examples/other/ReadmeExamples.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "c3c3afbe",
      "metadata": {
        "id": "c3c3afbe",
        "outputId": "43f24cb5-6825-436c-f214-47b5bd81ba9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pytorch-adapt[ignite,lightning]\n",
            "  Downloading pytorch_adapt-0.0.61-py3-none-any.whl (137 kB)\n",
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            "  Downloading pytorch_ignite-0.5.0.dev20220221-py3-none-any.whl (254 kB)\n",
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            "Collecting aiosignal>=1.1.2\n",
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            "\u001b[?25hCollecting frozenlist>=1.1.1\n",
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            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pytorch-metric-learning>=1.1.0->pytorch-adapt[ignite,lightning]) (1.4.1)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->pytorch-adapt[ignite,lightning]) (7.1.2)\n",
            "Building wheels for collected packages: future\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491070 sha256=1e62193844f4dc4ee5a79abd258613cce113689582ebe13b73b6f3b38538ab87\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n",
            "Successfully built future\n",
            "Installing collected packages: setuptools, multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, pyDeprecate, fsspec, aiohttp, torchmetrics, PyYAML, pytorch-metric-learning, future, pytorch-lightning, pytorch-ignite, pytorch-adapt\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 57.4.0\n",
            "    Uninstalling setuptools-57.4.0:\n",
            "      Successfully uninstalled setuptools-57.4.0\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: future\n",
            "    Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed PyYAML-6.0 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 frozenlist-1.3.0 fsspec-2022.2.0 future-0.18.2 multidict-6.0.2 pyDeprecate-0.3.1 pytorch-adapt-0.0.61 pytorch-ignite-0.5.0.dev20220221 pytorch-lightning-1.5.10 pytorch-metric-learning-1.2.0 setuptools-59.5.0 torchmetrics-0.7.2 yarl-1.7.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pkg_resources"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install pytorch-adapt[lightning,ignite]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e49fb4b2",
      "metadata": {
        "id": "e49fb4b2"
      },
      "source": [
        "### Load a toy dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "27bf5505",
      "metadata": {
        "scrolled": true,
        "id": "27bf5505",
        "outputId": "c312f958-7a18-472c-e717-9117e437a2a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509,
          "referenced_widgets": [
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            "507927123ec64cd58cfaf2779eb643d2",
            "6170e8b61d9f415a8858db68442b3ebe"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "edb261e8e6274208a6232f68ef4a36c1",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "752e6d5d137f4a2aa57472208a4c6ce0",
              "version_minor": 0,
              "version_major": 2
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "79be6edf1e264dc18904246bd134f67f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a0cc1714db747319c8595917f38d1a4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading https://public.boxcloud.com/d/1/b1!Yahew78_D-XP1uDDRyc1EwSddH6LM6D669aYCix0DRYH0C4LjKg8kdHBOBPVK_FN3s7JkSFEDmK9v0mG0hDPIX7LpSPF1KWNbr8q9cEZ8uj76zrlAbgF_TPYCrT16isD4dzakp8obnOatsOGlJEy-PzB12BQFj1WLs17aB-SsnyqD5OiccevpzFTXhoDAy9QdfYaeUndO6XLFJD66UByxs6e3C4j2iDeO1xvw7uWjXqM-3Mlo6KSNdfZpGxJej5LYAYcPfImj1kPdPL_tFkjGW26R6nN8rt9lS9fyQ1Y9EfzWbUOpQGPjY7oGqsvSYcWBF1FH9BjnzABnl-QvlCU4ck9Jkcdx_e8j3APxe0VLnHy2wFRa9Ttf_SOoRV9tN7wpIZtpMt0ttxcUa_yuqv2gAxX2j1uoCM3NQeHbhI3hCKEjzaLKJjIPJdifzdf1M9g3So1DY96YuGfpjMiQwT2FR9yW1rIgbSLK2cxFylp0KQi2qGJe-FyHzkpN0ewKLFREdOL1KtTAK_GchkF-y45mL8S8-rAjOqx_1-mtcL9DR7w_udyVOOrqxqSy-goHblGYJaJyT04LxRxgEPXl0tWgJDiPevl6QTe_xhpRDLw1gyloJRmDofXOOO5RvhBKPC8YNSMu3pcLQX9ksgiXCnyMtXi9qhIb397ROSEUprM4gkX-a2MC9210HmVlZD-1TC29dcE_cOhy6hKUKnkCkmI94zTxrVlSVSD07faXi7QUGxm2Ldch2MD0UYvRK6Dit8UTt6B2Q9dzir6lk-APQKirivptoaoFVP0sFQWP7aqvQYZ16B_WA8KeFJGarlzaF3Kim7fdCCs_77t99FDWch2RltbVEJlVroVpBzxCHIZdk6gVTuKgnnvpSjxVED29MTsx8OXqkzOtNpke6IoRZ3cUiekvueB87o4ubK9Jj9wTmku6aI5sIwjMnEdekLggCm3czwYffNsnYOZ2Qc7pxUQTTFncY52Gk4V1OoZx_KniI1lXXeDZu_6JXznzIAyejOVCUEAJUn0MdCxuHBoPcRrTbnzZ94Sr0ySMKXDFcv41638FJImQtaqn4M2HS5ylUEcRSx3CLKOLQQXS8_9vFvhRcAWvbqQQICFpBLHZMSGDiwBwuPqtOq3Oqvm6HPMkWX63vamRE-D41KSfsp4rUC_0LlKer1yMz9-NpXkoyU2gB4kzKJP_clmodrhQcoP5m97ec3QA-tr29pqpiPPhH3aRnFz9iKeurggjD5MGOimqP9yJ3GupkgyfOc__wlXH796Ld-FlXB8hYoDq17wc7gvgevwFVTLKQi8tDTEIAADXOqwDKsTpFZq0ttxnJXvKkBZI0A3wx_n7XwpC7O6xkivUELWo7OWsQBj5Xu-MueRBdXmh3-xTyxVwuJ25zj-oH3Bwa1pRE-zvOdevuzUsbMH7E6NTq4-hX207dlaQQCIkhq87g../download to ./mnist_m.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "77c7d8fd65ff4e1887981ebdd4c6f044",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/134178716 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 68007/68007 [00:18<00:00, 3587.80it/s]\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from tqdm import tqdm\n",
        "\n",
        "from pytorch_adapt.datasets import get_mnist_mnistm\n",
        "\n",
        "# mnist is the source domain\n",
        "# mnistm is the target domain\n",
        "datasets = get_mnist_mnistm([\"mnist\"], [\"mnistm\"], \".\", download=True)\n",
        "dataloader = torch.utils.data.DataLoader(\n",
        "    datasets[\"train\"], batch_size=32, num_workers=2\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1fad561",
      "metadata": {
        "id": "f1fad561"
      },
      "source": [
        "### Load toy models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "d0b08ee0",
      "metadata": {
        "id": "d0b08ee0"
      },
      "outputs": [],
      "source": [
        "from pytorch_adapt.models import Discriminator, mnistC, mnistG\n",
        "\n",
        "device = torch.device(\"cuda\")\n",
        "\n",
        "\n",
        "def get_models():\n",
        "    G = mnistG(pretrained=True).to(device)\n",
        "    C = mnistC(pretrained=True).to(device)\n",
        "    D = Discriminator(in_size=1200, h=256).to(device)\n",
        "    return {\"G\": G, \"C\": C, \"D\": D}\n",
        "\n",
        "\n",
        "def get_optimizers(models):\n",
        "    G_opt = torch.optim.Adam(models[\"G\"].parameters(), lr=0.0001)\n",
        "    C_opt = torch.optim.Adam(models[\"C\"].parameters(), lr=0.0001)\n",
        "    D_opt = torch.optim.Adam(models[\"D\"].parameters(), lr=0.0001)\n",
        "    return [G_opt, C_opt, D_opt]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c86afd0d",
      "metadata": {
        "id": "c86afd0d"
      },
      "source": [
        "### Use in vanilla PyTorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "b943c4fd",
      "metadata": {
        "id": "b943c4fd",
        "outputId": "8b13b990-e8cb-497b-c700-ef052c4bd5ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133,
          "referenced_widgets": [
            "f0663f4721bf4033838e0887aa5b851f",
            "ec370d21c4eb42b7b633115a43e6e257",
            "44fc968d30d24fcaadff2e4c1125ff50",
            "3b52533345564fcea24d65c88bb58836",
            "ffe0aaaa41e34122ac81cb7e132f52e0",
            "3dbf9e1a6dac4a19a3b3a74266249d45",
            "1e5559e270694560bf525bb924cef200",
            "bfa0520a50864724ab0f0feff5635754",
            "8dfc08e6ba434f6b8ac0f549fa00d992",
            "5a20ed92dd8a42338651cb1b847a0de2",
            "584e577fa0b84371ac6c8ed49f49b1e7",
            "bc1a998578794b81942f6bfa8ed090cf",
            "6af2133cf57844859017063112199e89",
            "1f2ce6c2a39946119b95546a55104837",
            "e0c079614c764b8eb6279feda8f0d270",
            "9373926edbfa42b3b3a6ffd1a76cb884",
            "481769612ab8480eaeb1873c57473793",
            "e6d43b45edb3484780541feb93f164ac",
            "68db1869819147d996bc3784fb50e794",
