test_totalsegmentator_convertion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "27f77997-5c64-4b3b-836c-5b1f729e27ed",
   "metadata": {},
   "source": [
    "# Test TotalSegmentator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5a0549d-06b4-4adb-affb-be02fbaa4582",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import importlib\n",
    "import numpy as np\n",
    "import totalsegmentator.libs\n",
    "import tensorflow as tf\n",
    "#import tensorflow_addons as tfa\n",
    "\n",
    "from pathlib import Path\n",
    "from nnunet.paths import default_plans_identifier, network_training_output_dir, default_trainer\n",
    "from nnunet.training.model_restore import load_model_and_checkpoint_files\n",
    "\n",
    "from utils import is_equal\n",
    "from utils.med_utils import load_medical_data\n",
    "from custom_architectures import totalsegmentator_arch\n",
    "from models.weights_converter import *\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = ''\n",
    "tf.config.set_visible_devices([], 'GPU')\n",
    "\n",
    "# low-res model\n",
    "pretrained_task, pretrained_name = 256, 'Task256_TotalSegmentator_3mm_1139subj'\n",
    "# full-res cardiac part model\n",
    "#pretrained_task, pretrained_name = 253, 'Task253_TotalSegmentator_part3_cardiac_1139subj'\n",
    "# full res model part 1\n",
    "#pretrained_task, pretrained_name = 251, 'Task251_TotalSegmentator_part1_organs_1139subj'\n",
    "\n",
    "\n",
    "model_path = Path.home() /'.totalsegmentator/nnunet/results/nnUNet' / '3d_fullres' / pretrained_name\n",
    "model_path = model_path / os.listdir(model_path)[0]\n",
    "\n",
    "totalsegmentator.libs.download_pretrained_weights(253)\n",
    "\n",
    "ckpt = 'model_final_checkpoint'\n",
    "\n",
    "trainer, params = load_model_and_checkpoint_files(str(model_path), checkpoint_name = ckpt)\n",
    "trainer.load_checkpoint_ram(params[0], False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd3723e0-4e54-4d89-83c5-d2c388f4cf08",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Model information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3016d08f-4766-43b9-a11c-e245fdffa7f7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generic_UNet(\n",
      "  (conv_blocks_localization): ModuleList(\n",
      "    (0): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (1): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (2): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (3): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (4): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(64, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (conv_blocks_context): ModuleList(\n",
      "    (0): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(1, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (1): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (2): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (3): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (4): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (5): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (td): ModuleList()\n",
      "  (tu): ModuleList(\n",
      "    (0): ConvTranspose3d(320, 320, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "    (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "    (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "    (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "    (4): ConvTranspose3d(64, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "  )\n",
      "  (seg_outputs): ModuleList(\n",
      "    (0): Conv3d(320, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "    (1): Conv3d(256, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "    (2): Conv3d(128, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "    (3): Conv3d(64, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "    (4): Conv3d(32, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(trainer.network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "650188b9-3c6c-4a76-baed-bf84920cf360",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# variables : 98\n",
      "Name : conv_blocks_localization.0.0.blocks.0.conv.weight\t- Shape : (320, 640, 3, 3, 3)\n",
      "Name : conv_blocks_localization.0.0.blocks.0.conv.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_localization.0.0.blocks.0.instnorm.weight\t- Shape : (320,)\n",
      "Name : conv_blocks_localization.0.0.blocks.0.instnorm.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_localization.0.1.blocks.0.conv.weight\t- Shape : (320, 320, 3, 3, 3)\n",
      "Name : conv_blocks_localization.0.1.blocks.0.conv.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_localization.0.1.blocks.0.instnorm.weight\t- Shape : (320,)\n",
      "Name : conv_blocks_localization.0.1.blocks.0.instnorm.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_localization.1.0.blocks.0.conv.weight\t- Shape : (256, 512, 3, 3, 3)\n",
      "Name : conv_blocks_localization.1.0.blocks.0.conv.bias\t- Shape : (256,)\n",
      "Name : conv_blocks_localization.1.0.blocks.0.instnorm.weight\t- Shape : (256,)\n",
      "Name : conv_blocks_localization.1.0.blocks.0.instnorm.bias\t- Shape : (256,)\n",
      "Name : conv_blocks_localization.1.1.blocks.0.conv.weight\t- Shape : (256, 256, 3, 3, 3)\n",
      "Name : conv_blocks_localization.1.1.blocks.0.conv.bias\t- Shape : (256,)\n",
      "Name : conv_blocks_localization.1.1.blocks.0.instnorm.weight\t- Shape : (256,)\n",
      "Name : conv_blocks_localization.1.1.blocks.0.instnorm.bias\t- Shape : (256,)\n",
      "Name : conv_blocks_localization.2.0.blocks.0.conv.weight\t- Shape : (128, 256, 3, 3, 3)\n",
      "Name : conv_blocks_localization.2.0.blocks.0.conv.bias\t- Shape : (128,)\n",
      "Name : conv_blocks_localization.2.0.blocks.0.instnorm.weight\t- Shape : (128,)\n",
      "Name : conv_blocks_localization.2.0.blocks.0.instnorm.bias\t- Shape : (128,)\n",
      "Name : conv_blocks_localization.2.1.blocks.0.conv.weight\t- Shape : (128, 128, 3, 3, 3)\n",
      "Name : conv_blocks_localization.2.1.blocks.0.conv.bias\t- Shape : (128,)\n",
      "Name : conv_blocks_localization.2.1.blocks.0.instnorm.weight\t- Shape : (128,)\n",
      "Name : conv_blocks_localization.2.1.blocks.0.instnorm.bias\t- Shape : (128,)\n",
      "Name : conv_blocks_localization.3.0.blocks.0.conv.weight\t- Shape : (64, 128, 3, 3, 3)\n",
      "Name : conv_blocks_localization.3.0.blocks.0.conv.bias\t- Shape : (64,)\n",
      "Name : conv_blocks_localization.3.0.blocks.0.instnorm.weight\t- Shape : (64,)\n",
      "Name : conv_blocks_localization.3.0.blocks.0.instnorm.bias\t- Shape : (64,)\n",
      "Name : conv_blocks_localization.3.1.blocks.0.conv.weight\t- Shape : (64, 64, 3, 3, 3)\n",
      "Name : conv_blocks_localization.3.1.blocks.0.conv.bias\t- Shape : (64,)\n",
      "Name : conv_blocks_localization.3.1.blocks.0.instnorm.weight\t- Shape : (64,)\n",
      "Name : conv_blocks_localization.3.1.blocks.0.instnorm.bias\t- Shape : (64,)\n",
      "Name : conv_blocks_localization.4.0.blocks.0.conv.weight\t- Shape : (32, 64, 3, 3, 3)\n",
      "Name : conv_blocks_localization.4.0.blocks.0.conv.bias\t- Shape : (32,)\n",
      "Name : conv_blocks_localization.4.0.blocks.0.instnorm.weight\t- Shape : (32,)\n",
      "Name : conv_blocks_localization.4.0.blocks.0.instnorm.bias\t- Shape : (32,)\n",
      "Name : conv_blocks_localization.4.1.blocks.0.conv.weight\t- Shape : (32, 32, 3, 3, 3)\n",
      "Name : conv_blocks_localization.4.1.blocks.0.conv.bias\t- Shape : (32,)\n",
      "Name : conv_blocks_localization.4.1.blocks.0.instnorm.weight\t- Shape : (32,)\n",
      "Name : conv_blocks_localization.4.1.blocks.0.instnorm.bias\t- Shape : (32,)\n",
      "Name : conv_blocks_context.0.blocks.0.conv.weight\t- Shape : (32, 1, 3, 3, 3)\n",
      "Name : conv_blocks_context.0.blocks.0.conv.bias\t- Shape : (32,)\n",
      "Name : conv_blocks_context.0.blocks.0.instnorm.weight\t- Shape : (32,)\n",
      "Name : conv_blocks_context.0.blocks.0.instnorm.bias\t- Shape : (32,)\n",
      "Name : conv_blocks_context.0.blocks.1.conv.weight\t- Shape : (32, 32, 3, 3, 3)\n",
      "Name : conv_blocks_context.0.blocks.1.conv.bias\t- Shape : (32,)\n",
      "Name : conv_blocks_context.0.blocks.1.instnorm.weight\t- Shape : (32,)\n",
      "Name : conv_blocks_context.0.blocks.1.instnorm.bias\t- Shape : (32,)\n",
      "Name : conv_blocks_context.1.blocks.0.conv.weight\t- Shape : (64, 32, 3, 3, 3)\n",
      "Name : conv_blocks_context.1.blocks.0.conv.bias\t- Shape : (64,)\n",
      "Name : conv_blocks_context.1.blocks.0.instnorm.weight\t- Shape : (64,)\n",
      "Name : conv_blocks_context.1.blocks.0.instnorm.bias\t- Shape : (64,)\n",
      "Name : conv_blocks_context.1.blocks.1.conv.weight\t- Shape : (64, 64, 3, 3, 3)\n",
      "Name : conv_blocks_context.1.blocks.1.conv.bias\t- Shape : (64,)\n",
      "Name : conv_blocks_context.1.blocks.1.instnorm.weight\t- Shape : (64,)\n",
      "Name : conv_blocks_context.1.blocks.1.instnorm.bias\t- Shape : (64,)\n",
      "Name : conv_blocks_context.2.blocks.0.conv.weight\t- Shape : (128, 64, 3, 3, 3)\n",
      "Name : conv_blocks_context.2.blocks.0.conv.bias\t- Shape : (128,)\n",
      "Name : conv_blocks_context.2.blocks.0.instnorm.weight\t- Shape : (128,)\n",
      "Name : conv_blocks_context.2.blocks.0.instnorm.bias\t- Shape : (128,)\n",
      "Name : conv_blocks_context.2.blocks.1.conv.weight\t- Shape : (128, 128, 3, 3, 3)\n",
      "Name : conv_blocks_context.2.blocks.1.conv.bias\t- Shape : (128,)\n",
      "Name : conv_blocks_context.2.blocks.1.instnorm.weight\t- Shape : (128,)\n",
      "Name : conv_blocks_context.2.blocks.1.instnorm.bias\t- Shape : (128,)\n",
      "Name : conv_blocks_context.3.blocks.0.conv.weight\t- Shape : (256, 128, 3, 3, 3)\n",
      "Name : conv_blocks_context.3.blocks.0.conv.bias\t- Shape : (256,)\n",
      "Name : conv_blocks_context.3.blocks.0.instnorm.weight\t- Shape : (256,)\n",
      "Name : conv_blocks_context.3.blocks.0.instnorm.bias\t- Shape : (256,)\n",
      "Name : conv_blocks_context.3.blocks.1.conv.weight\t- Shape : (256, 256, 3, 3, 3)\n",
      "Name : conv_blocks_context.3.blocks.1.conv.bias\t- Shape : (256,)\n",
      "Name : conv_blocks_context.3.blocks.1.instnorm.weight\t- Shape : (256,)\n",
      "Name : conv_blocks_context.3.blocks.1.instnorm.bias\t- Shape : (256,)\n",
      "Name : conv_blocks_context.4.blocks.0.conv.weight\t- Shape : (320, 256, 3, 3, 3)\n",
      "Name : conv_blocks_context.4.blocks.0.conv.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_context.4.blocks.0.instnorm.weight\t- Shape : (320,)\n",
      "Name : conv_blocks_context.4.blocks.0.instnorm.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_context.4.blocks.1.conv.weight\t- Shape : (320, 320, 3, 3, 3)\n",
      "Name : conv_blocks_context.4.blocks.1.conv.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_context.4.blocks.1.instnorm.weight\t- Shape : (320,)\n",
      "Name : conv_blocks_context.4.blocks.1.instnorm.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_context.5.0.blocks.0.conv.weight\t- Shape : (320, 320, 3, 3, 3)\n",
      "Name : conv_blocks_context.5.0.blocks.0.conv.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_context.5.0.blocks.0.instnorm.weight\t- Shape : (320,)\n",
      "Name : conv_blocks_context.5.0.blocks.0.instnorm.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_context.5.1.blocks.0.conv.weight\t- Shape : (320, 320, 3, 3, 3)\n",
      "Name : conv_blocks_context.5.1.blocks.0.conv.bias\t- Shape : (320,)\n",
      "Name : conv_blocks_context.5.1.blocks.0.instnorm.weight\t- Shape : (320,)\n",
      "Name : conv_blocks_context.5.1.blocks.0.instnorm.bias\t- Shape : (320,)\n",
      "Name : tu.0.weight\t- Shape : (320, 320, 1, 1, 1)\n",
      "Name : tu.1.weight\t- Shape : (320, 256, 2, 2, 2)\n",
      "Name : tu.2.weight\t- Shape : (256, 128, 2, 2, 2)\n",
      "Name : tu.3.weight\t- Shape : (128, 64, 2, 2, 2)\n",
      "Name : tu.4.weight\t- Shape : (64, 32, 2, 2, 2)\n",
      "Name : seg_outputs.0.weight\t- Shape : (105, 320, 1, 1, 1)\n",
      "Name : seg_outputs.1.weight\t- Shape : (105, 256, 1, 1, 1)\n",
      "Name : seg_outputs.2.weight\t- Shape : (105, 128, 1, 1, 1)\n",
      "Name : seg_outputs.3.weight\t- Shape : (105, 64, 1, 1, 1)\n",
      "Name : seg_outputs.4.weight\t- Shape : (105, 32, 1, 1, 1)\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_vars(trainer.network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f91cb0ee-245e-426f-b86c-f5e62092e2a6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<nnunet.training.network_training.custom_trainers.nnUNetTrainerV2_ep8000_nomirror.nnUNetTrainerV2_ep8000_nomirror object at 0x7f3dd0154a30>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'fp16': True,\n",
       " 'amp_grad_scaler': <torch.cuda.amp.grad_scaler.GradScaler at 0x7f3ec81532e0>,\n",
       " 'network': Generic_UNet(\n",
       "   (conv_blocks_localization): ModuleList(\n",
       "     (0): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (1): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (2): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (3): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (4): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(64, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "   )\n",
       "   (conv_blocks_context): ModuleList(\n",
       "     (0): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(1, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (1): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (2): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (3): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (4): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (5): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "   )\n",
       "   (td): ModuleList()\n",
       "   (tu): ModuleList(\n",
       "     (0): ConvTranspose3d(320, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "     (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "     (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "     (4): ConvTranspose3d(64, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "   )\n",
       "   (seg_outputs): ModuleList(\n",
       "     (0): Conv3d(320, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (1): Conv3d(256, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (2): Conv3d(128, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (3): Conv3d(64, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (4): Conv3d(32, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "   )\n",
