_group in optimizer.param_groups])\n",
        "    \n",
        "    # print training/validation statistics \n",
        "    print('Epoch: {}  Training Loss: {:.6f}  Validation Loss: {:.6f} Dice Score: {}'.format(\n",
        "        epoch, train_loss, valid_loss, dice_score))\n",
        "    \n",
        "    # save model if validation loss has decreased\n",
        "    if valid_loss <= valid_loss_min:\n",
        "        print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n",
        "        valid_loss_min,\n",
        "        valid_loss))\n",
        "        torch.save(model.state_dict(), 'drive/Shared drives/Capstone Summer 2020/Models/Unet_From_Scratch/model_cifar.pt')\n",
        "        valid_loss_min = valid_loss\n",
        "    \n",
        "    scheduler.step()\n",
        "\n",
        "  time_elapsed = time.time() - start\n",
        "  print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n",
        "  torch.save(model.state_dict(), 'drive/Shared drives/Capstone Summer 2020/Models/Unet_From_Scratch/Laroy_UNet-Crop.pth')\n",
        "  return model, train_loss_list, valid_loss_list, dice_score_list, lr_rate_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "he_OzYMCsVcN",
        "colab_type": "text"
      },
      "source": [
        "##TODO\n",
        "*change criterion to hybriddiceloss function provided by cory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTUIw5r_sPN1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = UNet(3,3)\n",
        "model.to(device)\n",
        "\n",
        "lr = 0.0004125\n",
        "weights = [1.0, 1.0, 0.75]\n",
        "class_weights = torch.FloatTensor(weights).to(device)\n",
        "\n",
        "# criterion = nn.CrossEntropyLoss()\n",
        "criterion = HybridLogisticDiceLoss3D()\n",
        "optimizer = optim.Adam(model.parameters(), lr=lr)\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=1000, gamma=0.1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1sTy9oyskLt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381,
          "referenced_widgets": [
            "a366aab09bcc415db27c0b14894f7b57",
            "fd4c2eae411a44ebaf6cfd30cb84b660",
            "0a89d180780b4ee5ab28078fa82a1665",
            "d435bb59191746b2abf8828ab105c04b",
            "31ef05df67bb4aae960e07447ba9717c",
            "f0fe3872ca39443d8424944a2bfac1f5",
            "982063bdba2f46d8ae10b4447c8a0e2d",
            "023bde5ac63d4d89afe4583491a7f43f",
            "0a7f5fa1fc9947dbbd5f309e0a786578",
            "d12a44cbddf84316b44b4538b3943698",
            "2afce2ea38fc404385f29c228e25f011",
            "a9e548d83afb4884ad62504b656e1e51",
            "ad44526226b340c1a3c82295d9d15cb1",
            "860692611ee046358d26431a059895f6",
            "9ecc39817eac484490b0d1ec585ed186",
            "2bc8ea36d83145808aaa66cca8cce307",
            "b4a566801360447e88ea0782acc4efa8",
            "f672cf27aa4c4b1a8ed12bc9fb1bfd42",
            "9b1efd2d321849709f9c2175c0b4a0ce",
            "6da4fd55503b45e7b547f1f4dad2375e",
            "33e8b10b60ff4f6ba7d74e9dbc5c1c4f",
            "d32e063442f64da89ae728a8d7d3c47b",
            "5d1fa6b6254f4ee5ac102bcb161aefa0",
            "1f265bf9d01c463e975d20b468a045e9",
            "4f66f3a1a9294a93914bf15f1fd9ddc6",
            "a6f0255d20534317b6e355054e4f8474",
            "93c04a08541a4d17a4188f4e6d4fefe7",
            "ad8336c95d7241858f1f3c91835b97f9",
            "e3fb24ac56924b7bb57b54cdc6427013",
            "6494cab95a47424c930b98a409dd72ca",
            "8e2d8671bc6749fa9936f93cd5c65311",
            "68823c67611f4fd790c4e725f1a8e0fd"
          ]
        },
        "outputId": "409f6aef-e190-41bc-8fbb-c69ff3be5bc2"
      },
      "source": [