            "ba7da9e918c44ef09e0604270d49a3ff",
            "0a51711ff8bd4577ab16eeebabfe9947",
            "4435da26c17f44ca9185b7566525df23"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://cornell.box.com/shared/static/tdx0ts24e273j7mf3r2ox7a12xh4fdfy\" to /root/.cache/torch/hub/checkpoints/mnistG-68ee7945.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0663f4721bf4033838e0887aa5b851f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/161k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://cornell.box.com/shared/static/j4zrogronmievq1csulrkai7zjm27gcq\" to /root/.cache/torch/hub/checkpoints/mnistC-ac7b5a13.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bc1a998578794b81942f6bfa8ed090cf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/1.31M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1844/1844 [01:28<00:00, 20.85it/s]\n"
          ]
        }
      ],
      "source": [
        "from pytorch_adapt.hooks import DANNHook\n",
        "from pytorch_adapt.utils.common_functions import batch_to_device\n",
        "\n",
        "models = get_models()\n",
        "optimizers = get_optimizers(models)\n",
        "\n",
        "# Assuming that models, optimizers, and dataloader are already created.\n",
        "hook = DANNHook(optimizers)\n",
        "for data in tqdm(dataloader):\n",
        "    data = batch_to_device(data, device)\n",
        "    # Optimization is done inside the hook.\n",
        "    # The returned loss is for logging.\n",
        "    _, loss = hook({**models, **data})"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "559c1220",
      "metadata": {
        "id": "559c1220"
      },
      "source": [
        "### Build complex algorithms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c1582602",
      "metadata": {
        "id": "c1582602",
        "outputId": "39591b7f-2040-425a-9b3e-cb0dba5dbe84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1844/1844 [02:09<00:00, 14.24it/s]\n"
          ]
        }
      ],
      "source": [
        "from pytorch_adapt.hooks import MCCHook, VATHook\n",
        "\n",
        "models = get_models()\n",
        "optimizers = get_optimizers(models)\n",
        "\n",
        "# G and C are the Generator and Classifier models\n",
        "G, C = models[\"G\"], models[\"C\"]\n",
        "misc = {\"combined_model\": torch.nn.Sequential(G, C)}\n",
        "hook = DANNHook(optimizers, post_g=[MCCHook(), VATHook()])\n",
        "for data in tqdm(dataloader):\n",
        "    data = batch_to_device(data, device)\n",
        "    _, loss = hook({**models, **data, **misc})"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "764f8823",
      "metadata": {
        "id": "764f8823"
      },
      "source": [
        "### Wrap with your favorite PyTorch framework"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "e10720dc",
      "metadata": {
        "id": "e10720dc"
      },
      "outputs": [],
      "source": [
        "from pytorch_adapt.adapters import DANN\n",
        "from pytorch_adapt.containers import Models\n",
        "from pytorch_adapt.datasets import DataloaderCreator\n",
        "\n",
        "models = get_models()\n",
        "models_cont = Models(models)\n",
        "adapter = DANN(models=models_cont)\n",
        "dc = DataloaderCreator(num_workers=2)\n",
        "dataloaders = dc(**datasets)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1faec48",
      "metadata": {
        "id": "b1faec48"
      },
      "source": [
        "#### Lightning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "abdc1ae5",
      "metadata": {
        "id": "abdc1ae5",
        "outputId": "e7f657a3-83e2-4b9a-926b-6e116d9fb57d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344,
          "referenced_widgets": [
            "198d7f7f5a9943de9f7e807184d8dbed",
            "d3c54396307f443ba990625ef095d7ac",
            "7807d708c90c4096a37a695f00afdd7d",
            "58739d8bc0e2460d83416e3479da6848",
            "ec5ef16010be430b98f7287cb57d5f4a",