       " ),\n",
       " 'optimizer': SGD (\n",
       " Parameter Group 0\n",
       "     dampening: 0\n",
       "     differentiable: False\n",
       "     foreach: None\n",
       "     lr: 0.01\n",
       "     maximize: False\n",
       "     momentum: 0.99\n",
       "     nesterov: True\n",
       "     weight_decay: 3e-05\n",
       " ),\n",
       " 'lr_scheduler': None,\n",
       " 'tr_gen': None,\n",
       " 'val_gen': None,\n",
       " 'was_initialized': True,\n",
       " 'output_folder': '/etinfo/users2/qlanglois/.totalsegmentator/nnunet/results/nnUNet/3d_fullres/Task256_TotalSegmentator_3mm_1139subj/nnUNetTrainerV2_ep8000_nomirror__nnUNetPlansv2.1/fold_0',\n",
       " 'fold': 0,\n",
       " 'loss': MultipleOutputLoss2(\n",
       "   (loss): DC_and_CE_loss(\n",
       "     (ce): RobustCrossEntropyLoss()\n",
       "     (dc): SoftDiceLoss()\n",
       "   )\n",
       " ),\n",
       " 'dataset_directory': '/mnt/nvme/nnunet/preprocessed/Task256_TotalSegmentator_3mm_1139subj',\n",
       " 'dataset': None,\n",
       " 'dataset_tr': None,\n",
       " 'dataset_val': None,\n",
       " 'patience': 50,\n",
       " 'val_eval_criterion_alpha': 0.9,\n",
       " 'train_loss_MA_alpha': 0.93,\n",
       " 'train_loss_MA_eps': 0.0005,\n",
       " 'max_num_epochs': 8000,\n",
       " 'num_batches_per_epoch': 250,\n",
       " 'num_val_batches_per_epoch': 50,\n",
       " 'also_val_in_tr_mode': False,\n",
       " 'lr_threshold': 1e-06,\n",
       " 'val_eval_criterion_MA': None,\n",
       " 'train_loss_MA': None,\n",
       " 'best_val_eval_criterion_MA': 0.9161797181657702,\n",
       " 'best_MA_tr_loss_for_patience': -0.5271133326027676,\n",
       " 'best_epoch_based_on_MA_tr_loss': 7978,\n",
       " 'all_tr_losses': [1.3904114,\n",
       "  0.8039812,\n",
       "  0.69540334,\n",
       "  0.6362724,\n",
       "  0.60003287,\n",
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       "  ...],\n",
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       "    array([3., 3., 3.]),\n",
       "    array([3.        , 2.99999976, 3.        ]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.00000024, 3.        ]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.        , 2.99999976]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.00000024, 3.        , 3.00000024]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.        , 3.00000024]),\n",
       "    array([3., 3., 3.]),\n",
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       "    array([3., 3., 3.]),\n",
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       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
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       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
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       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.00000024, 3.        ]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.        , 2.99999976]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([2.99999976, 3.        , 2.99999976]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
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       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.00000024, 3.        , 3.        ]),\n",
       "    array([3.        , 3.00000024, 3.        ]),\n",
       "    array([3.00000024, 3.        , 3.        ]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
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       "    array([3., 3., 3.]),\n",
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       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.        , 3.00000024]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.        , 2.99999976]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.00000024, 3.        , 3.        ]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.00000024, 2.99999976]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.        , 3.00000024]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
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       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    ...],\n",
       "   'all_classes': [1,\n",
       "    2,\n",
       "    3,\n",
       "    4,\n",
       "    5,\n",
       "    6,\n",
       "    7,\n",
       "    8,\n",
       "    9,\n",
       "    10,\n",
       "    11,\n",
       "    12,\n",
       "    13,\n",
       "    14,\n",
       "    15,\n",
       "    16,\n",
       "    17,\n",
       "    18,\n",
       "    19,\n",
       "    20,\n",
       "    21,\n",
       "    22,\n",
       "    23,\n",
       "    24,\n",
       "    25,\n",
       "    26,\n",
       "    27,\n",
       "    28,\n",
       "    29,\n",
       "    30,\n",
       "    31,\n",
       "    32,\n",
       "    33,\n",
       "    34,\n",
       "    35,\n",
       "    36,\n",
       "    37,\n",
       "    38,\n",
       "    39,\n",
       "    40,\n",
       "    41,\n",
       "    42,\n",
       "    43,\n",
       "    44,\n",
       "    45,\n",
       "    46,\n",
       "    47,\n",
       "    48,\n",
       "    49,\n",
       "    50,\n",
       "    51,\n",
       "    52,\n",
       "    53,\n",
       "    54,\n",
       "    55,\n",
       "    56,\n",
       "    57,\n",
       "    58,\n",
       "    59,\n",
       "    60,\n",
       "    61,\n",
       "    62,\n",
       "    63,\n",
       "    64,\n",
       "    65,\n",
       "    66,\n",
       "    67,\n",
       "    68,\n",
       "    69,\n",
       "    70,\n",
       "    71,\n",
       "    72,\n",
       "    73,\n",
       "    74,\n",
       "    75,\n",
       "    76,\n",
       "    77,\n",
       "    78,\n",
       "    79,\n",
       "    80,\n",
       "    81,\n",
       "    82,\n",
       "    83,\n",
       "    84,\n",
       "    85,\n",
       "    86,\n",
       "    87,\n",
       "    88,\n",
       "    89,\n",
       "    90,\n",
       "    91,\n",
       "    92,\n",
       "    93,\n",
       "    94,\n",
       "    95,\n",
       "    96,\n",
       "    97,\n",
       "    98,\n",
       "    99,\n",
       "    100,\n",
       "    101,\n",
       "    102,\n",
       "    103,\n",
       "    104],\n",
       "   'modalities': {0: 'CT'},\n",
       "   'intensityproperties': OrderedDict([(0,\n",
       "                 OrderedDict([('local_props', None),\n",
       "                              ('median', 33.0),\n",
       "                              ('mean', -164.82027),\n",
       "                              ('sd', 470.82532),\n",
       "                              ('mn', -8221.0),\n",
       "                              ('mx', 17985.0),\n",
       "                              ('percentile_99_5', 1099.0),\n",
       "                              ('percentile_00_5', -1008.0)]))]),\n",
       "   'size_reductions': None},\n",
       "  'list_of_npz_files': None,\n",
       "  'original_spacings': [array([3., 3., 3.]),\n",
       "   array([3.        , 2.99999976, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 2.99999976, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.00000024, 3.        , 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.00000024, 3.00000024, 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 2.99999976, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([2.99999976, 3.        , 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3.        , 2.99999976, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 2.99999976, 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 2.99999976, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([2.99999976, 3.00000024, 3.        ]),\n",
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       "   array([3.        , 2.99999976, 3.00000024]),\n",
       "   array([2.99999976, 3.        , 3.        ]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.00000024, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3.        , 2.99999976, 3.        ]),\n",
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       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3.00000024, 3.        , 3.        ]),\n",
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       "   array([3.        , 3.        , 2.99999976]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 2.99999976]),\n",
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       "   array([3., 3., 3.]),\n",
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       "   array([2.99999976, 3.        , 2.99999976]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3.        , 3.        , 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3.00000024, 3.        , 3.        ]),\n",
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       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
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       "   array([3.        , 3.00000024, 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
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       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   ...],\n",
       "  'original_sizes': [(98, 70, 70),\n",
       "   (76, 76, 68),\n",
       "   (82, 158, 158),\n",
       "   (44, 114, 114),\n",
       "   (100, 82, 98),\n",
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       "   (204, 124, 124),\n",
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       "   (132, 116, 116),\n",
       "   (59, 136, 136),\n",
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       "   ...],\n",
       "  'preprocessed_data_folder': '/home/team/data/nnunet/preprocessed/Task256_TotalSegmentator_3mm_1139subj',\n",
       "  'num_classes': 104,\n",
       "  'all_classes': [1,\n",
       "   2,\n",
       "   3,\n",
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       "   101,\n",
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       "   103,\n",
       "   104],\n",
       "  'base_num_features': 32,\n",
       "  'use_mask_for_norm': OrderedDict([(0, False)]),\n",
       "  'keep_only_largest_region': None,\n",
       "  'min_region_size_per_class': None,\n",
       "  'min_size_per_class': None,\n",
       "  'transpose_forward': [0, 1, 2],\n",
       "  'transpose_backward': [0, 1, 2],\n",
       "  'data_identifier': 'nnUNetData_plans_v2.1',\n",
       "  'plans_per_stage': {0: {'batch_size': 2,\n",
       "    'num_pool_per_axis': [4, 4, 4],\n",
       "    'patch_size': array([96, 96, 96]),\n",
       "    'median_patient_size_in_voxels': array([114, 114, 120]),\n",
       "    'current_spacing': array([3., 3., 3.]),\n",
       "    'original_spacing': array([3., 3., 3.]),\n",
       "    'do_dummy_2D_data_aug': False,\n",
       "    'pool_op_kernel_sizes': [[2, 2, 2],\n",
       "     [2, 2, 2],\n",
       "     [2, 2, 2],\n",
       "     [2, 2, 2],\n",
       "     [1, 1, 1]],\n",
       "    'conv_kernel_sizes': [[3, 3, 3],\n",
       "     [3, 3, 3],\n",
       "     [3, 3, 3],\n",
       "     [3, 3, 3],\n",
       "     [3, 3, 3],\n",
       "     [3, 3, 3]]}},\n",
       "  'preprocessor_name': 'GenericPreprocessor',\n",
       "  'conv_per_stage': 2},\n",
       " 'gt_niftis_folder': None,\n",
       " 'folder_with_preprocessed_data': '/mnt/nvme/nnunet/preprocessed/Task256_TotalSegmentator_3mm_1139subj/nnUNetData_plans_v2.1_stage0',\n",
       " 'dl_tr': None,\n",
       " 'dl_val': None,\n",
       " 'num_input_channels': 1,\n",
       " 'num_classes': 105,\n",
       " 'net_pool_per_axis': [4, 4, 4],\n",
       " 'patch_size': array([96, 96, 96]),\n",
       " 'batch_size': 2,\n",
       " 'threeD': True,\n",
       " 'base_num_features': 32,\n",
       " 'intensity_properties': OrderedDict([(0,\n",
       "               OrderedDict([('local_props', None),\n",
       "                            ('median', 33.0),\n",
       "                            ('mean', -164.82027),\n",
       "                            ('sd', 470.82532),\n",
       "                            ('mn', -8221.0),\n",
       "                            ('mx', 17985.0),\n",
       "                            ('percentile_99_5', 1099.0),\n",
       "                            ('percentile_00_5', -1008.0)]))]),\n",
       " 'normalization_schemes': OrderedDict([(0, 'CT')]),\n",
       " 'net_num_pool_op_kernel_sizes': [[2, 2, 2],\n",
       "  [2, 2, 2],\n",
       "  [2, 2, 2],\n",
       "  [2, 2, 2],\n",
       "  [1, 1, 1]],\n",
       " 'net_conv_kernel_sizes': [[3, 3, 3],\n",
       "  [3, 3, 3],\n",
       "  [3, 3, 3],\n",
       "  [3, 3, 3],\n",
       "  [3, 3, 3],\n",
       "  [3, 3, 3]],\n",
       " 'basic_generator_patch_size': array([154, 154, 154]),\n",
       " 'data_aug_params': {'selected_data_channels': None,\n",
       "  'selected_seg_channels': [0],\n",
       "  'do_elastic': False,\n",
       "  'elastic_deform_alpha': (0.0, 900.0),\n",
       "  'elastic_deform_sigma': (9.0, 13.0),\n",
       "  'p_eldef': 0.2,\n",
       "  'do_scaling': True,\n",
       "  'scale_range': (0.7, 1.4),\n",
       "  'independent_scale_factor_for_each_axis': False,\n",
       "  'p_independent_scale_per_axis': 1,\n",
       "  'p_scale': 0.2,\n",
       "  'do_rotation': True,\n",
       "  'rotation_x': (-0.5235987755982988, 0.5235987755982988),\n",
       "  'rotation_y': (-0.5235987755982988, 0.5235987755982988),\n",
       "  'rotation_z': (-0.5235987755982988, 0.5235987755982988),\n",
       "  'rotation_p_per_axis': 1,\n",
       "  'p_rot': 0.2,\n",
       "  'random_crop': False,\n",
       "  'random_crop_dist_to_border': None,\n",
       "  'do_gamma': True,\n",
       "  'gamma_retain_stats': True,\n",
       "  'gamma_range': (0.7, 1.5),\n",
       "  'p_gamma': 0.3,\n",
       "  'do_mirror': False,\n",
       "  'mirror_axes': (0, 1, 2),\n",
       "  'dummy_2D': False,\n",
       "  'mask_was_used_for_normalization': OrderedDict([(0, False)]),\n",
       "  'border_mode_data': 'constant',\n",
       "  'all_segmentation_labels': None,\n",
       "  'move_last_seg_chanel_to_data': False,\n",
       "  'cascade_do_cascade_augmentations': False,\n",
       "  'cascade_random_binary_transform_p': 0.4,\n",
       "  'cascade_random_binary_transform_p_per_label': 1,\n",
       "  'cascade_random_binary_transform_size': (1, 8),\n",