        " model, train_loss_list, valid_loss_list, dice_score_list, lr_rate_list = train_model(model, criterion, optimizer, exp_lr_scheduler, n_epochs=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a366aab09bcc415db27c0b14894f7b57",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: UserWarning: Mixed memory format inputs detected while calling the operator. The operator will output contiguous tensor even if some of the inputs are in channels_last format. (Triggered internally at  /pytorch/aten/src/ATen/native/TensorIterator.cpp:918.)\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: UserWarning: Mixed memory format inputs detected while calling the operator. The operator will output channels_last tensor even if some of the inputs are not in channels_last format. (Triggered internally at  /pytorch/aten/src/ATen/native/TensorIterator.cpp:924.)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a7f5fa1fc9947dbbd5f309e0a786578",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch: 1  Training Loss: 0.498048  Validation Loss: 0.409477 Dice Score: [0.36547434 0.5471781  0.9525571 ]\n",
            "Validation loss decreased (inf --> 0.409477).  Saving model ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b4a566801360447e88ea0782acc4efa8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f66f3a1a9294a93914bf15f1fd9ddc6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=99.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch: 2  Training Loss: 0.442700  Validation Loss: 0.391349 Dice Score: [0.46944323 0.54550755 0.9552465 ]\n",
            "Validation loss decreased (0.409477 --> 0.391349).  Saving model ...\n",
            "Training complete in 0m 29s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBjR8_7hsoH4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "164304f8-1aad-4366-8407-70c3cdf5266c"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "plt.plot([i[0] for i in lr_rate_list])\n",
        "plt.ylabel('learing rate during training', fontsize=22)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "grq2w5b6sq_-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "8f763406-0ce5-48ad-9809-1fbc62ee00db"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "plt.plot(train_loss_list,  marker='o', label=\"Training Loss\")\n",
        "plt.plot(valid_loss_list,  marker='o', label=\"Validation Loss\")\n",
        "plt.ylabel('loss', fontsize=22)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKUcN3BNstI9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "1a255694-0971-4928-fa33-dda4205600fb"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "plt.plot(dice_score_list)\n",
        "plt.ylabel('Dice score')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3fm4K41s7Jr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load best model\n",
        "model = UNet(3,3)\n",
        "model.load_state_dict(torch.load('drive/Shared drives/Capstone Summer 2020/Models/Unet_From_Scratch/Laroy_UNet-Crop.pth')) # this is the final epoch saved\n",
        "# model.load_state_dict(torch.load('drive/Shared drives/Capstone Summer 2020/Models/Unet_From_Scratch/model_cifar.pt')) # This is the best model saved!\n",
        "model.eval();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0bkRJB6fqXZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "74024cb7d3224b8091b81be718ac40e1",
            "cd865169a19d4b438b65ee18b8999cc5",
            "dfd267a0d97848459fa6f514da15d1fd",
            "8c64857e931343df906d066a8a7026b7",
            "cf747ce4245c4db6915b34a971d66eb4",
            "f2641d7631ab48d1a81cbb0dc1f67fd2",
            "3cc37680308c45a994bc9379d861cea8",
            "04feb2df37a24367ae21b3e34d9a94b5",
            "ca585240a93c4ff09eef2fce2f37eceb",
            "224179086ae746bdb89914160c9048e5",
            "580d8a1b7db74618886b6667080664f6",
            "0e71d00e405f44aeb542321efa2f3c29",
            "be3ca20ac1cc4b4eb552e8e0e1f0dee7",
            "35a189612e6c452d8319da89a9901d14",
