Session2_5.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcaKN0qQ4LYi"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Od7tVRVF9BAv"
      },
      "source": [
        "# TSAI Assignment\n",
        "\n",
        "## Session 2.5 - PyTorch 101\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVIFjIoj9Sjk"
      },
      "source": [
        "1. Write a neural network that\n",
        "   a. can take 2 inputs:\n",
        "\n",
        "   1. an image from the MNIST dataset (say 5), and\n",
        "   2. a random number between 0 and 9, (say 7)\n",
        "\n",
        "   b. and gives 2 outputs:\n",
        "\n",
        "   1. the \"number\" that was represented by the MNIST image (predict 5), and\n",
        "   2. the \"sum\" of this number with the random number and the input image to the network (predict 5 + 7 = 12)\n",
        "\n",
        "2. You can mix fully connected layers and convolution layers\n",
        "\n",
        "3. You can use one-hot encoding to represent the random umber input as well as the \"summed\" output.\n",
        "\n",
        "4. **Your code MUST be** - well documented (via readme file on github and comments in the code) - must mention the data representation - must mention your data generation strategy (basically the class/method you are using for random number generation) - must mention how you have combined the two inputs (basically which layer you are combining) - must mention how you are evaluating your results - must mention \"what\" results you finally got and how did you evaluate your results - must mention what loss function you picked and why!\n",
        "\n",
        "5. Training MUST happen on the PU\n",
        "\n",
        "6. Accuracy is not really important for the UM\n",
        "\n",
        "7. Once done, upload the code with short trining logs in the readme file from colab to GitHub, and share the GitHub lik (public repository"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2fCxdxY83Gi"
      },
      "source": [
        "## Install Requirements and Import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 234,
      "metadata": {
        "id": "wwI3CQlj1ttl"
      },
      "outputs": [],
      "source": [
        "! pip install pytorch-lightning --quiet\n",
        "! pip install torchmetrics --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 244,
      "metadata": {
        "id": "dxg1SA1G2Ldw"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "import torch\n",
        "from torch import optim\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data import random_split\n",
        "from torchvision.datasets import MNIST\n",
        "from torchvision import transforms\n",
        "import pytorch_lightning as pl\n",
        "\n",
        "import torchmetrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaNI72fS8zC7"
      },
      "source": [
        "## Custom Dataset Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 236,
      "metadata": {
        "id": "9oS-j0x_7-_r"
      },
      "outputs": [],
      "source": [
        "class CustomDataset(MNIST):\n",
        "  def __init__(self, *args, **kwargs):\n",
        "    super().__init__(*args, **kwargs)\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    img, target = super().__getitem__(index)\n",
        "\n",
        "    rand_number = np.random.randint(low=0, high=10)\n",
        "    encoded_rand_number = F.one_hot(torch.tensor(rand_number),\n",
        "                                    num_classes=10)\n",
        "\n",
        "    encoded_sum = F.one_hot(torch.tensor(target + rand_number),\n",
        "                            num_classes=29)\n",
        "\n",
        "    if not isinstance(img, (torch.Tensor, np.ndarray)):\n",
        "      img = np.array(img)\n",
        "    \n",
        "    return ((img, encoded_rand_number), (target, encoded_sum))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 237,
      "metadata": {
        "id": "1R00rwpwGj65"
      },
      "outputs": [],
      "source": [
        "class CustomDataModule(pl.LightningDataModule):\n",
        "    def __init__(self, root, batch_size=128, num_workers=2):\n",
        "      super().__init__()\n",
        "      \n",
        "      self.root = root\n",
        "      self.batch_size = batch_size\n",
        "      self.num_workers = num_workers\n",
        "\n",
        "      self.train_transform = transforms.Compose([\n",
        "          transforms.ToTensor(),\n",
        "          transforms.Lambda(lambda x: (x / 255).float())\n",