       "  'cascade_remove_conn_comp_p': 0.2,\n",
       "  'cascade_remove_conn_comp_max_size_percent_threshold': 0.15,\n",
       "  'cascade_remove_conn_comp_fill_with_other_class_p': 0.0,\n",
       "  'do_additive_brightness': False,\n",
       "  'additive_brightness_p_per_sample': 0.15,\n",
       "  'additive_brightness_p_per_channel': 0.5,\n",
       "  'additive_brightness_mu': 0.0,\n",
       "  'additive_brightness_sigma': 0.1,\n",
       "  'num_threads': 12,\n",
       "  'num_cached_per_thread': 2,\n",
       "  'patch_size_for_spatialtransform': array([96, 96, 96])},\n",
       " 'transpose_forward': [0, 1, 2],\n",
       " 'transpose_backward': [0, 1, 2],\n",
       " 'batch_dice': False,\n",
       " 'online_eval_foreground_dc': [],\n",
       " 'online_eval_tp': [],\n",
       " 'online_eval_fp': [],\n",
       " 'online_eval_fn': [],\n",
       " 'classes': [1,\n",
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       "  100,\n",
       "  101,\n",
       "  102,\n",
       "  103,\n",
       "  104],\n",
       " 'do_dummy_2D_aug': False,\n",
       " 'use_mask_for_norm': OrderedDict([(0, False)]),\n",
       " 'only_keep_largest_connected_component': None,\n",
       " 'min_region_size_per_class': None,\n",
       " 'min_size_per_class': None,\n",
       " 'inference_pad_border_mode': 'constant',\n",
       " 'inference_pad_kwargs': {'constant_values': 0},\n",
       " 'pad_all_sides': None,\n",
       " 'lr_scheduler_eps': 0.001,\n",
       " 'lr_scheduler_patience': 30,\n",
       " 'initial_lr': 0.01,\n",
       " 'weight_decay': 3e-05,\n",
       " 'oversample_foreground_percent': 0.33,\n",
       " 'conv_per_stage': 2,\n",
       " 'regions_class_order': None,\n",
       " 'deep_supervision_scales': [[1, 1, 1],\n",
       "  [0.5, 0.5, 0.5],\n",
       "  [0.25, 0.25, 0.25],\n",
       "  [0.125, 0.125, 0.125],\n",
       "  [0.0625, 0.0625, 0.0625]],\n",
       " 'ds_loss_weights': array([0.53333333, 0.26666667, 0.13333333, 0.06666667, 0.        ]),\n",
       " 'pin_memory': True}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(trainer)\n",
    "vars(trainer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "015bdef8-9a96-412f-82ac-e93f238f373f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "trainer.plot_network_architecture()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf54ddb-c4e5-49ee-9d84-19c98cdc0e03",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Test with main class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83cab59-257f-443f-bf15-e48685a64064",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Test full resolution (without resampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a22f7289-b6e1-4923-a4a3-8b659b9ace71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading files for model Task251_TotalSegmentator_part1_organs_1139subj at : http://94.16.105.223/static/Task251_TotalSegmentator_part1_organs_1139subj.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████| 224M/224M [00:02<00:00, 90.6MiB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting files...\n",
      "Initializing model with kwargs : {'model': {'architecture_name': 'totalsegmentator', 'input_shape': (None, None, None, 1), 'output_dim': 18, 'final_activation': None, 'pretrained': 'Task251_TotalSegmentator_part1_organs_1139subj'}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 09:57:22.093194: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available folds (fold_0 used) : ['fold_0']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_localization': 40, 'conv_blocks_context': 48, 'tu': 5, 'seg_outputs': 5}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights transfered successfully !\n",
      "Initializing submodel : `model` !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_context': 48, 'tu': 5, 'conv_blocks_localization': 40, 'seg_outputs': 1}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submodel model saved in pretrained_models/test2_task_251/saving/model.json !\n",
      "Model test2_task_251 initialized successfully !\n",
      "\n",
      "========== test2_task_251 ==========\n",
      "Sub model model\n",
      "- Inputs \t: (None, None, None, None, 1)\n",
      "- Outputs \t: (None, None, None, None, 18)\n",
      "- Number of layers \t: 122\n",
      "- Number of parameters \t: 31.194 Millions\n",
      "- Model not compiled\n",
      "\n",
      "Transfer-learning from : Task251_TotalSegmentator_part1_organs_1139subj\n",
      "Already trained on 0 epochs (0 steps)\n",
      "\n",
      "- Image size : (None, None, 1)\n",
      "- Normalization style : OrderedDict([('local_props', None), ('median', -685.0), ('mean', -430.8972), ('sd', 454.4303), ('mn', -2583.0), ('mx', 5652.0), ('percentile_99_5', 398.0), ('percentile_00_5', -1006.0)])\n",
      "- Labels (n = 18) : [None, 'spleen', 'kidney_right', 'kidney_left', 'gallbladder', 'liver', 'stomach', 'aorta', 'inferior_vena_cava', 'portal_vein_and_splenic_vein', 'pancreas', 'adrenal_gland_right', 'adrenal_gland_left', 'lung_upper_lobe_left', 'lung_lower_lobe_left', 'lung_upper_lobe_right', 'lung_middle_lobe_right', 'lung_lower_lobe_right']\n",
      "- Voxel dims : [1.5 1.5 1.5]\n",
      "- # frames (axis = 2) : variable\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from models.detection import med_unet, med_unet_segmentator\n",
    "\n",
    "model = med_unet_segmentator.MedUNetSegmentator(\n",
    "    nom = 'test2_task_{}'.format(pretrained_task), pretrained = pretrained_name, labels = []\n",
    ")\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "54192aef-d066-45ed-a993-d215592af2d9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using preprocessor GenericPreprocessor\n",
      "before crop: (1, 213, 188, 249) after crop: (1, 213, 188, 249) spacing: [1.49999988 1.5        1.49999988] \n",
      "\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1.49999988, 1.5       , 1.49999988]), 'spacing_transposed': array([1.49999988, 1.5       , 1.49999988]), 'data.shape (data is transposed)': (1, 213, 188, 249)} \n",
      "after:  {'spacing': array([1.5, 1.5, 1.5]), 'data.shape (data is resampled)': (1, 213, 188, 249)} \n",
      "\n",
      "(1, 213, 188, 249)\n",
      "preprocessing...\n",
      "using preprocessor GenericPreprocessor\n",
      "before crop: (1, 213, 188, 249) after crop: (1, 213, 188, 249) spacing: [1.49999988 1.5        1.49999988] \n",
      "\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1.49999988, 1.5       , 1.49999988]), 'spacing_transposed': array([1.49999988, 1.5       , 1.49999988]), 'data.shape (data is transposed)': (1, 213, 188, 249)} \n",
      "after:  {'spacing': array([1.5, 1.5, 1.5]), 'data.shape (data is resampled)': (1, 213, 188, 249)} \n",
      "\n",
      "predicting...\n",
      "debug: mirroring False mirror_axes (0, 1, 2)\n",
      "step_size: 0.5\n",
      "do mirror: False\n",
      "data shape: (1, 213, 188, 249)\n",
      "patch size: [128 128 128]\n",
      "steps (x, y, and z): [[0, 42, 85], [0, 60], [0, 60, 121]]\n",
      "number of tiles: 18\n",
      "computing Gaussian\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/.local/lib/python3.9/site-packages/torch/amp/autocast_mode.py:204: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction done\n",
      "done\n",
      "(18, 213, 188, 249)\n"
     ]
    }
   ],
   "source": [
    "path = '/storage/Totalsegmentator_dataset/s0001/ct.nii.gz'\n",
    "\n",
    "inp, seg, infos = trainer.preprocess_patient([path])\n",
    "print(inp.shape)\n",
    "out = trainer.preprocess_predict_nifti([path], save = False)\n",
    "print(out.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5046e6b-d2a7-495d-9947-8d67276bc656",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value are equals !\n",
      "Values differ (19085 / 9970956 diff, 0.002%) : max 17 - mean 0.01962229098192791 - min 0\n",
      "CPU times: user 1min 23s, sys: 1.45 s, total: 1min 24s\n",
      "Wall time: 9.43 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "tf_inp = model.get_input(path)\n",
    "\n",
    "print(is_equal(inp, tf_inp[tf.newaxis, :, :, :, 0])[1])\n",
    "\n",
    "tf_out = model.infer(tf_inp, win_len = 128)\n",
    "\n",
    "print(is_equal(np.transpose(out, [3, 2, 1, 0]).argmax(axis = -1).astype(np.int32), tf_out)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7169c044-dad5-4b7b-84b1-f9a1676eba4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value are equals !\n",
      "steps : [array([ 0, 42, 85], dtype=int32), array([ 0, 60], dtype=int32), array([  0,  60, 121], dtype=int32)]\n",
      "Values differ (6 / 9970956 diff, 0.000%) : max 17 - mean 3.409903724377081e-06 - min 0\n",
      "CPU times: user 3min 22s, sys: 1 s, total: 3min 23s\n",
      "Wall time: 32.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "tf_inp = model.get_input(path)\n",
    "\n",
    "print(is_equal(inp, tf_inp[tf.newaxis, :, :, :, 0])[1])\n",
    "\n",
    "tf_out = model.infer(tf_inp, win_len = trainer.patch_size)\n",
    "\n",
    "print(is_equal(np.transpose(out, [3, 2, 1, 0]).argmax(axis = -1).astype(np.int32), tf_out)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d018d6-237e-4a30-9026-c2b2ab96276b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Test full resolution with the 5 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86e76e08-204f-4742-b033-dc46baf980e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-27 10:20:53.369879: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-06-27 10:20:53.487842: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-06-27 10:20:53.512966: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n",
      "/etinfo/users2/qlanglois/.local/lib/python3.9/site-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
      "\n",
      "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
      "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
      "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
      "\n",
      "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing model with kwargs : {'model': {'architecture_name': 'totalsegmentator', 'input_shape': (None, None, None, 1), 'output_dim': 18, 'final_activation': None, 'pretrained': 'Task251_TotalSegmentator_part1_organs_1139subj'}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-27 10:21:00.515415: E tensorflow/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2023-06-27 10:21:00.515448: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: metropolis\n",
      "2023-06-27 10:21:00.515452: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: metropolis\n",
      "2023-06-27 10:21:00.515493: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 470.57.2\n",
      "2023-06-27 10:21:00.515533: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 470.57.2\n",
      "2023-06-27 10:21:00.515551: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 470.57.2\n",
      "2023-06-27 10:21:00.522449: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available folds (fold_0 used) : ['fold_0']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_localization': 40, 'conv_blocks_context': 48, 'tu': 5, 'seg_outputs': 5}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n",
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_context': 48, 'tu': 5, 'conv_blocks_localization': 40, 'seg_outputs': 1}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights transfered successfully !\n",
      "Initializing submodel : `model` !\n",
      "Submodel model saved in pretrained_models/totalsegmentator_251/saving/model.json !\n",
      "Model totalsegmentator_251 initialized successfully !\n",
      "Downloading files for model Task252_TotalSegmentator_part2_vertebrae_1139subj at : http://94.16.105.223/static/Task252_TotalSegmentator_part2_vertebrae_1139subj.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 218M/218M [00:02<00:00, 112MiB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting files...\n",
      "Initializing model with kwargs : {'model': {'architecture_name': 'totalsegmentator', 'input_shape': (None, None, None, 1), 'output_dim': 25, 'final_activation': None, 'pretrained': 'Task252_TotalSegmentator_part2_vertebrae_1139subj'}}\n",
      "Available folds (fold_0 used) : ['fold_0']\n",
      "Weights transfered successfully !\n",
      "Initializing submodel : `model` !\n",
      "Submodel model saved in pretrained_models/totalsegmentator_252/saving/model.json !\n",
      "Model totalsegmentator_252 initialized successfully !\n",
      "Initializing model with kwargs : {'model': {'architecture_name': 'totalsegmentator', 'input_shape': (None, None, None, 1), 'output_dim': 19, 'final_activation': None, 'pretrained': 'Task253_TotalSegmentator_part3_cardiac_1139subj'}}\n",
      "Available folds (fold_0 used) : ['fold_0']\n",
      "Weights transfered successfully !\n",
      "Initializing submodel : `model` !\n",
      "Submodel model saved in pretrained_models/totalsegmentator_253/saving/model.json !\n",
      "Model totalsegmentator_253 initialized successfully !\n",
      "Downloading files for model Task254_TotalSegmentator_part4_muscles_1139subj at : http://94.16.105.223/static/Task254_TotalSegmentator_part4_muscles_1139subj.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 224M/224M [00:02<00:00, 111MiB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting files...\n",
      "Initializing model with kwargs : {'model': {'architecture_name': 'totalsegmentator', 'input_shape': (None, None, None, 1), 'output_dim': 22, 'final_activation': None, 'pretrained': 'Task254_TotalSegmentator_part4_muscles_1139subj'}}\n",
      "Available folds (fold_0 used) : ['fold_0']\n",
      "Weights transfered successfully !\n",
      "Initializing submodel : `model` !\n",
      "Submodel model saved in pretrained_models/totalsegmentator_254/saving/model.json !\n",
      "Model totalsegmentator_254 initialized successfully !\n",
      "Downloading files for model Task255_TotalSegmentator_part5_ribs_1139subj at : http://94.16.105.223/static/Task255_TotalSegmentator_part5_ribs_1139subj.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████| 219M/219M [00:02<00:00, 82.4MiB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting files...\n",
      "Initializing model with kwargs : {'model': {'architecture_name': 'totalsegmentator', 'input_shape': (None, None, None, 1), 'output_dim': 25, 'final_activation': None, 'pretrained': 'Task255_TotalSegmentator_part5_ribs_1139subj'}}\n",