            "8c7713ccdbdb4bd9845f9d271585d09f",
            "b856453ff6104016bac64d8e29dff65e",
            "17abc175a9c041dd8a3130e32782ae29",
            "d7ac40bc9b3f434faee5052742262c12",
            "531d00ebdf954765ada72d7b5afce05d",
            "86278277d64a479d9764ca31760eaed9"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/configuration_validator.py:122: UserWarning: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n",
            "  rank_zero_warn(\"You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\")\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "Missing logger folder: /content/lightning_logs\n",
            "\n",
            "  | Name   | Type       | Params\n",
            "--------------------------------------\n",
            "0 | models | ModuleDict | 756 K \n",
            "1 | misc   | ModuleDict | 0     \n",
            "--------------------------------------\n",
            "756 K     Trainable params\n",
            "0         Non-trainable params\n",
            "756 K     Total params\n",
            "3.024     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "198d7f7f5a9943de9f7e807184d8dbed",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pytorch_lightning as pl\n",
        "\n",
        "from pytorch_adapt.frameworks.lightning import Lightning\n",
        "\n",
        "L_adapter = Lightning(adapter)\n",
        "trainer = pl.Trainer(gpus=1, max_epochs=1)\n",
        "trainer.fit(L_adapter, dataloaders[\"train\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed0061eb",
      "metadata": {
        "id": "ed0061eb"
      },
      "source": [
        "#### Ignite"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "55dc6458",
      "metadata": {
        "id": "55dc6458",
        "outputId": "28f5455f-0f4e-4473-9a20-3b47313be84f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "af396769d6684d9bb67b222d04bbb652",
            "eb7c487e06cc43e08e94a9973686109f",
            "7c6628552e48433785cf76ad4f3252ec",
            "ce0ba5be3bd94ea3969f6ede557fe928",
            "0b5fcc45f8294a9cb4be4ec10362a404",
            "83b66a18853a4cde981455154991ddc9",
            "b712428f004a400fac1aa7c6e31e2361",
            "7db998ad4db248eea9e939ac3473e7be",
            "3329e54a4e594ad28041cfd4bfa89a04",
            "c52167a7411046109fd757a36713da05",
            "fcb5214772484a06b2f6b961b0036956"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af396769d6684d9bb67b222d04bbb652",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "[1/1843]   0%|          |it [00:00<?]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "from pytorch_adapt.frameworks.ignite import Ignite\n",
        "\n",
        "models = get_models()\n",
        "models_cont = Models(models)\n",
        "adapter = DANN(models=models_cont)\n",
        "\n",
        "trainer = Ignite(adapter)\n",
        "trainer.run(datasets, dataloader_creator=dc)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f9f10fe",
      "metadata": {
        "id": "1f9f10fe"
      },
      "source": [
        "### Check your model's performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "0734522c",
      "metadata": {
        "id": "0734522c"
      },
      "outputs": [],
      "source": [
        "from pytorch_adapt.validators import SNDValidator\n",
        "\n",
        "# Random predictions as placeholder\n",
        "preds = torch.randn(1000, 100)\n",
        "\n",
        "# Assuming predictions have been collected\n",
        "target_train = {\"preds\": preds}\n",
        "validator = SNDValidator()\n",
        "score = validator(target_train=target_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b0c71242",
      "metadata": {
        "id": "b0c71242"
      },
      "source": [
        "#### Lightning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "e3691423",
      "metadata": {
        "id": "e3691423",
        "outputId": "5676a8aa-241a-4164-f1fc-0075b03426a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292,