        "      ])\n",
        "      self.val_transform = transforms.Compose([\n",
        "          transforms.ToTensor(),\n",
        "          transforms.Lambda(lambda x: (x / 255).float())\n",
        "      ])\n",
        "        \n",
        "    def prepare_data(self):\n",
        "      # download, split, etc...\n",
        "      # only called on 1 GPU/TPU in distributed\n",
        "      CustomDataset(self.root, train=True, download=True)\n",
        "      CustomDataset(self.root, train=False, download=True)\n",
        "    \n",
        "    def setup(self, stage):\n",
        "      # make assignments here (val/train/test split)\n",
        "      # called on every process in DDP\n",
        "\n",
        "      self.train_data = CustomDataset(\n",
        "        self.root,\n",
        "        train=True, \n",
        "        download=False,\n",
        "        transform=self.train_transform)\n",
        "      \n",
        "      self.val_data = CustomDataset(\n",
        "        self.root,\n",
        "        train=False,\n",
        "        download=False,\n",
        "        transform=self.val_transform)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "      return DataLoader(\n",
        "        self.train_data,\n",
        "        batch_size=self.batch_size,\n",
        "\n",
        "        num_workers=self.num_workers,\n",
        "        pin_memory=True,\n",
        "        drop_last=True)\n",
        "    \n",
        "    def val_dataloader(self):\n",
        "      return DataLoader(\n",
        "        self.val_data,\n",
        "        batch_size=self.batch_size,\n",
        "\n",
        "        num_workers=self.num_workers,\n",
        "        pin_memory=True,\n",
        "        drop_last=True)\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTNGTcNG8vCU"
      },
      "source": [
        "## Custom Network Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 238,
      "metadata": {
        "id": "cPIkFasI5lgk"
      },
      "outputs": [],
      "source": [
        "class CustomNetwork(pl.LightningModule):\n",
        "  def __init__(self, lr=0.1, dropout_val=0.1, epochs=10): \n",
        "    super().__init__()\n",
        "    self.save_hyperparameters()\n",
        "\n",
        "    self.val_sum_accuracy = torchmetrics.Accuracy()\n",
        "    self.val_mnist_accuracy = torchmetrics.Accuracy()\n",
        "\n",
        "    self.loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "\n",
        "    block1 = lambda in_channels, out_channels, kernel_size: [\n",
        "      nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=False),\n",
        "      nn.ReLU(),\n",
        "      nn.BatchNorm2d(out_channels),\n",
        "      nn.Dropout(self.hparams.dropout_val),\n",
        "    ]\n",
        "\n",
        "    block2 = lambda in_channels, out_channels, kernel_size, pool_size: [\n",
        "      nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size),\n",
        "      nn.MaxPool2d(pool_size),\n",
        "    ]\n",
        "\n",
        "    self.mnist_encoder = nn.Sequential(\n",
        "        # input size 1X28x28\n",
        "        *block1(1, 10, (3,3)),\n",
        "        \n",
        "        # input size 10X26X26\n",
        "        *block1(10, 30, (3,3)),\n",
        "\n",
        "        # input size 30X24X24\n",
        "        *block2(30, 15, (1, 1), (2,2)),\n",
        "\n",
        "        # input size 15X12X12\n",
        "        *block1(15, 15, (3,3)),\n",
        "\n",
        "        # input size 15X10X10\n",
        "        *block1(15, 15, (3,3)),\n",
        "\n",
        "        # input size 15X8X8\n",
        "        *block1(15, 15, (3,3)),\n",
        "\n",
        "        # input size 15X6X6\n",
        "        nn.AvgPool2d((6,6)),\n",
        "\n",
        "        # input size 15X1X1\n",
        "        nn.Flatten()\n",
        "        \n",
        "        # output size 15\n",
        "    )\n",
        "\n",
        "    block3 = lambda in_channels, out_channels: [\n",
        "      nn.Linear(in_channels, out_channels, bias=False),\n",
        "      nn.ReLU(),\n",
        "      nn.BatchNorm1d(out_channels),\n",
        "      nn.Dropout(self.hparams.dropout_val),\n",
        "    ]\n",
        "\n",
        "    self.encoder = nn.Sequential(\n",
        "        *block3(15+10, 40),\n",
        "        *block3(40, 40)\n",
        "    )\n",
        "\n",