      "Available folds (fold_0 used) : ['fold_0']\n",
      "Weights transfered successfully !\n",
      "Initializing submodel : `model` !\n",
      "Submodel model saved in pretrained_models/totalsegmentator_255/saving/model.json !\n",
      "Model totalsegmentator_255 initialized successfully !\n",
      "\n",
      "========== totalsegmentator_251 ==========\n",
      "Sub model model\n",
      "- Inputs \t: (None, None, None, None, 1)\n",
      "- Outputs \t: (None, None, None, None, 18)\n",
      "- Number of layers \t: 122\n",
      "- Number of parameters \t: 31.194 Millions\n",
      "- Model not compiled\n",
      "\n",
      "Transfer-learning from : Task251_TotalSegmentator_part1_organs_1139subj\n",
      "Already trained on 0 epochs (0 steps)\n",
      "\n",
      "- Image size : (None, None, 1)\n",
      "- Resize config : {'interpolation': 'bicubic', 'preserve_aspect_ratio': False}\n",
      "- Normalization style : OrderedDict([('local_props', None), ('median', -685.0), ('mean', -430.8972), ('sd', 454.4303), ('mn', -2583.0), ('mx', 5652.0), ('percentile_99_5', 398.0), ('percentile_00_5', -1006.0)])\n",
      "- Labels (n = 18) : [None, 'spleen', 'kidney_right', 'kidney_left', 'gallbladder', 'liver', 'stomach', 'aorta', 'inferior_vena_cava', 'portal_vein_and_splenic_vein', 'pancreas', 'adrenal_gland_right', 'adrenal_gland_left', 'lung_upper_lobe_left', 'lung_lower_lobe_left', 'lung_upper_lobe_right', 'lung_middle_lobe_right', 'lung_lower_lobe_right']\n",
      "- Voxel dims : [1.5 1.5 1.5]\n",
      "- # frames (axis = 2) : variable\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import tensorflow as tf\n",
    "\n",
    "from utils.med_utils import *\n",
    "from loggers import set_level\n",
    "from models import get_pretrained\n",
    "from models.detection import med_unet, med_unet_segmentator\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = ''\n",
    "try:\n",
    "    tf.config.set_visible_devices([], 'GPU')\n",
    "except:\n",
    "    pass\n",
    "\n",
    "models       = []\n",
    "all_task_ids = (251, 252, 253, 254, 255)\n",
    "for task_id in all_task_ids:\n",
    "    models.append(med_unet_segmentator.MedUNetSegmentator(\n",
    "        nom = 'totalsegmentator_{}'.format(task_id), pretrained_task_id = task_id, labels = []\n",
    "    ))\n",
    "\n",
    "model = models[0]\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9457072c-c4a1-48e6-9bad-fa0695212631",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-27 14:08:57.232846: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-06-27 14:08:57.406013: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-06-27 14:08:57.430462: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n",
      "/etinfo/users2/qlanglois/.local/lib/python3.9/site-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
      "\n",
      "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
      "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
      "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
      "\n",
      "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model restoration...\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-27 14:09:13.897373: E tensorflow/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2023-06-27 14:09:13.897407: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: metropolis\n",
      "2023-06-27 14:09:13.897412: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: metropolis\n",
      "2023-06-27 14:09:13.897453: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 470.57.2\n",
      "2023-06-27 14:09:13.897467: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 470.57.2\n",
      "2023-06-27 14:09:13.897470: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 470.57.2\n",
      "2023-06-27 14:09:13.914565: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing submodel : `model` !\n",
      "Successfully restored model from pretrained_models/totalsegmentator_251/saving/model.json !\n",
      "Model totalsegmentator_251 initialized successfully !\n",
      "\n",
      "========== totalsegmentator_251 ==========\n",
      "Sub model model\n",
      "- Inputs \t: (None, None, None, None, 1)\n",
      "- Outputs \t: (None, None, None, None, 18)\n",
      "- Number of layers \t: 122\n",
      "- Number of parameters \t: 31.194 Millions\n",
      "- Model not compiled\n",
      "\n",
      "Transfer-learning from : Task251_TotalSegmentator_part1_organs_1139subj\n",
      "Already trained on 0 epochs (0 steps)\n",
      "\n",
      "- Image size : (None, None, 1)\n",
      "- Resize config : {'interpolation': 'bicubic', 'preserve_aspect_ratio': False}\n",
      "- Normalization style : OrderedDict([('local_props', None), ('median', -685.0), ('mean', -430.8972), ('sd', 454.4303), ('mn', -2583.0), ('mx', 5652.0), ('percentile_99_5', 398.0), ('percentile_00_5', -1006.0)])\n",
      "- Labels (n = 18) : [None, 'spleen', 'kidney_right', 'kidney_left', 'gallbladder', 'liver', 'stomach', 'aorta', 'inferior_vena_cava', 'portal_vein_and_splenic_vein', 'pancreas', 'adrenal_gland_right', 'adrenal_gland_left', 'lung_upper_lobe_left', 'lung_lower_lobe_left', 'lung_upper_lobe_right', 'lung_middle_lobe_right', 'lung_lower_lobe_right']\n",
      "- Voxel dims : [1.5 1.5 1.5]\n",
      "- # frames (axis = 2) : variable\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import tensorflow as tf\n",
    "\n",
    "from utils.med_utils import *\n",
    "from loggers import set_level\n",
    "from models import get_pretrained\n",
    "from models.detection import med_unet, med_unet_segmentator\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = ''\n",
    "try:\n",
    "    tf.config.set_visible_devices([], 'GPU')\n",
    "except:\n",
    "    pass\n",
    "\n",
    "models       = []\n",
    "all_task_ids = (251, 252, 253, 254, 255)\n",
    "for task_id in all_task_ids:\n",
    "    models.append('totalsegmentator_{}'.format(task_id))\n",
    "\n",
    "model = get_pretrained(models[0])\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdb6da27-4046-40fc-94dd-de38d064f08d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "========== Summary of model ==========\n",
      "\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_image (InputLayer)       [(None, None, None,  0           []                               \n",
      "                                 None, 1)]                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d (ZeroPadding3D)  (None, None, None,   0          ['input_image[0][0]']            \n",
      "                                None, 1)                                                          \n",
      "                                                                                                  \n",
      " conv_blocks_context/0/blocks/0  (None, None, None,   896        ['zero_padding3d[0][0]']         \n",
      " /conv (Conv3D)                 None, 32)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_context/0/blocks/0  (None, None, None,   64         ['conv_blocks_context/0/blocks/0/\n",
      " /norm (InstanceNormalization)  None, 32)                        conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu (LeakyReLU)        (None, None, None,   0           ['conv_blocks_context/0/blocks/0/\n",
      "                                None, 32)                        norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, None, None,   0           ['leaky_re_lu[0][0]']            \n",
      "                                None, 32)                                                         \n",
      "                                                                                                  \n",
      " zero_padding3d_1 (ZeroPadding3  (None, None, None,   0          ['dropout[0][0]']                \n",
      " D)                             None, 32)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_context/0/blocks/1  (None, None, None,   27680      ['zero_padding3d_1[0][0]']       \n",
      " /conv (Conv3D)                 None, 32)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_context/0/blocks/1  (None, None, None,   64         ['conv_blocks_context/0/blocks/1/\n",
      " /norm (InstanceNormalization)  None, 32)                        conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_1 (LeakyReLU)      (None, None, None,   0           ['conv_blocks_context/0/blocks/1/\n",
      "                                None, 32)                        norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, None, None,   0           ['leaky_re_lu_1[0][0]']          \n",
      "                                None, 32)                                                         \n",
      "                                                                                                  \n",
      " zero_padding3d_2 (ZeroPadding3  (None, None, None,   0          ['dropout_1[0][0]']              \n",
      " D)                             None, 32)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_context/1/blocks/0  (None, None, None,   55360      ['zero_padding3d_2[0][0]']       \n",
      " /conv (Conv3D)                 None, 64)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_context/1/blocks/0  (None, None, None,   128        ['conv_blocks_context/1/blocks/0/\n",
      " /norm (InstanceNormalization)  None, 64)                        conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_2 (LeakyReLU)      (None, None, None,   0           ['conv_blocks_context/1/blocks/0/\n",
      "                                None, 64)                        norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, None, None,   0           ['leaky_re_lu_2[0][0]']          \n",
      "                                None, 64)                                                         \n",
      "                                                                                                  \n",
      " zero_padding3d_3 (ZeroPadding3  (None, None, None,   0          ['dropout_2[0][0]']              \n",
      " D)                             None, 64)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_context/1/blocks/1  (None, None, None,   110656     ['zero_padding3d_3[0][0]']       \n",
      " /conv (Conv3D)                 None, 64)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_context/1/blocks/1  (None, None, None,   128        ['conv_blocks_context/1/blocks/1/\n",
      " /norm (InstanceNormalization)  None, 64)                        conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_3 (LeakyReLU)      (None, None, None,   0           ['conv_blocks_context/1/blocks/1/\n",
      "                                None, 64)                        norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)            (None, None, None,   0           ['leaky_re_lu_3[0][0]']          \n",
      "                                None, 64)                                                         \n",
      "                                                                                                  \n",
      " zero_padding3d_4 (ZeroPadding3  (None, None, None,   0          ['dropout_3[0][0]']              \n",
      " D)                             None, 64)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_context/2/blocks/0  (None, None, None,   221312     ['zero_padding3d_4[0][0]']       \n",
      " /conv (Conv3D)                 None, 128)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/2/blocks/0  (None, None, None,   256        ['conv_blocks_context/2/blocks/0/\n",
      " /norm (InstanceNormalization)  None, 128)                       conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_4 (LeakyReLU)      (None, None, None,   0           ['conv_blocks_context/2/blocks/0/\n",
      "                                None, 128)                       norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)            (None, None, None,   0           ['leaky_re_lu_4[0][0]']          \n",
      "                                None, 128)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_5 (ZeroPadding3  (None, None, None,   0          ['dropout_4[0][0]']              \n",
      " D)                             None, 128)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/2/blocks/1  (None, None, None,   442496     ['zero_padding3d_5[0][0]']       \n",
      " /conv (Conv3D)                 None, 128)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/2/blocks/1  (None, None, None,   256        ['conv_blocks_context/2/blocks/1/\n",
      " /norm (InstanceNormalization)  None, 128)                       conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_5 (LeakyReLU)      (None, None, None,   0           ['conv_blocks_context/2/blocks/1/\n",
      "                                None, 128)                       norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout_5 (Dropout)            (None, None, None,   0           ['leaky_re_lu_5[0][0]']          \n",
      "                                None, 128)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_6 (ZeroPadding3  (None, None, None,   0          ['dropout_5[0][0]']              \n",
      " D)                             None, 128)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/3/blocks/0  (None, None, None,   884992     ['zero_padding3d_6[0][0]']       \n",
      " /conv (Conv3D)                 None, 256)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/3/blocks/0  (None, None, None,   512        ['conv_blocks_context/3/blocks/0/\n",
      " /norm (InstanceNormalization)  None, 256)                       conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_6 (LeakyReLU)      (None, None, None,   0           ['conv_blocks_context/3/blocks/0/\n",
      "                                None, 256)                       norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout_6 (Dropout)            (None, None, None,   0           ['leaky_re_lu_6[0][0]']          \n",
      "                                None, 256)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_7 (ZeroPadding3  (None, None, None,   0          ['dropout_6[0][0]']              \n",
      " D)                             None, 256)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/3/blocks/1  (None, None, None,   1769728    ['zero_padding3d_7[0][0]']       \n",
