acceleration/automatic_mixed_precision.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automatic mixed precision training and evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows how to apply the automatic mixed precision (AMP) feature of PyTorch to training and validation programs.  \n",
    "It's modified from the Spleen 3D segmentation tutorial notebook, and compares the training speed and memory usage with/without AMP.\n",
    "\n",
    "The Spleen dataset can be downloaded from http://medicaldecathlon.com/.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/master/acceleration/automatic_mixed_precision.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.6.0rc1+15.gf3d436a0\n",
      "Numpy version: 1.20.3\n",
      "Pytorch version: 1.9.0a0+c3d40fd\n",
      "MONAI flags: HAS_EXT = True, USE_COMPILED = False\n",
      "MONAI rev id: f3d436a09deefcf905ece2faeec37f55ab030003\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.5\n",
      "Nibabel version: 3.2.1\n",
      "scikit-image version: 0.15.0\n",
      "Pillow version: 8.2.0\n",
      "Tensorboard version: 2.5.0\n",
      "gdown version: 3.13.0\n",
      "TorchVision version: 0.10.0a0\n",
      "ITK version: 5.1.2\n",
      "tqdm version: 4.53.0\n",
      "lmdb version: 1.2.1\n",
      "psutil version: 5.8.0\n",
      "pandas version: 1.1.4\n",
      "einops version: 0.3.0\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Copyright 2020 MONAI Consortium\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "import glob\n",
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from monai.apps import download_and_extract\n",
    "from monai.config import print_config\n",
    "from monai.data import CacheDataset, list_data_collate, decollate_batch\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.losses import DiceLoss\n",
    "from monai.metrics import DiceMetric\n",
    "from monai.networks.layers import Norm\n",
    "from monai.networks.nets import UNet\n",
    "from monai.transforms import (\n",
    "    AddChanneld,\n",
    "    AsDiscrete,\n",
    "    Compose,\n",
    "    CropForegroundd,\n",
    "    FgBgToIndicesd,\n",
    "    LoadImaged,\n",
    "    Orientationd,\n",
    "    RandCropByPosNegLabeld,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    "    EnsureTyped,\n",
    "    EnsureType,\n",
    ")\n",
    "from monai.utils import get_torch_version_tuple, set_determinism\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "print_config()\n",
    "\n",
    "if get_torch_version_tuple() < (1, 6):\n",
    "    raise RuntimeError(\n",
    "        \"AMP feature only exists in PyTorch version greater than v1.6.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root dir is: /workspace/data/medical\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(f\"root dir is: {root_dir}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "Downloads and extracts the Decathlon Spleen dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "resource = \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar\"\n",
    "md5 = \"410d4a301da4e5b2f6f86ec3ddba524e\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
    "data_root = os.path.join(root_dir, \"Task09_Spleen\")\n",
    "if not os.path.exists(data_root):\n",
    "    download_and_extract(resource, compressed_file, root_dir, md5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set MSD Spleen dataset path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = sorted(\n",
    "    glob.glob(os.path.join(data_root, \"imagesTr\", \"*.nii.gz\"))\n",
    ")\n",
    "train_labels = sorted(\n",
    "    glob.glob(os.path.join(data_root, \"labelsTr\", \"*.nii.gz\"))\n",
    ")\n",
    "data_dicts = [\n",
    "    {\"image\": image_name, \"label\": label_name}\n",
    "    for image_name, label_name in zip(train_images, train_labels)\n",
    "]\n",
    "train_files, val_files = data_dicts[:-9], data_dicts[-9:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup transforms for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transformations():\n",
    "    train_transforms = Compose(\n",
    "        [\n",
    "            LoadImaged(keys=[\"image\", \"label\"]),\n",
    "            AddChanneld(keys=[\"image\", \"label\"]),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                pixdim=(1.5, 1.5, 2.0),\n",
    "                mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"],\n",
    "                a_min=-57,\n",
    "                a_max=164,\n",
    "                b_min=0.0,\n",
    "                b_max=1.0,\n",
    "                clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "            # pre-compute foreground and background indexes\n",
    "            # and cache them to accelerate training\n",
    "            FgBgToIndicesd(\n",
    "                keys=\"label\",\n",
    "                fg_postfix=\"_fg\",\n",
    "                bg_postfix=\"_bg\",\n",
    "                image_key=\"image\",\n",
    "            ),\n",
    "            # randomly crop out patch samples from\n",
    "            # big image based on pos / neg ratio\n",
    "            # the image centers of negative samples must be in valid image area\n",
    "            RandCropByPosNegLabeld(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                label_key=\"label\",\n",
    "                spatial_size=(128, 128, 96),\n",
    "                pos=1,\n",
    "                neg=1,\n",
    "                num_samples=4,\n",
    "                fg_indices_key=\"label_fg\",\n",
    "                bg_indices_key=\"label_bg\",\n",