        "    self.mnist_decode = nn.Sequential(\n",
        "        nn.Linear(40, 10, bias=False)\n",
        "    )\n",
        "\n",
        "    self.sum_decode = nn.Sequential(\n",
        "        nn.Linear(40, 19, bias=False)\n",
        "    )\n",
        "\n",
        "  \n",
        "  def forward(self, img, encoded_rand_number):\n",
        "    x = self.mnist_encoder(img)\n",
        "    x = torch.cat([x, encoded_rand_number], -1)\n",
        "    x = self.encoder(x)\n",
        "\n",
        "    mnist_out = self.mnist_decode(x)\n",
        "    sum_out = self.sum_decode(x)\n",
        "    return mnist_out, sum_out\n",
        "\n",
        "  def training_step(self, train_batch, batch_idx):\n",
        "    (img, encoded_rand_number), (target, encoded_sum) = train_batch\n",
        "    mnist_pred, sum_pred = self.forward(img, encoded_rand_number)\n",
        "    decoded_sum = torch.argmax(encoded_sum, dim=1)\n",
        "\n",
        "    mnist_loss = self.loss_fn(mnist_pred, target)\n",
        "    sum_loss = self.loss_fn(sum_pred, decoded_sum)\n",
        "    loss = mnist_loss + sum_loss\n",
        "\n",
        "    self.log(\"mnist training loss\", mnist_loss)\n",
        "    self.log(\"sum training loss\", sum_loss)\n",
        "    self.log(\"total training loss\", loss)\n",
        "    \n",
        "    return loss\n",
        "\n",
        "  def validation_step(self, valid_batch, batch_idx): \n",
        "    (img, encoded_rand_number), (target, encoded_sum) = valid_batch\n",
        "\n",
        "    # Get Predicted Output\n",
        "    mnist_pred, sum_pred = self.forward(img, encoded_rand_number)\n",
        "    decoded_sum = torch.argmax(encoded_sum, dim=1)\n",
        "\n",
        "    mnist_loss = self.loss_fn(mnist_pred, target)\n",
        "    sum_loss = self.loss_fn(sum_pred, decoded_sum)\n",
        "    loss = mnist_loss + sum_loss\n",
        "\n",
        "    decoded_mnist_pred = torch.argmax(F.log_softmax(mnist_pred, dim=1), dim=1)\n",
        "    decoded_sum_pred = torch.argmax(F.log_softmax(sum_pred, dim=1), dim=1)\n",
        "    \n",
        "    self.val_mnist_accuracy(decoded_mnist_pred, target)\n",
        "    self.val_sum_accuracy(decoded_sum_pred, decoded_sum)\n",
        "\n",
        "    self.log(\"mnist validation loss\", mnist_loss, prog_bar=True)\n",
        "    self.log(\"sum validation loss\", sum_loss, prog_bar=True)\n",
        "    self.log(\"total validation loss\", loss, prog_bar=True)\n",
        "    self.log(\"mnist validation acc\", self.val_mnist_accuracy, on_step=True, on_epoch=True, prog_bar=True)\n",
        "    self.log(\"sum validation acc\", self.val_sum_accuracy, on_step=True, on_epoch=True, prog_bar=True)\n",
        "\n",
        "    return loss\n",
        "\n",
        "  def configure_optimizers(self): \n",
        "    # return torch.optim.SGD(self.parameters(), lr=self.lr)\n",
        "    optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)\n",
        "\n",
        "    lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(\n",
        "                    optimizer,\n",
        "                    max_lr=self.hparams.lr,\n",
        "                    steps_per_epoch=int(len(self.train_dataloader())),\n",
        "                    epochs=self.hparams.epochs,\n",
        "                    anneal_strategy=\"linear\",\n",
        "                    final_div_factor = 30)\n",
        "    \n",
        "    scheduler = {\n",
        "      # REQUIRED: The scheduler instance\n",
        "      \"scheduler\": lr_scheduler,\n",
        "      # The unit of the scheduler's step size, could also be 'step'.\n",
        "      # 'epoch' updates the scheduler on epoch end whereas 'step'\n",
        "      # updates it after a optimizer update.\n",
        "      \"interval\": \"step\",\n",
        "      # How many epochs/steps should pass between calls to\n",
        "      # `scheduler.step()`. 1 corresponds to updating the learning\n",
        "      # rate after every epoch/step.\n",
        "      \"frequency\": 1,\n",
        "      # Metric to to monitor for schedulers like `ReduceLROnPlateau`\n",
        "      # \"monitor\": \"val_loss\",\n",
        "      # If set to `True`, will enforce that the value specified 'monitor'\n",
        "      # is available when the scheduler is updated, thus stopping\n",
        "      # training if not found. If set to `False`, it will only produce a warning\n",
        "      # \"strict\": True,\n",