      " /conv (Conv3D)                 None, 256)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/3/blocks/1  (None, None, None,   512        ['conv_blocks_context/3/blocks/1/\n",
      " /norm (InstanceNormalization)  None, 256)                       conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_7 (LeakyReLU)      (None, None, None,   0           ['conv_blocks_context/3/blocks/1/\n",
      "                                None, 256)                       norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout_7 (Dropout)            (None, None, None,   0           ['leaky_re_lu_7[0][0]']          \n",
      "                                None, 256)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_8 (ZeroPadding3  (None, None, None,   0          ['dropout_7[0][0]']              \n",
      " D)                             None, 256)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/4/blocks/0  (None, None, None,   2212160    ['zero_padding3d_8[0][0]']       \n",
      " /conv (Conv3D)                 None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/4/blocks/0  (None, None, None,   640        ['conv_blocks_context/4/blocks/0/\n",
      " /norm (InstanceNormalization)  None, 320)                       conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_8 (LeakyReLU)      (None, None, None,   0           ['conv_blocks_context/4/blocks/0/\n",
      "                                None, 320)                       norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout_8 (Dropout)            (None, None, None,   0           ['leaky_re_lu_8[0][0]']          \n",
      "                                None, 320)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_9 (ZeroPadding3  (None, None, None,   0          ['dropout_8[0][0]']              \n",
      " D)                             None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/4/blocks/1  (None, None, None,   2765120    ['zero_padding3d_9[0][0]']       \n",
      " /conv (Conv3D)                 None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/4/blocks/1  (None, None, None,   640        ['conv_blocks_context/4/blocks/1/\n",
      " /norm (InstanceNormalization)  None, 320)                       conv[0][0]']                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_9 (LeakyReLU)      (None, None, None,   0           ['conv_blocks_context/4/blocks/1/\n",
      "                                None, 320)                       norm[0][0]']                     \n",
      "                                                                                                  \n",
      " dropout_9 (Dropout)            (None, None, None,   0           ['leaky_re_lu_9[0][0]']          \n",
      "                                None, 320)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_10 (ZeroPadding  (None, None, None,   0          ['dropout_9[0][0]']              \n",
      " 3D)                            None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/5/0/blocks  (None, None, None,   2765120    ['zero_padding3d_10[0][0]']      \n",
      " /0/conv (Conv3D)               None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/5/0/blocks  (None, None, None,   640        ['conv_blocks_context/5/0/blocks/\n",
      " /0/norm (InstanceNormalization  None, 320)                      0/conv[0][0]']                   \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " leaky_re_lu_10 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_context/5/0/blocks/\n",
      "                                None, 320)                       0/norm[0][0]']                   \n",
      "                                                                                                  \n",
      " dropout_10 (Dropout)           (None, None, None,   0           ['leaky_re_lu_10[0][0]']         \n",
      "                                None, 320)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_11 (ZeroPadding  (None, None, None,   0          ['dropout_10[0][0]']             \n",
      " 3D)                            None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/5/1/blocks  (None, None, None,   2765120    ['zero_padding3d_11[0][0]']      \n",
      " /1/conv (Conv3D)               None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_context/5/1/blocks  (None, None, None,   640        ['conv_blocks_context/5/1/blocks/\n",
      " /1/norm (InstanceNormalization  None, 320)                      1/conv[0][0]']                   \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " leaky_re_lu_11 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_context/5/1/blocks/\n",
      "                                None, 320)                       1/norm[0][0]']                   \n",
      "                                                                                                  \n",
      " dropout_11 (Dropout)           (None, None, None,   0           ['leaky_re_lu_11[0][0]']         \n",
      "                                None, 320)                                                        \n",
      "                                                                                                  \n",
      " tu/0 (Conv3DTranspose)         (None, None, None,   819200      ['dropout_11[0][0]']             \n",
      "                                None, 320)                                                        \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, None, None,   0           ['tu/0[0][0]',                   \n",
      "                                None, 640)                        'dropout_9[0][0]']              \n",
      "                                                                                                  \n",
      " zero_padding3d_12 (ZeroPadding  (None, None, None,   0          ['concatenate[0][0]']            \n",
      " 3D)                            None, 640)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/0/0/b  (None, None, None,   5529920    ['zero_padding3d_12[0][0]']      \n",
      " locks/0/conv (Conv3D)          None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/0/0/b  (None, None, None,   640        ['conv_blocks_localization/0/0/bl\n",
      " locks/0/norm (InstanceNormaliz  None, 320)                      ocks/0/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_12 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/0/0/bl\n",
      "                                None, 320)                       ocks/0/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_12 (Dropout)           (None, None, None,   0           ['leaky_re_lu_12[0][0]']         \n",
      "                                None, 320)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_13 (ZeroPadding  (None, None, None,   0          ['dropout_12[0][0]']             \n",
      " 3D)                            None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/0/1/b  (None, None, None,   2765120    ['zero_padding3d_13[0][0]']      \n",
      " locks/1/conv (Conv3D)          None, 320)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/0/1/b  (None, None, None,   640        ['conv_blocks_localization/0/1/bl\n",
      " locks/1/norm (InstanceNormaliz  None, 320)                      ocks/1/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_13 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/0/1/bl\n",
      "                                None, 320)                       ocks/1/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_13 (Dropout)           (None, None, None,   0           ['leaky_re_lu_13[0][0]']         \n",
      "                                None, 320)                                                        \n",
      "                                                                                                  \n",
      " tu/1 (Conv3DTranspose)         (None, None, None,   655360      ['dropout_13[0][0]']             \n",
      "                                None, 256)                                                        \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, None, None,   0           ['tu/1[0][0]',                   \n",
      "                                None, 512)                        'dropout_7[0][0]']              \n",
      "                                                                                                  \n",
      " zero_padding3d_14 (ZeroPadding  (None, None, None,   0          ['concatenate_1[0][0]']          \n",
      " 3D)                            None, 512)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/1/0/b  (None, None, None,   3539200    ['zero_padding3d_14[0][0]']      \n",
      " locks/0/conv (Conv3D)          None, 256)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/1/0/b  (None, None, None,   512        ['conv_blocks_localization/1/0/bl\n",
      " locks/0/norm (InstanceNormaliz  None, 256)                      ocks/0/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_14 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/1/0/bl\n",
      "                                None, 256)                       ocks/0/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_14 (Dropout)           (None, None, None,   0           ['leaky_re_lu_14[0][0]']         \n",
      "                                None, 256)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_15 (ZeroPadding  (None, None, None,   0          ['dropout_14[0][0]']             \n",
      " 3D)                            None, 256)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/1/1/b  (None, None, None,   1769728    ['zero_padding3d_15[0][0]']      \n",
      " locks/1/conv (Conv3D)          None, 256)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/1/1/b  (None, None, None,   512        ['conv_blocks_localization/1/1/bl\n",
      " locks/1/norm (InstanceNormaliz  None, 256)                      ocks/1/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_15 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/1/1/bl\n",
      "                                None, 256)                       ocks/1/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_15 (Dropout)           (None, None, None,   0           ['leaky_re_lu_15[0][0]']         \n",
      "                                None, 256)                                                        \n",
      "                                                                                                  \n",
      " tu/2 (Conv3DTranspose)         (None, None, None,   262144      ['dropout_15[0][0]']             \n",
      "                                None, 128)                                                        \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate)    (None, None, None,   0           ['tu/2[0][0]',                   \n",
      "                                None, 256)                        'dropout_5[0][0]']              \n",
      "                                                                                                  \n",
      " zero_padding3d_16 (ZeroPadding  (None, None, None,   0          ['concatenate_2[0][0]']          \n",
      " 3D)                            None, 256)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/2/0/b  (None, None, None,   884864     ['zero_padding3d_16[0][0]']      \n",
      " locks/0/conv (Conv3D)          None, 128)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/2/0/b  (None, None, None,   256        ['conv_blocks_localization/2/0/bl\n",
      " locks/0/norm (InstanceNormaliz  None, 128)                      ocks/0/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_16 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/2/0/bl\n",
      "                                None, 128)                       ocks/0/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_16 (Dropout)           (None, None, None,   0           ['leaky_re_lu_16[0][0]']         \n",
      "                                None, 128)                                                        \n",
      "                                                                                                  \n",
      " zero_padding3d_17 (ZeroPadding  (None, None, None,   0          ['dropout_16[0][0]']             \n",
      " 3D)                            None, 128)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/2/1/b  (None, None, None,   442496     ['zero_padding3d_17[0][0]']      \n",
      " locks/1/conv (Conv3D)          None, 128)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/2/1/b  (None, None, None,   256        ['conv_blocks_localization/2/1/bl\n",
      " locks/1/norm (InstanceNormaliz  None, 128)                      ocks/1/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_17 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/2/1/bl\n",
      "                                None, 128)                       ocks/1/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_17 (Dropout)           (None, None, None,   0           ['leaky_re_lu_17[0][0]']         \n",
      "                                None, 128)                                                        \n",
      "                                                                                                  \n",
      " tu/3 (Conv3DTranspose)         (None, None, None,   65536       ['dropout_17[0][0]']             \n",
      "                                None, 64)                                                         \n",
      "                                                                                                  \n",
      " concatenate_3 (Concatenate)    (None, None, None,   0           ['tu/3[0][0]',                   \n",
      "                                None, 128)                        'dropout_3[0][0]']              \n",
      "                                                                                                  \n",
      " zero_padding3d_18 (ZeroPadding  (None, None, None,   0          ['concatenate_3[0][0]']          \n",
      " 3D)                            None, 128)                                                        \n",
      "                                                                                                  \n",