    "            ),\n",
    "            EnsureTyped(keys=[\"image\", \"label\"]),\n",
    "        ]\n",
    "    )\n",
    "    val_transforms = Compose(\n",
    "        [\n",
    "            LoadImaged(keys=[\"image\", \"label\"]),\n",
    "            AddChanneld(keys=[\"image\", \"label\"]),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                pixdim=(1.5, 1.5, 2.0),\n",
    "                mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"],\n",
    "                a_min=-57,\n",
    "                a_max=164,\n",
    "                b_min=0.0,\n",
    "                b_max=1.0,\n",
    "                clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "            EnsureTyped(keys=[\"image\", \"label\"]),\n",
    "        ]\n",
    "    )\n",
    "    return train_transforms, val_transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a typical PyTorch training process\n",
    "Usage of PyTorch AMP module refers to: https://pytorch.org/docs/stable/notes/amp_examples.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def train_process(amp=False):\n",
    "    train_trans, val_trans = transformations()\n",
    "    train_ds = CacheDataset(\n",
    "        data=train_files, transform=train_trans, cache_rate=1.0, num_workers=8\n",
    "    )\n",
    "    val_ds = CacheDataset(\n",
    "        data=val_files, transform=val_trans, cache_rate=1.0, num_workers=5\n",
    "    )\n",
    "\n",
    "    # use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
    "    # to generate 2 x 4 images for network training\n",
    "    train_loader = DataLoader(\n",
    "        train_ds,\n",
    "        batch_size=2,\n",
    "        shuffle=True,\n",
    "        num_workers=1,\n",
    "        collate_fn=list_data_collate,\n",
    "    )\n",
    "    val_loader = DataLoader(val_ds, batch_size=1, num_workers=1)\n",
    "    device = torch.device(\"cuda:0\")\n",
    "    model = UNet(\n",
    "        spatial_dims=3,\n",
    "        in_channels=1,\n",
    "        out_channels=2,\n",
    "        channels=(16, 32, 64, 128, 256),\n",
    "        strides=(2, 2, 2, 2),\n",
    "        num_res_units=2,\n",
    "        norm=Norm.BATCH,\n",
    "    ).to(device)\n",
    "    loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "    scaler = torch.cuda.amp.GradScaler() if amp else None\n",
    "\n",
    "    post_pred = Compose([EnsureType(), AsDiscrete(argmax=True, to_onehot=True, num_classes=2)])\n",
    "    post_label = Compose([EnsureType(), AsDiscrete(to_onehot=True, num_classes=2)])\n",
    "\n",
    "    dice_metric = DiceMetric(include_background=False, reduction=\"mean\", get_not_nans=False)\n",
    "\n",
    "    max_epochs = 300\n",
    "    val_interval = 1  # do validation for every epoch\n",
    "    best_metric = -1\n",
    "    best_metric_epoch = -1\n",
    "    best_metrics_epochs_and_time = [[], [], []]\n",
    "    epoch_loss_values = []\n",
    "    metric_values = []\n",
    "    epoch_times = []\n",
    "    total_start = time.time()\n",
    "    for epoch in range(max_epochs):\n",
    "        epoch_start = time.time()\n",
    "        print(\"-\" * 10)\n",
    "        print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        step = 0\n",
    "        for batch_data in train_loader:\n",
    "            step_start = time.time()\n",
    "            step += 1\n",
    "            inputs, labels = (\n",
    "                batch_data[\"image\"].to(device),\n",
    "                batch_data[\"label\"].to(device),\n",
    "            )\n",
    "            optimizer.zero_grad()\n",
    "            if amp and scaler is not None:\n",
    "                with torch.cuda.amp.autocast():\n",
    "                    outputs = model(inputs)\n",
    "                    loss = loss_function(outputs, labels)\n",
    "                scaler.scale(loss).backward()\n",
    "                scaler.step(optimizer)\n",
    "                scaler.update()\n",
    "            else:\n",
    "                outputs = model(inputs)\n",
    "                loss = loss_function(outputs, labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "            epoch_loss += loss.item()\n",
    "            print(\n",
    "                f\"{step}/{len(train_ds) // train_loader.batch_size},\"\n",
    "                f\" train_loss: {loss.item():.4f}\"\n",
    "                f\" step time: {(time.time() - step_start):.4f}\"\n",
    "            )\n",
    "        epoch_loss /= step\n",
    "        epoch_loss_values.append(epoch_loss)\n",
    "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "        if (epoch + 1) % val_interval == 0:\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                for val_data in val_loader:\n",
    "                    val_inputs, val_labels = (\n",
    "                        val_data[\"image\"].to(device),\n",
    "                        val_data[\"label\"].to(device),\n",
    "                    )\n",
    "                    roi_size = (160, 160, 128)\n",
    "                    sw_batch_size = 4\n",
    "                    if amp:\n",
    "                        with torch.cuda.amp.autocast():\n",
    "                            val_outputs = sliding_window_inference(\n",
    "                                val_inputs, roi_size, sw_batch_size, model\n",
    "                            )\n",
    "                    else:\n",
    "                        val_outputs = sliding_window_inference(\n",
    "                            val_inputs, roi_size, sw_batch_size, model\n",
    "                        )\n",
