Segmentat_pytorch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Segmentat-pytorch.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkRomoBT3Tub",
        "outputId": "f4e59b0c-4b78-498d-c5e4-2bfa73f1674d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rSaYxi5P4QS7",
        "outputId": "50dc5023-964b-498b-be12-9fa0115152b7"
      },
      "source": [
        "import os\n",
        "os.chdir(\"/content/drive/My Drive\")\n",
        "!ls"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'Colab Notebooks'\t  Optic-Disc-Unet-master    Style-Transfer\n",
            " Mask-Wearing-Detection   pytorch_test.ipynb\t    yolov3_with_opencv\n",
            " optic_disc_copy.ipynb\t  PyTorch_Tutorial-master   yolov3_with_opencv.ipynb\n",
            " optic_disc.ipynb\t  Segmentat-pytorch.ipynb\n",
            " Optic-Disc-Unet\t  style-pytorch.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFLula2_4Tng",
        "outputId": "8507df0c-6934-465b-9a32-937b0836bd9b"
      },
      "source": [
        "!git clone https://github.com/Flionay/segmentation-learning-experiment.git"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'segmentation-learning-experiment'...\n",
            "remote: Enumerating objects: 95, done.\u001b[K\n",
            "remote: Total 95 (delta 0), reused 0 (delta 0), pack-reused 95\u001b[K\n",
            "Unpacking objects: 100% (95/95), done.\n",
            "Checking out files: 100% (80/80), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "moHsreeQ4tjO",
        "outputId": "4844b1cb-11e8-47ef-a514-28ff9ba10b58"
      },
      "source": [
        "os.chdir(\"/content/drive/My Drive/segmentation-learning-experiment\")\n",
        "!ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data   model_result  predict.py\t\tREADME.md   train.py\n",
            "model  pic_results   pretrained_models\tresult.txt  utils\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIJS8eaa6RcT"
      },
      "source": [
        "!python3 predict.py"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hmfimhK64qz",
        "outputId": "c0393ea5-fe6e-4778-b54d-5c2fa9eb69ec"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sun Aug 22 08:13:09 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.57.02    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}