      " conv_blocks_localization/3/0/b  (None, None, None,   221248     ['zero_padding3d_18[0][0]']      \n",
      " locks/0/conv (Conv3D)          None, 64)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_localization/3/0/b  (None, None, None,   128        ['conv_blocks_localization/3/0/bl\n",
      " locks/0/norm (InstanceNormaliz  None, 64)                       ocks/0/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_18 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/3/0/bl\n",
      "                                None, 64)                        ocks/0/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_18 (Dropout)           (None, None, None,   0           ['leaky_re_lu_18[0][0]']         \n",
      "                                None, 64)                                                         \n",
      "                                                                                                  \n",
      " zero_padding3d_19 (ZeroPadding  (None, None, None,   0          ['dropout_18[0][0]']             \n",
      " 3D)                            None, 64)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_localization/3/1/b  (None, None, None,   110656     ['zero_padding3d_19[0][0]']      \n",
      " locks/1/conv (Conv3D)          None, 64)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_localization/3/1/b  (None, None, None,   128        ['conv_blocks_localization/3/1/bl\n",
      " locks/1/norm (InstanceNormaliz  None, 64)                       ocks/1/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_19 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/3/1/bl\n",
      "                                None, 64)                        ocks/1/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_19 (Dropout)           (None, None, None,   0           ['leaky_re_lu_19[0][0]']         \n",
      "                                None, 64)                                                         \n",
      "                                                                                                  \n",
      " tu/4 (Conv3DTranspose)         (None, None, None,   16384       ['dropout_19[0][0]']             \n",
      "                                None, 32)                                                         \n",
      "                                                                                                  \n",
      " concatenate_4 (Concatenate)    (None, None, None,   0           ['tu/4[0][0]',                   \n",
      "                                None, 64)                         'dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " zero_padding3d_20 (ZeroPadding  (None, None, None,   0          ['concatenate_4[0][0]']          \n",
      " 3D)                            None, 64)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_localization/4/0/b  (None, None, None,   55328      ['zero_padding3d_20[0][0]']      \n",
      " locks/0/conv (Conv3D)          None, 32)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_localization/4/0/b  (None, None, None,   64         ['conv_blocks_localization/4/0/bl\n",
      " locks/0/norm (InstanceNormaliz  None, 32)                       ocks/0/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_20 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/4/0/bl\n",
      "                                None, 32)                        ocks/0/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_20 (Dropout)           (None, None, None,   0           ['leaky_re_lu_20[0][0]']         \n",
      "                                None, 32)                                                         \n",
      "                                                                                                  \n",
      " zero_padding3d_21 (ZeroPadding  (None, None, None,   0          ['dropout_20[0][0]']             \n",
      " 3D)                            None, 32)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_localization/4/1/b  (None, None, None,   27680      ['zero_padding3d_21[0][0]']      \n",
      " locks/1/conv (Conv3D)          None, 32)                                                         \n",
      "                                                                                                  \n",
      " conv_blocks_localization/4/1/b  (None, None, None,   64         ['conv_blocks_localization/4/1/bl\n",
      " locks/1/norm (InstanceNormaliz  None, 32)                       ocks/1/conv[0][0]']              \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " leaky_re_lu_21 (LeakyReLU)     (None, None, None,   0           ['conv_blocks_localization/4/1/bl\n",
      "                                None, 32)                        ocks/1/norm[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_21 (Dropout)           (None, None, None,   0           ['leaky_re_lu_21[0][0]']         \n",
      "                                None, 32)                                                         \n",
      "                                                                                                  \n",
      " seg_outputs/4 (Conv3D)         (None, None, None,   576         ['dropout_21[0][0]']             \n",
      "                                None, 18)                                                         \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 31,193,760\n",
      "Trainable params: 31,193,760\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae0a8b9d-7b3b-4ce7-8fe6-2e79b1188fe9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "volume shape : (1, 128, 128, 128, 1) - steps : [[0], [0], [0]]\n",
      "Making prediction with friend totalsegmentator_252...\n",
      "volume shape : (1, 128, 128, 128, 1) - steps : [[0], [0], [0]]\n",
      "Making prediction with friend totalsegmentator_253...\n",
      "volume shape : (1, 128, 128, 128, 1) - steps : [[0], [0], [0]]\n",
      "Making prediction with friend totalsegmentator_254...\n",
      "volume shape : (1, 128, 128, 128, 1) - steps : [[0], [0], [0]]\n",
      "Making prediction with friend totalsegmentator_255...\n",
      "volume shape : (1, 128, 128, 128, 1) - steps : [[0], [0], [0]]\n",
      "Timers for logger timer :\n",
      "- predict : 9.249 sec\n",
      "  - initialization : 0 ms\n",
      "  - pre-processing : 51 ms\n",
      "  - infer : 1.740 sec\n",
      "    - _get_gaussian : 0 ms\n",
      "    - patch prediction : 1.625 sec\n",
      "  - friends prediction : 7.432 sec\n",
      "    - inference of totalsegmentator_252 : 1.859 sec\n",
      "      - infer : 1.807 sec\n",
      "        - _get_gaussian : 0 ms\n",
      "        - patch prediction : 1.655 sec\n",
      "    - inference of totalsegmentator_253 : 1.811 sec\n",
      "      - infer : 1.760 sec\n",
      "        - _get_gaussian : 0 ms\n",
      "        - patch prediction : 1.639 sec\n",
      "    - inference of totalsegmentator_254 : 1.793 sec\n",
      "      - infer : 1.741 sec\n",
      "        - _get_gaussian : 0 ms\n",
      "        - patch prediction : 1.603 sec\n",
      "    - inference of totalsegmentator_255 : 1.945 sec\n",
      "      - infer : 1.895 sec\n",
      "        - _get_gaussian : 0 ms\n",
      "        - patch prediction : 1.742 sec\n",
      "    - combine_preds : 22 ms\n",
      "  - post_process : 21 ms\n",
      "Values differ (121897 / 2032254 diff, 0.060%) : max 101 - mean 3.810631446659719 - min 0\n",
      "0 Vallue differ for target with dtype bool (121897 / 2032254 diff, 0.05998118345443040344556 %)\n",
      "1 Value are equals !\n",
      "2 Value are equals !\n",
      "3 Value are equals !\n",
      "4 Value are equals !\n",
      "5 Value are equals !\n",
      "6 Value are equals !\n",
      "7 Value are equals !\n",
      "8 Value are equals !\n",
      "9 Value are equals !\n",
      "10 Value are equals !\n",
      "11 Value are equals !\n",
      "12 Value are equals !\n",
      "13 Vallue differ for target with dtype bool (91 / 2032254 diff, 0.00004477786733351244678 %)\n",
      "14 Value are equals !\n",
      "15 Vallue differ for target with dtype bool (93 / 2032254 diff, 0.00004576199628589733222 %)\n",
      "16 Value are equals !\n",
      "17 Value are equals !\n",
      "18 Value are equals !\n",
      "19 Value are equals !\n",
      "20 Value are equals !\n",
      "21 Value are equals !\n",
      "22 Value are equals !\n",
      "23 Value are equals !\n",
      "24 Value are equals !\n",
      "25 Value are equals !\n",
      "26 Value are equals !\n",
      "27 Value are equals !\n",
      "28 Value are equals !\n",
      "29 Value are equals !\n",
      "30 Value are equals !\n",
      "31 Vallue differ for target with dtype bool (109 / 2032254 diff, 0.00005363502790497644285 %)\n",
      "32 Vallue differ for target with dtype bool (3205 / 2032254 diff, 0.00157706664619678446453 %)\n",
      "33 Vallue differ for target with dtype bool (5197 / 2032254 diff, 0.00255725908277213385966 %)\n",
      "34 Vallue differ for target with dtype bool (4912 / 2032254 diff, 0.00241702070705728704406 %)\n",
      "35 Vallue differ for target with dtype bool (3672 / 2032254 diff, 0.00180686075657865603816 %)\n",
      "36 Vallue differ for target with dtype bool (2802 / 2032254 diff, 0.00137876466229122944182 %)\n",
      "37 Vallue differ for target with dtype bool (2812 / 2032254 diff, 0.00138368530705315368606 %)\n",
      "38 Vallue differ for target with dtype bool (2777 / 2032254 diff, 0.00136646305038641818069 %)\n",
      "39 Vallue differ for target with dtype bool (2826 / 2032254 diff, 0.00139057420971984793158 %)\n",
      "40 Vallue differ for target with dtype bool (3788 / 2032254 diff, 0.00186394023581697956989 %)\n",
      "41 Vallue differ for target with dtype bool (3081 / 2032254 diff, 0.00151605065114892136394 %)\n",
      "42 Vallue differ for target with dtype bool (1437 / 2032254 diff, 0.00070709665228854267239 %)\n",
      "43 Vallue differ for target with dtype bool (2782 / 2032254 diff, 0.00136892337276738051965 %)\n",
      "44 Value are equals !\n",
      "45 Value are equals !\n",
      "46 Value are equals !\n",
      "47 Value are equals !\n",
      "48 Value are equals !\n",
      "49 Value are equals !\n",
      "50 Vallue differ for target with dtype bool (27456 / 2032254 diff, 0.01351012225833975524680 %)\n",
      "51 Value are equals !\n",
      "52 Value are equals !\n",
      "53 Value are equals !\n",
      "54 Value are equals !\n",
      "55 Value are equals !\n",
      "56 Value are equals !\n",
      "57 Value are equals !\n",
      "58 Vallue differ for target with dtype bool (874 / 2032254 diff, 0.00043006435219219643499 %)\n",
      "59 Vallue differ for target with dtype bool (938 / 2032254 diff, 0.00046155647866851287750 %)\n",
      "60 Vallue differ for target with dtype bool (453 / 2032254 diff, 0.00022290520771517732812 %)\n",
      "61 Vallue differ for target with dtype bool (161 / 2032254 diff, 0.00007922238066698355239 %)\n",
      "62 Value are equals !\n",
      "63 Value are equals !\n",
      "64 Value are equals !\n",
      "65 Value are equals !\n",
      "66 Value are equals !\n",
      "67 Value are equals !\n",
      "68 Value are equals !\n",
      "69 Value are equals !\n",
      "70 Vallue differ for target with dtype bool (820 / 2032254 diff, 0.00040349287047780445356 %)\n",
      "71 Vallue differ for target with dtype bool (652 / 2032254 diff, 0.00032082603847747377841 %)\n",
      "72 Vallue differ for target with dtype bool (535 / 2032254 diff, 0.00026325449476295780058 %)\n",
      "73 Vallue differ for target with dtype bool (138 / 2032254 diff, 0.00006790489771455733255 %)\n",
      "74 Value are equals !\n",
      "75 Value are equals !\n",
      "76 Value are equals !\n",
      "77 Value are equals !\n",
      "78 Value are equals !\n",
      "79 Value are equals !\n",
      "80 Value are equals !\n",
      "81 Value are equals !\n",
      "82 Value are equals !\n",
      "83 Vallue differ for target with dtype bool (207 / 2032254 diff, 0.00010185734657183600561 %)\n",
      "84 Vallue differ for target with dtype bool (3372 / 2032254 diff, 0.00165924141372092263935 %)\n",
      "85 Vallue differ for target with dtype bool (13280 / 2032254 diff, 0.00653461624383566263424 %)\n",
      "86 Vallue differ for target with dtype bool (3069 / 2032254 diff, 0.00151014587743461201064 %)\n",
      "87 Vallue differ for target with dtype bool (4691 / 2032254 diff, 0.00230827445781875677938 %)\n",
      "88 Value are equals !\n",
      "89 Value are equals !\n",
      "90 Value are equals !\n",
      "91 Value are equals !\n",
      "92 Value are equals !\n",
      "93 Vallue differ for target with dtype bool (7878 / 2032254 diff, 0.00387648394344407721396 %)\n",
      "94 Value are equals !\n",
      "95 Value are equals !\n",
      "96 Value are equals !\n",
      "97 Value are equals !\n",
      "98 Value are equals !\n",
      "99 Value are equals !\n",
      "100 Vallue differ for target with dtype bool (8596 / 2032254 diff, 0.00422978623735025243552 %)\n",
      "101 Vallue differ for target with dtype bool (9193 / 2032254 diff, 0.00452354872963714200323 %)\n",
      "102 Value are equals !\n",
      "103 Value are equals !\n",
      "104 Value are equals !\n"
     ]
    }
   ],
   "source": [
    "from utils import is_equal\n",
    "from utils.med_utils.label_utils import TOTALSEGMENTATOR_LABELS\n",
    "\n",
    "subj_id = 's0003'\n",
    "path    = '/storage/Totalsegmentator_dataset/{}/ct.nii.gz'.format(subj_id)\n",
    "\n",
    "set_level('time')\n",
    "\n",
    "out     = model.predict(\n",
    "    path, friends = models[1:], mapping = TOTALSEGMENTATOR_LABELS, save = False, verbose = True, win_len = (128, 128, 128)\n",
    ")\n",
    "orig, _ = load_medical_data(\n",
    "    'evaluations/totalsegmentator_fullres/segmentations/{}.nii.gz'.format(subj_id), dtype = tf.int32, use_sparse = False, is_one_hot = False\n",
    ")\n",
    "\n",
    "pred = out[0][1]['segmentation'].numpy().astype(np.int32)\n",
    "\n",
    "print(is_equal(orig, pred)[1])\n",
    "for i in range(len(out[0][1]['labels'])):\n",
    "    print(i, is_equal(orig == i, pred == i)[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fadcdc01-8480-405d-8fab-537fe4a9fe12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values differ (233110 / 2391680 diff, 0.097%) : max 101 - mean 7.757954241370083 - min 0\n",