    "                    val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
    "                    val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
    "                    dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "\n",
    "                metric = dice_metric.aggregate().item()\n",
    "                dice_metric.reset()\n",
    "                metric_values.append(metric)\n",
    "                if metric > best_metric:\n",
    "                    best_metric = metric\n",
    "                    best_metric_epoch = epoch + 1\n",
    "                    best_metrics_epochs_and_time[0].append(best_metric)\n",
    "                    best_metrics_epochs_and_time[1].append(best_metric_epoch)\n",
    "                    best_metrics_epochs_and_time[2].append(\n",
    "                        time.time() - total_start\n",
    "                    )\n",
    "                    torch.save(model.state_dict(), \"best_metric_model.pth\")\n",
    "                    print(\"saved new best metric model\")\n",
    "                print(\n",
    "                    f\"current epoch: {epoch + 1} current\"\n",
    "                    f\" mean dice: {metric:.4f}\"\n",
    "                    f\" best mean dice: {best_metric:.4f} \"\n",
    "                    f\"at epoch: {best_metric_epoch}\"\n",
    "                )\n",
    "        print(\n",
    "            f\"time consuming of epoch {epoch + 1}\"\n",
    "            f\" is: {(time.time() - epoch_start):.4f}\"\n",
    "        )\n",
    "        epoch_times.append(time.time() - epoch_start)\n",
    "    print(\n",
    "        f\"train completed, best_metric: {best_metric:.4f}\"\n",
    "        f\" at epoch: {best_metric_epoch}\"\n",
    "        f\" total time: {(time.time() - total_start):.4f}\"\n",
    "    )\n",
    "    return (\n",
    "        max_epochs,\n",
    "        epoch_loss_values,\n",
    "        metric_values,\n",
    "        epoch_times,\n",
    "        best_metrics_epochs_and_time,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic and train with AMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "amp_start = time.time()\n",
    "(\n",
    "    max_epochs,\n",
    "    amp_epoch_loss_values,\n",
    "    amp_metric_values,\n",
    "    amp_epoch_times,\n",
    "    amp_best,\n",
    ") = train_process(amp=True)\n",
    "amp_total_time = time.time() - amp_start\n",
    "print(\n",
    "    f\"total training time of {max_epochs} \"\n",
    "    f\"epochs with AMP: {amp_total_time:.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the memory usage during training with AMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tesla V100-PCIE-32GB\n",
      "|===========================================================================|\n",
      "|                  PyTorch CUDA memory summary, device ID 0                 |\n",
      "|---------------------------------------------------------------------------|\n",
      "|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\n",
      "|===========================================================================|\n",
      "|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocated memory      |   37839 KB |    1876 MB |   45927 GB |   45927 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active memory         |   37839 KB |    1876 MB |   45927 GB |   45927 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved memory   |    2590 MB |    2590 MB |    2590 MB |       0 B  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable memory |   23600 KB |    1012 MB |   32888 GB |   32888 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocations           |     245    |     609    |    4695 K  |    4694 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active allocs         |     245    |     609    |    4695 K  |    4694 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved segments |      50    |      50    |      50    |       0    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable allocs |      47    |      60    |    3179 K  |    3178 K  |\n",
      "|===========================================================================|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.get_device_name(0))\n",
    "print(torch.cuda.memory_summary(0, abbreviated=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic and train without AMP\n",
    "In order to correctly measure the memory usage, please restart the notebook and skip above AMP training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "start = time.time()\n",
    "(\n",
    "    max_epochs,\n",
    "    epoch_loss_values,\n",
    "    metric_values,\n",
    "    epoch_times,\n",
    "    best,\n",
    ") = train_process(amp=False)\n",
    "total_time = time.time() - start\n",
    "print(\n",
    "    f\"total training time of {max_epochs} epochs without AMP: {total_time:.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the memory usage during training without AMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tesla V100-PCIE-32GB\n",
      "|===========================================================================|\n",
      "|                  PyTorch CUDA memory summary, device ID 0                 |\n",
      "|---------------------------------------------------------------------------|\n",
      "|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\n",
      "|===========================================================================|\n",