        "      # If using the `LearningRateMonitor` callback to monitor the\n",
        "      # learning rate progress, this keyword can be used to specify\n",
        "      # a custom logged name\n",
        "      \"name\": \"one_cycle_lr\",\n",
        "    }\n",
        "    \n",
        "    return [optimizer], [scheduler]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jU3_AvdQTvy"
      },
      "source": [
        "## TRAIN AND TEST"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 239,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZaNFtloK05X",
        "outputId": "84c2d33b-aadc-413a-f144-aeaa31a411ee"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n"
          ]
        }
      ],
      "source": [
        "config = {\n",
        "  \"lr\": 0.01,\n",
        "  \"dropout_val\": 0.1,\n",
        "  \"epochs\": 10\n",
        "} \n",
        "\n",
        "data_module = CustomDataModule(\"dataset\")\n",
        "\n",
        "model = CustomNetwork(**config) \n",
        "\n",
        "trainer = pl.Trainer(\n",
        "  gpus=1,\n",
        "  max_epochs=config[\"epochs\"],\n",
        "  progress_bar_refresh_rate=20,\n",
        ") \n",
        "\n",
        "# trainer.fit(model, data_module)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 240,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664,
          "referenced_widgets": [
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        },
        "id": "NcrEiWB4P7IE",
        "outputId": "648ea2a3-89af-43f0-80f1-1c77fae8d0f9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name               | Type             | Params\n",
            "--------------------------------------------------------\n",
            "0 | val_sum_accuracy   | Accuracy         | 0     \n",
            "1 | val_mnist_accuracy | Accuracy         | 0     \n",
            "2 | loss_fn            | CrossEntropyLoss | 0     \n",
            "3 | mnist_encoder      | Sequential       | 9.5 K \n",
            "4 | encoder            | Sequential       | 2.8 K \n",
            "5 | mnist_decode       | Sequential       | 400   \n",
            "6 | sum_decode         | Sequential       | 760   \n",
            "--------------------------------------------------------\n",
            "13.4 K    Trainable params\n",
            "0         Non-trainable params\n",
            "13.4 K    Total params\n",
            "0.054     Total estimated model params size (MB)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e03047a3044d45f48357a20c5ed96f14",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Finding best initial lr:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Restoring states from the checkpoint file at /content/lr_find_temp_model.ckpt\n",
            "Restored all states from the checkpoint file at /content/lr_find_temp_model.ckpt\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "lr_finder.results =  {'lr': [1e-08, 1.492723603461587e-08, 1.8237659375231342e-08, 2.2282237563313445e-08, 2.722378462130032e-08, 3.3261221948696366e-08, 4.0637585879769546e-08, 4.964981108279366e-08, 6.066068363042928e-08, 7.411344491069478e-08, 9.054963425725768e-08, 1.1063088855204407e-07, 1.3516557634062242e-07, 1.6514133861356448e-07, 2.0176484617913606e-07, 2.4651037405571e-07, 3.011791482403928e-07, 3.67971853851103e-07, 4.4957722344889053e-07, 5.49280271653059e-07, 6.710945330208825e-07, 8.199236264123132e-07, 1.001758649594303e-06, 1.2239193501814797e-06, 1.4953487812212206e-06, 1.8269733027492629e-06, 2.2321424211364327e-06, 2.727166171907997e-06, 3.3319716783182443e-06, 4.070905315369043e-06, 4.973712770291162e-06, 6.076736451708607e-06, 7.424378449051936e-06, 9.070887933480851e-06, 1.1082544951931345e-05, 1.3540328522661751e-05, 1.654317643617198e-05, 2.0211967984401358e-05, 2.469438994250346e-05, 3.0170881682725832e-05, 3.6861898740259296e-05, 4.5036787225051646e-05, 5.5024626317996e-05, 6.722747531491187e-05, 8.213655847652628e-05, 0.00010035203920370016, 0.00012260717954501354, 0.00014979785757486557, 0.0001830186308606944, 0.00022360679774997898, 0.00027319623015898184, 0.00033378314489585496, 0.00040780646113503644, 0.0004982459788236816, 0.0006087423301804094, 0.0007437435329199318, 0.0009086840446865019, 0.0011102035265114897, 0.0013564141215924852, 0.0016572270086699927, 0.002024751375369718, 0.0024737818721357386, 0.0030223941691556898, 0.003692672590352411, 0.004511599115263372, 0.005512139535474684, 0.006734570488710049, 0.00822810079017685, 0.010052852327673304, 0.012282280261157894, 