      "Values differ (233110 / 2391680 diff, 0.097%) : max 101 - mean 7.757954241370083 - min 0\n",
      "0 Vallue differ for target with dtype bool (233110 / 2391680 diff, 0.09746705244848809235325 %)\n",
      "1 Value are equals !\n",
      "2 Value are equals !\n",
      "3 Value are equals !\n",
      "4 Value are equals !\n",
      "5 Value are equals !\n",
      "6 Value are equals !\n",
      "7 Value are equals !\n",
      "8 Value are equals !\n",
      "9 Value are equals !\n",
      "10 Value are equals !\n",
      "11 Value are equals !\n",
      "12 Value are equals !\n",
      "13 Vallue differ for target with dtype bool (994 / 2391680 diff, 0.00041560743912229063373 %)\n",
      "14 Value are equals !\n",
      "15 Vallue differ for target with dtype bool (1157 / 2391680 diff, 0.00048376036928017123731 %)\n",
      "16 Value are equals !\n",
      "17 Value are equals !\n",
      "18 Value are equals !\n",
      "19 Value are equals !\n",
      "20 Value are equals !\n",
      "21 Value are equals !\n",
      "22 Value are equals !\n",
      "23 Value are equals !\n",
      "24 Value are equals !\n",
      "25 Value are equals !\n",
      "26 Value are equals !\n",
      "27 Value are equals !\n",
      "28 Value are equals !\n",
      "29 Value are equals !\n",
      "30 Value are equals !\n",
      "31 Value are equals !\n",
      "32 Value are equals !\n",
      "33 Vallue differ for target with dtype bool (522 / 2391680 diff, 0.00021825662295959325854 %)\n",
      "34 Vallue differ for target with dtype bool (3941 / 2391680 diff, 0.00164779569173133526479 %)\n",
      "35 Vallue differ for target with dtype bool (4367 / 2391680 diff, 0.00182591316564088834706 %)\n",
      "36 Vallue differ for target with dtype bool (3093 / 2391680 diff, 0.00129323320845598074488 %)\n",
      "37 Vallue differ for target with dtype bool (2738 / 2391680 diff, 0.00114480198019801973457 %)\n",
      "38 Vallue differ for target with dtype bool (2696 / 2391680 diff, 0.00112724110248862714051 %)\n",
      "39 Vallue differ for target with dtype bool (2776 / 2391680 diff, 0.00116069039336366068398 %)\n",
      "40 Vallue differ for target with dtype bool (4317 / 2391680 diff, 0.00180500735884399246371 %)\n",
      "41 Vallue differ for target with dtype bool (3982 / 2391680 diff, 0.00166493845330478989347 %)\n",
      "42 Vallue differ for target with dtype bool (953 / 2391680 diff, 0.00039846467754883595084 %)\n",
      "43 Vallue differ for target with dtype bool (3631 / 2391680 diff, 0.00151817968959058074464 %)\n",
      "44 Value are equals !\n",
      "45 Value are equals !\n",
      "46 Value are equals !\n",
      "47 Value are equals !\n",
      "48 Value are equals !\n",
      "49 Value are equals !\n",
      "50 Vallue differ for target with dtype bool (24025 / 2391680 diff, 0.01004524016590848355113 %)\n",
      "51 Value are equals !\n",
      "52 Value are equals !\n",
      "53 Value are equals !\n",
      "54 Value are equals !\n",
      "55 Value are equals !\n",
      "56 Value are equals !\n",
      "57 Value are equals !\n",
      "58 Vallue differ for target with dtype bool (846 / 2391680 diff, 0.00035372625100347875179 %)\n",
      "59 Vallue differ for target with dtype bool (925 / 2391680 diff, 0.00038675742574257427567 %)\n",
      "60 Value are equals !\n",
      "61 Value are equals !\n",
      "62 Value are equals !\n",
      "63 Value are equals !\n",
      "64 Value are equals !\n",
      "65 Value are equals !\n",
      "66 Value are equals !\n",
      "67 Value are equals !\n",
      "68 Value are equals !\n",
      "69 Value are equals !\n",
      "70 Vallue differ for target with dtype bool (892 / 2391680 diff, 0.00037295959325662293628 %)\n",
      "71 Vallue differ for target with dtype bool (778 / 2391680 diff, 0.00032529435375970030490 %)\n",
      "72 Value are equals !\n",
      "73 Value are equals !\n",
      "74 Value are equals !\n",
      "75 Value are equals !\n",
      "76 Value are equals !\n",
      "77 Value are equals !\n",
      "78 Value are equals !\n",
      "79 Value are equals !\n",
      "80 Value are equals !\n",
      "81 Value are equals !\n",
      "82 Value are equals !\n",
      "83 Value are equals !\n",
      "84 Vallue differ for target with dtype bool (4604 / 2391680 diff, 0.00192500668985817508135 %)\n",
      "85 Vallue differ for target with dtype bool (3240 / 2391680 diff, 0.00135469628043885471566 %)\n",
      "86 Vallue differ for target with dtype bool (3342 / 2391680 diff, 0.00139734412630452241311 %)\n",
      "87 Vallue differ for target with dtype bool (3181 / 2391680 diff, 0.00133002742841851757764 %)\n",
      "88 Value are equals !\n",
      "89 Value are equals !\n",
      "90 Value are equals !\n",
      "91 Value are equals !\n",
      "92 Value are equals !\n",
      "93 Vallue differ for target with dtype bool (143113 / 2391680 diff, 0.05983785456248327805362 %)\n",
      "94 Value are equals !\n",
      "95 Value are equals !\n",
      "96 Value are equals !\n",
      "97 Value are equals !\n",
      "98 Value are equals !\n",
      "99 Value are equals !\n",
      "100 Vallue differ for target with dtype bool (6337 / 2391680 diff, 0.00264960195343858688835 %)\n",
      "101 Vallue differ for target with dtype bool (6660 / 2391680 diff, 0.00278465346534653452465 %)\n",
      "102 Value are equals !\n",
      "103 Value are equals !\n",
      "104 Value are equals !\n"
     ]
    }
   ],
   "source": [
    "from utils import is_equal\n",
    "from utils.med_utils.label_utils import TOTALSEGMENTATOR_LABELS\n",
    "\n",
    "pred = out[0][1]['segmentation'].numpy().astype(np.int32)\n",
    "\n",
    "print(is_equal(orig, pred)[1])\n",
    "pred = rearrange_labels(pred, labels = [''] + out[0][1]['labels'][1:], mapping = TOTALSEGMENTATOR_LABELS)\n",
    "\n",
    "print(is_equal(orig, pred)[1])\n",
    "for i in range(105):\n",
    "    print(i, is_equal(orig == i, pred == i)[1])\n",
    "\n",
    "#print(is_equal(orig, out[0][1]['segmentation'].numpy().astype(np.int32)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e39a5c0-d14c-4183-a6a5-5d9c5c7426b9",
   "metadata": {},
   "source": [
    "### Test low resolution (with resampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3f599b7-1458-4719-9de5-f90bfcc1d8a6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model restoration...\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n",
      "The given value for groups will be overwritten.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 10:35:55.537667: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing submodel : `model` !\n",
      "Successfully restored model from pretrained_models/test2_task_256/saving/model.json !\n",
      "Model test2_task_256 initialized successfully !\n",
      "\n",
      "========== test2_task_256 ==========\n",
      "Sub model model\n",
      "- Inputs \t: (None, None, None, None, 1)\n",
      "- Outputs \t: (None, None, None, None, 105)\n",
      "- Number of layers \t: 122\n",
      "- Number of parameters \t: 30.480 Millions\n",
      "- Model not compiled\n",
      "\n",
      "Transfer-learning from : Task256_TotalSegmentator_3mm_1139subj\n",
      "Already trained on 0 epochs (0 steps)\n",
      "\n",
      "- Image size : (None, None, 1)\n",
      "- Resize config : {'interpolation': 'bicubic', 'preserve_aspect_ratio': False}\n",
      "- Normalization style : OrderedDict([('local_props', None), ('median', 33.0), ('mean', -164.82027), ('sd', 470.82532), ('mn', -8221.0), ('mx', 17985.0), ('percentile_99_5', 1099.0), ('percentile_00_5', -1008.0)])\n",
      "- Labels (n = 105) : [None, 'spleen', 'kidney_right', 'kidney_left', 'gallbladder', 'liver', 'stomach', 'aorta', 'inferior_vena_cava', 'portal_vein_and_splenic_vein', 'pancreas', 'adrenal_gland_right', 'adrenal_gland_left', 'lung_upper_lobe_left', 'lung_lower_lobe_left', 'lung_upper_lobe_right', 'lung_middle_lobe_right', 'lung_lower_lobe_right', 'vertebrae_L5', 'vertebrae_L4', 'vertebrae_L3', 'vertebrae_L2', 'vertebrae_L1', 'vertebrae_T12', 'vertebrae_T11', 'vertebrae_T10', 'vertebrae_T9', 'vertebrae_T8', 'vertebrae_T7', 'vertebrae_T6', 'vertebrae_T5', 'vertebrae_T4', 'vertebrae_T3', 'vertebrae_T2', 'vertebrae_T1', 'vertebrae_C7', 'vertebrae_C6', 'vertebrae_C5', 'vertebrae_C4', 'vertebrae_C3', 'vertebrae_C2', 'vertebrae_C1', 'esophagus', 'trachea', 'heart_myocardium', 'heart_atrium_left', 'heart_ventricle_left', 'heart_atrium_right', 'heart_ventricle_right', 'pulmonary_artery', 'brain', 'iliac_artery_left', 'iliac_artery_right', 'iliac_vena_left', 'iliac_vena_right', 'small_bowel', 'duodenum', 'colon', 'rib_left_1', 'rib_left_2', 'rib_left_3', 'rib_left_4', 'rib_left_5', 'rib_left_6', 'rib_left_7', 'rib_left_8', 'rib_left_9', 'rib_left_10', 'rib_left_11', 'rib_left_12', 'rib_right_1', 'rib_right_2', 'rib_right_3', 'rib_right_4', 'rib_right_5', 'rib_right_6', 'rib_right_7', 'rib_right_8', 'rib_right_9', 'rib_right_10', 'rib_right_11', 'rib_right_12', 'humerus_left', 'humerus_right', 'scapula_left', 'scapula_right', 'clavicula_left', 'clavicula_right', 'femur_left', 'femur_right', 'hip_left', 'hip_right', 'sacrum', 'face', 'gluteus_maximus_left', 'gluteus_maximus_right', 'gluteus_medius_left', 'gluteus_medius_right', 'gluteus_minimus_left', 'gluteus_minimus_right', 'autochthon_left', 'autochthon_right', 'iliopsoas_left', 'iliopsoas_right', 'urinary_bladder']\n",
      "- Voxel dims : [3. 3. 3.]\n",
      "- # frames (axis = 2) : variable\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from models.detection import med_unet, med_unet_segmentator\n",
    "\n",
    "model = med_unet_segmentator.MedUNetSegmentator(\n",
    "    nom = 'test2_task_{}'.format(pretrained_task), pretrained = pretrained_name, labels = []\n",
    ")\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dff66fa8-2697-4f28-8469-6b53729e6095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using preprocessor GenericPreprocessor\n",
      "before crop: (1, 213, 188, 249) after crop: (1, 213, 188, 249) spacing: [1.49999988 1.5        1.49999988] \n",
      "\n",
      "no separate z, order 3\n",
      "no separate z, order 1\n",
      "before: {'spacing': array([1.49999988, 1.5       , 1.49999988]), 'spacing_transposed': array([1.49999988, 1.5       , 1.49999988]), 'data.shape (data is transposed)': (1, 213, 188, 249)} \n",
      "after:  {'spacing': array([3., 3., 3.]), 'data.shape (data is resampled)': (1, 106, 94, 124)} \n",
      "\n",
      "(1, 106, 94, 124)\n",
      "preprocessing...\n",
      "using preprocessor GenericPreprocessor\n",
      "before crop: (1, 213, 188, 249) after crop: (1, 213, 188, 249) spacing: [1.49999988 1.5        1.49999988] \n",
      "\n",
      "no separate z, order 3\n",
      "no separate z, order 1\n",
      "before: {'spacing': array([1.49999988, 1.5       , 1.49999988]), 'spacing_transposed': array([1.49999988, 1.5       , 1.49999988]), 'data.shape (data is transposed)': (1, 213, 188, 249)} \n",
      "after:  {'spacing': array([3., 3., 3.]), 'data.shape (data is resampled)': (1, 106, 94, 124)} \n",
      "\n",
      "predicting...\n",
      "debug: mirroring False mirror_axes (0, 1, 2)\n",
      "step_size: 0.5\n",
      "do mirror: False\n",
      "data shape: (1, 106, 96, 124)\n",
      "patch size: [96 96 96]\n",
      "steps (x, y, and z): [[0, 10], [0], [0, 28]]\n",
      "number of tiles: 4\n",
      "computing Gaussian\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/.local/lib/python3.9/site-packages/torch/amp/autocast_mode.py:204: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction done\n",
      "done\n",
      "(105, 106, 94, 124)\n"
     ]
    }
   ],
   "source": [
    "path = '/storage/Totalsegmentator_dataset/s0001/ct.nii.gz'\n",
    "\n",
    "inp, seg, infos = trainer.preprocess_patient([path])\n",
    "print(inp.shape)\n",
    "out = trainer.preprocess_predict_nifti([path], save = False)\n",
    "print(out.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bec7e446-f0df-4ef1-8ee2-1fbee566a38f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values differ (1213053 / 1235536 diff, 0.982%) : max 0.22455573081970215 - mean 0.0034582193475216627 - min 0.0\n",
      "volume shape : (1, 106, 96, 124, 1) - steps : [array([ 0, 10], dtype=int32), [0], array([ 0, 28], dtype=int32)]\n",
      "Values differ (6123 / 1235536 diff, 0.005%) : max 104 - mean 0.2951310200593103 - min 0\n",
      "CPU times: user 23.4 s, sys: 691 ms, total: 24.1 s\n",
      "Wall time: 6.34 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "tf_inp = model.get_input(path)\n",
    "\n",
    "print(is_equal(inp, tf_inp[tf.newaxis, :, :, :, 0])[1])\n",
    "\n",
    "tf_out = model.infer(tf_inp, win_len = trainer.patch_size)\n",
    "\n",
    "print(is_equal(np.transpose(out, [3, 2, 1, 0]).argmax(axis = -1).astype(np.int32), tf_out)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0754fda5-f668-40f8-a36d-879b7e4216fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values differ (1213053 / 1235536 diff, 0.982%) : max 0.22455573081970215 - mean 0.0034582193475216627 - min 0.0\n",
      "Values differ (10683 / 1235536 diff, 0.009%) : max 104 - mean 0.4412562644876394 - min 0\n",
      "CPU times: user 9.87 s, sys: 264 ms, total: 10.1 s\n",
      "Wall time: 3.33 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "tf_inp = model.get_input(path)\n",
    "\n",
    "print(is_equal(inp, tf_inp[tf.newaxis, :, :, :, 0])[1])\n",
    "\n",
    "tf_out = model.infer(tf_inp, win_len = 128)\n",
    "\n",
    "print(is_equal(np.transpose(out, [3, 2, 1, 0]).argmax(axis = -1).astype(np.int32), tf_out)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abdf5f5b-c21e-48de-aaa8-b3bd5510fabe",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Test convertion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c0b710-b99e-4d99-af0f-4e3b4f0c7646",
   "metadata": {},
   "source": [
    "### Convertion from file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4940bdc9-df25-41b2-9b07-599ff9dcce9d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Files analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b221467e-9962-4a36-8407-43fb406ad009",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['num_stages', 'num_modalities', 'modalities', 'normalization_schemes', 'dataset_properties', 'list_of_npz_files', 'original_spacings', 'original_sizes', 'preprocessed_data_folder', 'num_classes', 'all_classes', 'base_num_features', 'use_mask_for_norm', 'keep_only_largest_region', 'min_region_size_per_class', 'min_size_per_class', 'transpose_forward', 'transpose_backward', 'data_identifier', 'plans_per_stage', 'preprocessor_name', 'conv_per_stage'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'num_stages': 1,\n",