      "|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocated memory      |   38319 KB |    2788 MB |   61299 GB |   61299 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active memory         |   38319 KB |    2788 MB |   61299 GB |   61299 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved memory   |    4118 MB |    4118 MB |    4118 MB |       0 B  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable memory |  107088 KB |     771 MB |   30136 GB |   30136 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocations           |     245    |     542    |    3755 K  |    3754 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active allocs         |     245    |     542    |    3755 K  |    3754 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved segments |      57    |      57    |      57    |       0    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable allocs |      31    |      48    |    2422 K  |    2422 K  |\n",
      "|===========================================================================|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.get_device_name(0))\n",
    "print(torch.cuda.memory_summary(0, abbreviated=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot training loss and validation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 12))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title(\"Regular Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"red\")\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.title(\"Regular Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"red\")\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.title(\"AMP Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(amp_epoch_loss_values))]\n",
    "y = amp_epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"green\")\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.title(\"AMP Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(amp_metric_values))]\n",
    "y = amp_metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total time and every epoch time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Total Train Time(300 epochs)\")\n",
    "plt.bar(\"regular\", total_time, 1, label=\"Regular training\", color=\"red\")\n",
    "plt.bar(\"AMP\", amp_total_time, 1, label=\"AMP training\", color=\"green\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Epoch Time\")\n",
    "x = [i + 1 for i in range(len(epoch_times))]\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.plot(x, epoch_times, label=\"Regular training\", color=\"red\")\n",
    "plt.plot(x, amp_epoch_times, label=\"AMP training\", color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total time to achieve metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_best_metric_time(threshold, best_values):\n",
    "    for i, v in enumerate(best_values[0]):\n",
    "        if v > threshold:\n",
    "            return best_values[2][i]\n",
    "    return -1\n",
    "\n",
    "\n",
    "def get_best_metric_epochs(threshold, best_values):\n",
    "    for i, v in enumerate(best_values[0]):\n",
    "        if v > threshold:\n",
    "            return best_values[1][i]\n",
    "    return -1\n",
    "\n",
    "\n",
    "plt.figure(\"train\", (18, 6))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.title(\"Metrics Time\")\n",
    "plt.xlabel(\"secs\")\n",
    "plt.ylabel(\"best mean_dice\")\n",
    "plt.plot(best[2], best[0], label=\"Regular training\", color=\"red\")\n",
    "plt.plot(amp_best[2], amp_best[0], label=\"AMP training\", color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.title(\"Typical Metrics Time\")\n",
    "plt.xlabel(\"best mean_dice\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.bar(\n",
    "    \"0.80\",\n",
    "    get_best_metric_time(0.8, best),\n",
    "    0.5,\n",
    "    label=\"Regular training\",\n",
    "    color=\"red\",\n",
    ")\n",
    "plt.bar(\n",
    "    \"0.80 \",\n",
    "    get_best_metric_time(0.8, amp_best),\n",
    "    0.5,\n",
    "    label=\"AMP training\",\n",
    "    color=\"green\",\n",
    ")\n",
    "plt.bar(\"0.85\", get_best_metric_time(0.85, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.85 \", get_best_metric_time(0.85, amp_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.90\", get_best_metric_time(0.9, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.90 \", get_best_metric_time(0.9, amp_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.93\", get_best_metric_time(0.93, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.93 \", get_best_metric_time(0.93, amp_best), 0.5, color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.title(\"Typical Metrics Epochs\")\n",
    "plt.xlabel(\"best mean_dice\")\n",
    "plt.ylabel(\"epochs\")\n",
    "plt.bar(\n",
    "    \"0.80\",\n",
    "    get_best_metric_epochs(0.8, best),\n",
    "    0.5,\n",
    "    label=\"Regular training\",\n",
    "    color=\"red\",\n",
    ")\n",
    "plt.bar(\n",
    "    \"0.80 \",\n",
    "    get_best_metric_epochs(0.8, amp_best),\n",
    "    0.5,\n",
    "    label=\"AMP training\",\n",
    "    color=\"green\",\n",
    ")\n",
    "plt.bar(\"0.85\", get_best_metric_epochs(0.85, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.85 \", get_best_metric_epochs(0.85, amp_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.90\", get_best_metric_epochs(0.9, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.90 \", get_best_metric_epochs(0.9, amp_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.93\", get_best_metric_epochs(0.93, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.93 \", get_best_metric_epochs(0.93, amp_best), 0.5, color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}