0.015006129951631699, 0.018334049650160736, 0.022400004375412525, 0.027367668659831583, 0.0334370152488211, 0.04085236498024655, 0.04991222189122028, 0.060981289463241585, 0.07450515171823664, 0.0910282101513041, 0.11121559854929848, 0.135879957873713, 0.16601414902764572, 0.20283122035545753, 0.24781323876215627, 0.3027709501435098, 0.3699166707505326, 0.4519533437217084, 0.5521833457432486, 0.6746414238367819, 0.8242571136293374, 1.0070531772340972, 1.2303880488356316, 1.5032520475983184, 1.836629281913991, 2.2439398134019717, 2.74157987992172, 3.349581924212312, 4.092420997534536, 5.0], 'loss': [5.470012664794922, 5.5217538313432115, 5.5371070195502785, 5.579743953473677, 5.583406697557153, 5.577814644196146, 5.5697234659802, 5.561662140753519, 5.563734618846924, 5.577428202095582, 5.584092160389941, 5.59504923476997, 5.580091046746207, 5.570130378910329, 5.5624257884767365, 5.56757364103392, 5.566417689517024, 5.567694053508854, 5.567275545100662, 5.573718279051128, 5.570943669410819, 5.564598275600283, 5.560965373895442, 5.564420276814831, 5.558186853214376, 5.556217146213152, 5.561334109704852, 5.557820080954203, 5.555505462627886, 5.557653004512969, 5.561983910612552, 5.561393605269573, 5.565089816456104, 5.5642064115614005, 5.565344882446077, 5.568840310308244, 5.565211182127336, 5.566204717777058, 5.560869996177825, 5.558693737773321, 5.55543559816989, 5.555079391341466, 5.551335504281082, 5.551776433117452, 5.548621118724703, 5.546718467114833, 5.547579689490344, 5.547634102936998, 5.548081484947009, 5.543858577326847, 5.5423931576659, 5.541140559706753, 5.537810770674988, 5.535492390379066, 5.532575509130708, 5.530251733305099, 5.525003397526834, 5.523049754486958, 5.516923720986967, 5.512768568512705, 5.501472399995563, 5.49483779376566, 5.484690948031275, 5.468980546361912, 5.457220924255723, 5.442956893445581, 5.428338988462702, 5.411509001720132, 5.3934886133699065, 5.37697096483955, 5.356518314119844, 5.331993082306531, 5.304991018008137, 5.280388215568835, 5.250376615026409, 5.222639490030162, 5.190801450903184, 5.157947077098435, 5.130289547521267, 5.103590929950763, 5.07564825786625, 5.0371600309537055, 4.999831178036191, 4.965426726622679, 4.930140032400667, 4.9082872811980085, 4.9007224538926835, 4.887875966612465, 4.917062236819733, 4.928575498873713, 4.984143198916961, 5.057174637054504, 5.160960154801689, 5.2747620747844906, 5.600293276041585, 6.220743669104087, 6.936652468169107, 7.694601285723533, 9.91155424259861, 12.572559658246345]}\n"
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        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Run learning rate finder\n",
        "lr_finder = trainer.tuner.lr_find(model, data_module, max_lr=5)\n",
        "\n",
        "# Results can be found in\n",
        "print(\"lr_finder.results = \", lr_finder.results)\n",
        "\n",
        "# Plot with\n",
        "fig = lr_finder.plot(suggest=True)\n",
        "fig.show()\n",
        "\n",
        "# Pick point based on plot, or get suggestion\n",
        "new_lr = lr_finder.suggestion()\n",
        "\n",
        "# update hparams of the model\n",
        "model.hparams.lr = new_lr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 241,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "MBopKXQe6FHj",
        "outputId": "4b670c7c-3641-4ca6-eacf-0ede939e8ae8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/datamodule.py:424: LightningDeprecationWarning: DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.\n",
            "  f\"DataModule.{name} has already been called, so it will not be called again. \"\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name               | Type             | Params\n",
            "--------------------------------------------------------\n",
            "0 | val_sum_accuracy   | Accuracy         | 0     \n",
            "1 | val_mnist_accuracy | Accuracy         | 0     \n",
            "2 | loss_fn            | CrossEntropyLoss | 0     \n",
            "3 | mnist_encoder      | Sequential       | 9.5 K \n",
            "4 | encoder            | Sequential       | 2.8 K \n",
            "5 | mnist_decode       | Sequential       | 400   \n",
            "6 | sum_decode         | Sequential       | 760   \n",
            "--------------------------------------------------------\n",
            "13.4 K    Trainable params\n",
            "0         Non-trainable params\n",
            "13.4 K    Total params\n",