            "2e856c88861549c29327e302ad3a4991",
            "f16b124bf8ce43a184ea22d46e74919b",
            "2d6b52d63da14432a89fb7c27451af85",
            "74d47bde6e5b45feaa910c2e433d8034",
            "e626b08471344aa6a81b51b1c8095b28",
            "7a6b30e58d134646a0c43d0052b5642d",
            "e3ceea5902834f5c973d41ed0a6332a4",
            "7664795123c749c4b3d64b18621cc3cf",
            "90ba180f20944976add84edd6d24b547",
            "508b650e14264cdda10b393ffa51fdd9",
            "f5d1d17f815549d29a3c99f2fc756b77",
            "2de8f778469440c3946e8a4071bc4da5",
            "42fff0fa0119489687b0c863f845099d",
            "51ebd4624a504b9292b39f3a3ac497fd",
            "2c25a5d881ac467092d72e45c5bfe979",
            "c6795eb462bf49228824041081351dff",
            "7725729e21074831b9ea2cd9f8166dcb",
            "2fac9540f2974106acd560e23c884007",
            "5a17ccac655745e6a65b47c6f31a1c48",
            "944bcbd16ad54e48b72877704c56091d",
            "8bafbd1e77dc48049bc076b9b05a0d6b",
            "e27043497c204bda92c9a55f7532a3a8",
            "6eb08760e54844108efc1380b224458d"
          ]
        },
        "outputId": "1ae92e6f-8c33-41fe-dd3d-849185fd0304"
      },
      "source": [
        "model.to(device)\n",
        "# model.load_state_dict(torch.load('drive/Shared drives/Capstone Summer 2020/Models/Unet_From_Scratch/Laroy_UNet-Crop.pth'))\n",
        "\n",
        "VisModel(model, criterion, optimizer, device, datasets, dataset_sizes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74024cb7d3224b8091b81be718ac40e1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(Dropdown(description='instance_id', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<__main__.VisModel at 0x7fc89d029438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGM5g-ZisjiW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def decode_segmap(image, nc=3):\n",
        "  \n",
        "  label_colors = np.array([(0, 0, 0), (130, 130, 130), (255, 255, 255)])\n",
        "\n",
        "  r = np.zeros_like(image).astype(np.uint8)\n",
        "  g = np.zeros_like(image).astype(np.uint8)\n",
        "  b = np.zeros_like(image).astype(np.uint8)\n",
        "  \n",
        "  for l in range(0, nc):\n",
        "    idx = image == l\n",
        "    r[idx] = label_colors[l, 0]\n",
        "    g[idx] = label_colors[l, 1]\n",
        "    b[idx] = label_colors[l, 2]\n",
        "    \n",
        "  rgb = np.stack([r, g, b], axis=2)\n",
        "  return rgb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPMQ6JTut6W7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "0edd45cd-fb3b-4c9f-9941-16cdd6090a9c"
      },
      "source": [
        "imageName = \"V0001\"\n",
        "folderName= \"Validation\"\n",
        "\n",
        "img_path = \"drive/Shared drives/Capstone Summer 2020/Data/\"+ folderName +\"/Images/\"+ imageName +\".jpg\"\n",
        "img = np.array(Image.open(img_path).convert(\"RGB\").resize((128, 128), resample=Image.NEAREST))\n",
        "\n",
        "img = torch.from_numpy(Dataset.preprocess(img))\n",
        "img = img.to(device)\n",
        "img = img.type(torch.cuda.FloatTensor)\n",
        "img = img.unsqueeze(0)\n",
        "\n",
        "img = normalize(img)\n",
        "\n",
        "mask_path = \"drive/Shared drives/Capstone Summer 2020/Data/\"+ folderName +\"/Masks/\"+ imageName +\".bmp\"\n",
        "ground_truth = np.array(Image.open(mask_path).resize((128, 128), resample=Image.NEAREST))\n",
        "\n",
        "ground_truth = Dataset.preprocess(ground_truth)*2\n",
        "\n",
        "# ground_truth = ground_truth.resize((408, 408), resample=Image.NEAREST)\n",
        "# ground_truth = fix_mask(np.array(ground_truth))\n",
        "ground_truth = torch.from_numpy(ground_truth).long()[0]\n",
        "print(\"UNIQUE MASK:\", np.unique(ground_truth))\n",