          "referenced_widgets": [
            "366239a83f654a848d56a5843af49194",
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            "27606d632ef54a93af14cd4ae422ab5e",
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            "9dbc4dc89a3b40c0b73cb2e43f628d43",
            "f54a84e5bfb74df681910ea6b55993ee",
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            "95c52007720d413581f6721557e649d1",
            "91f53c9053e3465c80a900633ad62186",
            "b44973c1043242e9b05e941447db0057",
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            "c4d9d1173ad04fad907b805e45d9332a",
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name   | Type       | Params\n",
            "--------------------------------------\n",
            "0 | models | ModuleDict | 756 K \n",
            "1 | misc   | ModuleDict | 0     \n",
            "--------------------------------------\n",
            "756 K     Trainable params\n",
            "0         Non-trainable params\n",
            "756 K     Total params\n",
            "3.024     Total estimated model params size (MB)\n"
          ]
        },
        {
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              "model_id": "366239a83f654a848d56a5843af49194",
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              "Validation sanity check: 0it [00:00, ?it/s]"
            ]
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              "model_id": "660eeea2cc5644b99b46ffabf34e9362",
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              "Training: 0it [00:00, ?it/s]"
            ]
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              "model_id": "b46456f6d277464093d3ccd70053fc39",
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            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from pytorch_adapt.frameworks.utils import filter_datasets\n",
        "\n",
        "models = get_models()\n",
        "models_cont = Models(models)\n",
        "adapter = DANN(models=models_cont)\n",
        "validator = SNDValidator()\n",
        "dataloaders = dc(**filter_datasets(datasets, validator))\n",
        "train_loader = dataloaders.pop(\"train\")\n",
        "\n",
        "L_adapter = Lightning(adapter, validator=validator)\n",
        "trainer = pl.Trainer(gpus=1, max_epochs=1)\n",
        "trainer.fit(L_adapter, train_loader, list(dataloaders.values()))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a2ba312",
      "metadata": {
        "id": "3a2ba312"
      },
      "source": [
        "#### Ignite"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "3cac2964",
      "metadata": {
        "id": "3cac2964",
        "outputId": "f85ffc6e-2b79-4e29-88d6-dfadd5b3caae",
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          "base_uri": "https://localhost:8080/",
          "height": 99,
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      "outputs": [
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "02cd3f74ca5e444f96ec5bc9687d28d6",
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              "[1/1844]   0%|          |it [00:00<?]"
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          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
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              "(8.411684036254883, 1)"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "from pytorch_adapt.validators import ScoreHistory\n",
        "\n",
        "models = get_models()\n",
        "models_cont = Models(models)\n",
        "adapter = DANN(models=models_cont)\n",
        "\n",
        "validator = ScoreHistory(SNDValidator())\n",
        "trainer = Ignite(adapter, validator=validator)\n",
        "trainer.run(datasets, dataloader_creator=dc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "d5f22fb2",
      "metadata": {
        "id": "d5f22fb2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
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