            "0.054     Total estimated model params size (MB)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7a80236fbbb84b728a79e6d169b363e6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validation sanity check: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fc561f30281f4f2faeea6843b1896497",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training: 99it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e60f67af82441729af7cb0e25ed1fd5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52ba694e78414ba2a7964c7c04a9241e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1aeee4255014ae79883c94dae7caecd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e952992117a94fd6b7b01e2d71935523",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f90422fbe8f3468aa44c8771581a7846",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f18b6d3eef0>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f18b6d3eef0>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f18b6d3eef0>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f18b6d3eef0>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3e42f81ecf2e44f6bf31e7f6a643926f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "23b39eb8ecbb44c0a51896229a8315e6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3ca7fcaeaa3f4f358ec9853d6a111c3c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8cb0994748494d9d9f2b8bdb4dd504b4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "70a3a4f26630448bad3c1807e4b7e4f8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/datamodule.py:424: LightningDeprecationWarning: DataModule.teardown has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.teardown.\n",
            "  f\"DataModule.{name} has already been called, so it will not be called again. \"\n"
          ]
        }
      ],
      "source": [
        "# Fit model\n",
        "trainer.fit(model,data_module )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 246,
      "metadata": {
        "id": "0Ey7tT-B6QfS"
      },
      "outputs": [],
      "source": [
        "def infer_and_plot_output(sample_data, model):\n",
        "    model = model.eval()\n",
        "    \n",
        "    (mnist_img, adder_num), (mnist_target, adder_target) = sample_data\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        mnist_pred, adder_pred = model(mnist_img, adder_num)\n",
        "        mnist_pred, adder_pred = torch.argmax(mnist_pred, dim=1), torch.argmax(adder_pred, dim=1)\n",
        "\n",
        "    num_images = 25\n",
        "    fig = plt.figure(figsize=(12, 15))\n",
        "\n",
        "    assert len(mnist_img[0] >= 25), \"need atleast 25 images in `sample_data`\"\n",
        "\n",
        "    fig.suptitle('MNIST and Adder')\n",
        "\n",
        "    for idx in range(0, 25):\n",
        "\n",
        "        image = mnist_img[idx].cpu().numpy()\n",
        "        add_num = torch.argmax(adder_num, dim=1)[idx].cpu().item()\n",
        "        \n",
        "        image_target = mnist_target[idx].cpu().item()\n",
        "        add_target = torch.argmax(adder_target, dim=1)[idx].cpu().item()\n",
        "        \n",
        "        image_pred = mnist_pred[idx].cpu().item()\n",
        "        add_pred = adder_pred[idx].cpu().item()\n",
        "\n",
        "        isvalid = (image_target == image_pred) and (add_target == add_pred)\n",
        "        isvalid = str(isvalid).upper()\n",
        "\n",
        "        ax = fig.add_subplot(5, 5, idx+1)\n",
        "        ax.axis('off')\n",
        "        ax.imshow(mnist_img[idx].squeeze())\n",
        "        ax.set_title(f'target: {image_target} + {add_num} = {add_target}'+\n",
        "                     f'\\npred: {image_pred} + {add_num} = {add_pred}'+ \n",
        "                     f'\\nValid: {isvalid}'\n",
        "                     )\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 247,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 942
        },
        "id": "gXIdxXAr6R8y",
        "outputId": "e0edcecf-5056-4e9d-d811-89561b86d279"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x1080 with 25 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "it = iter(data_module.val_dataloader())\n",
        "next(it)\n",
        "next(it)\n",
        "next(it)\n",
        "sample = next(it)\n",
        "infer_and_plot_output(sample, model)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "Od7tVRVF9BAv"
      ],
      "name": "MySession2.5V2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
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