        "\n",
        "with torch.no_grad():\n",
        "  result = model(img)\n",
        "\n",
        "mask = torch.argmax(result, dim=1)\n",
        "\n",
        "mask = mask[0]\n",
        "\n",
        "full_mask = mask.detach().cpu().numpy()\n",
        "\n",
        "rgb = decode_segmap(torch.argmax(result.squeeze(), dim=0).detach().cpu().numpy(), nc=3)\n",
        "\n",
        "plt.imshow(rgb)\n",
        "\n",
        "\n",
        "#Plotting all predicted masks\n",
        "ch1_mask = result[0][0].detach().cpu().numpy()\n",
        "ch2_mask = result[0][1].detach().cpu().numpy()\n",
        "ch3_mask = result[0][2].detach().cpu().numpy()\n",
        "\n",
        "fig, axs = plt.subplots(1, 4, figsize=(22,5))\n",
        "\n",
        "\n",
        "#plot original image\n",
        "axs[0].imshow((ch1_mask), cmap='gray')\n",
        "axs[0].axis('off')   \n",
        "axs[0].set_title('Ch 1')\n",
        "\n",
        "#plot ground truth\n",
        "axs[1].imshow((ch2_mask), cmap='gray')\n",
        "axs[1].axis('off')   \n",
        "axs[1].set_title('Ch 2')\n",
        "\n",
        "#plot the mask\n",
        "axs[2].imshow((ch3_mask), cmap='gray')\n",
        "axs[2].axis('off')   \n",
        "axs[2].set_title('Ch 3')\n",
        "\n",
        "axs[3].imshow((ground_truth), cmap='gray')\n",
        "axs[3].axis('off')   \n",
        "axs[3].set_title('GT')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "UNIQUE MASK: [0 1 2]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GT')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1584x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ejsJ4vRaGxc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "8f35f4e7-3900-465e-d145-e403b143ba02"
      },
      "source": [
        "# img = img.type(torch.cuda.FloatTensor)\n",
        "print(result.shape, ground_truth.unsqueeze(0).shape)\n",
        "\n",
        "dice_metric(result, ground_truth.unsqueeze(0).unsqueeze(0).to(device))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 3, 128, 128]) torch.Size([1, 128, 128])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.8602, 0.7507, 0.9747], device='cuda:0')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgK3c4Q7C4J9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "outputId": "705cbc97-9288-4ce1-f87d-9f95acf14dda"
      },
      "source": [
        "print(np.unique(full_mask))\n",
        "print(full_mask.shape)\n",
        "plt.figure(figsize=(12, 12))\n",
        "plt.tick_params(which='both', bottom=False, top=False, left=False, labelbottom=False, labelleft=False)\n",
        "plt.imshow(full_mask, cmap=\"gray\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 2]\n",
            "(128, 128)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc816189470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcmtFOLDIkZx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "adfe279b-31c7-4148-f524-26f1026cbbed"
      },
      "source": [
        "batch_size = 4\n",
        "dataset = Dataset(os.path.join(ROOT_PATH, 'Testing'))\n",
        "x1 = round(len(dataset)*0.95)\n",
        "testing_size = len(dataset) - x1\n",
        "print('Size of testing:', testing_size)\n",
        "\n",
        "\n",
        "train, val = torch.utils.data.random_split(dataset, [x1, testing_size])\n",
        "testing = DataLoader(train, batch_size=batch_size, shuffle=True, num_workers=4)\n",
        "\n",
        "model.eval()\n",
        "\n",
        "for data, target in testing:\n",
        "  data = data.to(device)\n",
        "  target = target.to(device)\n",
        "  with torch.no_grad():\n",
        "    output = model(data)\n",
        "  dice_metric(output, target).cpu().numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Size of testing: 20\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}