       " 'num_modalities': 1,\n",
       " 'modalities': {0: 'CT'},\n",
       " 'normalization_schemes': OrderedDict([(0, 'CT')]),\n",
       " 'dataset_properties': {'all_sizes': [(98, 70, 70),\n",
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       "   (122, 126, 126),\n",
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       "   (73, 82, 82),\n",
       "   (27, 160, 160),\n",
       "   (219, 120, 120),\n",
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       "   array([3.        , 3.        , 2.99999976]),\n",
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       "   array([3.00000024, 3.        , 3.00000024]),\n",
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       "   array([3.        , 3.00000024, 3.        ]),\n",
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       "   array([3.        , 3.        , 2.99999976]),\n",
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       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([2.99999976, 3.        , 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3.00000024, 3.        , 3.        ]),\n",
       "   array([3.        , 3.00000024, 3.        ]),\n",
       "   array([3.00000024, 3.        , 3.        ]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.00000024, 3.        , 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.00000024, 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   ...],\n",
       "  'all_classes': [1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   8,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   12,\n",
       "   13,\n",
       "   14,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20,\n",
       "   21,\n",
       "   22,\n",
       "   23,\n",
       "   24,\n",
       "   25,\n",
       "   26,\n",
       "   27,\n",
       "   28,\n",
       "   29,\n",
       "   30,\n",
       "   31,\n",
       "   32,\n",
       "   33,\n",
       "   34,\n",
       "   35,\n",
       "   36,\n",
       "   37,\n",
       "   38,\n",
       "   39,\n",
       "   40,\n",
       "   41,\n",
       "   42,\n",
       "   43,\n",
       "   44,\n",
       "   45,\n",
       "   46,\n",
       "   47,\n",
       "   48,\n",
       "   49,\n",
       "   50,\n",
       "   51,\n",
       "   52,\n",
       "   53,\n",
       "   54,\n",
       "   55,\n",
       "   56,\n",
       "   57,\n",
       "   58,\n",
       "   59,\n",
       "   60,\n",
       "   61,\n",
       "   62,\n",
       "   63,\n",
       "   64,\n",
       "   65,\n",
       "   66,\n",
       "   67,\n",
       "   68,\n",
       "   69,\n",
       "   70,\n",
       "   71,\n",
       "   72,\n",
       "   73,\n",
       "   74,\n",
       "   75,\n",
       "   76,\n",
       "   77,\n",
       "   78,\n",
       "   79,\n",
       "   80,\n",
       "   81,\n",
       "   82,\n",
       "   83,\n",
       "   84,\n",
       "   85,\n",
       "   86,\n",
       "   87,\n",
       "   88,\n",
       "   89,\n",
       "   90,\n",
       "   91,\n",
       "   92,\n",
       "   93,\n",
       "   94,\n",
       "   95,\n",
       "   96,\n",
       "   97,\n",
       "   98,\n",
       "   99,\n",
       "   100,\n",
       "   101,\n",
       "   102,\n",
       "   103,\n",
       "   104],\n",
       "  'modalities': {0: 'CT'},\n",
       "  'intensityproperties': OrderedDict([(0,\n",
       "                OrderedDict([('local_props', None),\n",
       "                             ('median', 33.0),\n",
       "                             ('mean', -164.82027),\n",
       "                             ('sd', 470.82532),\n",
       "                             ('mn', -8221.0),\n",
       "                             ('mx', 17985.0),\n",
       "                             ('percentile_99_5', 1099.0),\n",
       "                             ('percentile_00_5', -1008.0)]))]),\n",
       "  'size_reductions': None},\n",
       " 'list_of_npz_files': None,\n",
       " 'original_spacings': [array([3., 3., 3.]),\n",
       "  array([3.        , 2.99999976, 3.        ]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3.        , 3.        , 2.99999976]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3.        , 2.99999976, 3.        ]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3.        , 3.        , 3.00000024]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3.00000024, 3.        , 3.        ]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3.00000024, 3.00000024, 2.99999976]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3.        , 2.99999976, 3.        ]),\n",
       "  array([3., 3., 3.]),\n",
       "  array([3., 3., 3.]),\n",
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       "  array([2.99999976, 3.        , 3.        ]),\n",
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       "  array([3.        , 3.        , 3.00000024]),\n",
       "  array([3.        , 2.99999976, 3.        ]),\n",
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       "  array([3., 3., 3.]),\n",
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       "  array([3.        , 2.99999976, 2.99999976]),\n",
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       "  array([3., 3., 3.]),\n",
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       "  ...],\n",
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       "  (80, 53, 102),\n",
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       "  (53, 70, 59),\n",
       "  (219, 123, 123),\n",
       "  (43, 80, 71),\n",
       "  (149, 114, 114),\n",
       "  (169, 169, 118),\n",
       "  (158, 129, 129),\n",
       "  (265, 138, 138),\n",
       "  (58, 91, 91),\n",
       "  (56, 80, 80),\n",
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       "  (198, 127, 127),\n",
       "  (52, 72, 72),\n",
       "  (99, 116, 116),\n",
       "  (114, 128, 128),\n",
       "  (102, 122, 122),\n",
       "  (104, 109, 109),\n",
       "  (112, 124, 124),\n",
       "  (118, 104, 104),\n",
       "  (242, 166, 166),\n",
       "  (50, 57, 57),\n",
       "  (112, 90, 50),\n",
       "  (92, 104, 104),\n",
       "  (32, 121, 121),\n",
       "  (83, 99, 99),\n",
       "  (19, 166, 166),\n",
       "  (217, 101, 217),\n",
       "  (98, 64, 46),\n",
       "  (64, 53, 53),\n",
       "  (102, 130, 130),\n",
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       "  (87, 116, 116),\n",
       "  (142, 116, 116),\n",
       "  (38, 58, 62),\n",
       "  ...],\n",
       " 'preprocessed_data_folder': '/home/team/data/nnunet/preprocessed/Task256_TotalSegmentator_3mm_1139subj',\n",
       " 'num_classes': 104,\n",
       " 'all_classes': [1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5,\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  14,\n",
       "  15,\n",
       "  16,\n",
       "  17,\n",
       "  18,\n",
       "  19,\n",
       "  20,\n",
       "  21,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26,\n",
       "  27,\n",
       "  28,\n",
       "  29,\n",
       "  30,\n",
       "  31,\n",
       "  32,\n",
       "  33,\n",
       "  34,\n",
       "  35,\n",
       "  36,\n",
       "  37,\n",
       "  38,\n",
       "  39,\n",
       "  40,\n",
       "  41,\n",
       "  42,\n",
       "  43,\n",
       "  44,\n",
       "  45,\n",
       "  46,\n",
       "  47,\n",
       "  48,\n",
       "  49,\n",
       "  50,\n",
       "  51,\n",
       "  52,\n",
       "  53,\n",
       "  54,\n",
       "  55,\n",
       "  56,\n",
       "  57,\n",
       "  58,\n",
       "  59,\n",
       "  60,\n",
       "  61,\n",
       "  62,\n",
       "  63,\n",
       "  64,\n",
       "  65,\n",
       "  66,\n",
       "  67,\n",
       "  68,\n",
       "  69,\n",
       "  70,\n",
       "  71,\n",
       "  72,\n",
       "  73,\n",
       "  74,\n",
       "  75,\n",
       "  76,\n",
       "  77,\n",
       "  78,\n",
       "  79,\n",
       "  80,\n",
       "  81,\n",
       "  82,\n",
       "  83,\n",
       "  84,\n",
       "  85,\n",
       "  86,\n",
       "  87,\n",
       "  88,\n",
       "  89,\n",
       "  90,\n",
       "  91,\n",
       "  92,\n",
       "  93,\n",
       "  94,\n",
       "  95,\n",
       "  96,\n",
       "  97,\n",
       "  98,\n",
       "  99,\n",
       "  100,\n",
       "  101,\n",
       "  102,\n",
       "  103,\n",
       "  104],\n",
       " 'base_num_features': 32,\n",
       " 'use_mask_for_norm': OrderedDict([(0, False)]),\n",
       " 'keep_only_largest_region': None,\n",
       " 'min_region_size_per_class': None,\n",
       " 'min_size_per_class': None,\n",
       " 'transpose_forward': [0, 1, 2],\n",
       " 'transpose_backward': [0, 1, 2],\n",
       " 'data_identifier': 'nnUNetData_plans_v2.1',\n",
       " 'plans_per_stage': {0: {'batch_size': 2,\n",
       "   'num_pool_per_axis': [4, 4, 4],\n",
       "   'patch_size': array([96, 96, 96]),\n",
       "   'median_patient_size_in_voxels': array([114, 114, 120]),\n",
       "   'current_spacing': array([3., 3., 3.]),\n",
       "   'original_spacing': array([3., 3., 3.]),\n",
       "   'do_dummy_2D_data_aug': False,\n",
       "   'pool_op_kernel_sizes': [[2, 2, 2],\n",
       "    [2, 2, 2],\n",
       "    [2, 2, 2],\n",
       "    [2, 2, 2],\n",
       "    [1, 1, 1]],\n",
       "   'conv_kernel_sizes': [[3, 3, 3],\n",
       "    [3, 3, 3],\n",
       "    [3, 3, 3],\n",
       "    [3, 3, 3],\n",
       "    [3, 3, 3],\n",
       "    [3, 3, 3]]}},\n",
       " 'preprocessor_name': 'GenericPreprocessor',\n",
       " 'conv_per_stage': 2}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import load_data\n",
    "\n",
    "data = load_data(model_path / 'plans.pkl')\n",
    "print(data.keys())\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d45880dd-e14a-4d0a-a270-c84a4fe467f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(0,\n",
       "              OrderedDict([('local_props', None),\n",
       "                           ('median', 35.0),\n",
       "                           ('mean', -51.83935),\n",
       "                           ('sd', 345.06757),\n",
       "                           ('mn', -8686.0),\n",
       "                           ('mx', 23350.0),\n",
       "                           ('percentile_99_5', 803.0),\n",
       "                           ('percentile_00_5', -1021.0)]))])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import load_data\n",
    "\n",
    "data = load_data(model_path / 'fold_0/model_final_checkpoint.model.pkl')\n",
    "data['plans']['dataset_properties']['intensityproperties']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1352f34b-09ba-4f01-b474-fda392376973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['epoch', 'state_dict', 'optimizer_state_dict', 'lr_scheduler_state_dict', 'plot_stuff', 'best_stuff', 'amp_grad_scaler'])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_dict = torch.load(model_path / 'fold_0/model_final_checkpoint.model', map_location = 'cpu')\n",
    "state_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e256d230-30b7-4af5-acdd-e9b5129e71c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['debug.json', 'model_final_checkpoint.model', 'model_final_checkpoint.model.pkl', 'progress.png']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(model_path / 'fold_0'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9c3c9c-ed00-456d-8b89-479e4c1f3cd3",
   "metadata": {},
   "source": [
    "#### Convertion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d7d1a6b-7048-4954-b162-d9599f69991b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pt_model = trainer.network.eval().cpu()\n",
    "\n",
    "#print(pt_model)\n",
    "#print_vars(pt_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2bfff66a-1ffe-4306-930b-faec1c38a50c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading files for model Task256_TotalSegmentator_3mm_1139subj at : http://94.16.105.223/static/Task256_TotalSegmentator_3mm_1139subj.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 219M/219M [00:02<00:00, 111MiB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-22 13:55:03.630214: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available folds (fold_0 used) : ['fold_0']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_localization': 40, 'conv_blocks_context': 48, 'tu': 5, 'seg_outputs': 5}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n",
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_context': 48, 'tu': 5, 'conv_blocks_localization': 40, 'seg_outputs': 5}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights transfered successfully !\n"
     ]
    }
   ],
   "source": [
    "importlib.reload(totalsegmentator_arch)\n",
    "\n",
    "tf_model = totalsegmentator_arch.TotalSegmentator(\n",
    "    pretrained = pretrained_name, input_shape = (None, None, None, 1), final_activation = None, return_all_outputs = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b74e6ceb-c838-4888-8a8f-0324caeb14d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# outputs : 5\n",
      "Invalid items (4) :\n",
      "Item #0 : Values differ (266188 / 92897280 diff, 0.003%) : max 0.004673004150390625 - mean 0.00014701350301038474 - min 0.0\n",
      "Item #1 : Values differ (92752 / 11612160 diff, 0.008%) : max 0.00908660888671875 - mean 0.00018541066674515605 - min 0.0\n",
      "Item #2 : Values differ (2802 / 1451520 diff, 0.002%) : max 0.00669097900390625 - mean 0.00012151803821325302 - min 0.0\n",
      "Item #3 : Values differ (8 / 181440 diff, 0.000%) : max 0.0013065338134765625 - mean 3.25112123391591e-05 - min 0.0\n",
      "# outputs : 5\n",
      "Invalid items (4) :\n",
      "Item #0 : Values differ (764772 / 185794560 diff, 0.004%) : max 0.00592803955078125 - mean 0.00015793004422448575 - min 0.0\n",
      "Item #1 : Values differ (234048 / 23224320 diff, 0.010%) : max 0.0106658935546875 - mean 0.00019896100275218487 - min 0.0\n",
      "Item #2 : Values differ (7011 / 2903040 diff, 0.002%) : max 0.009002685546875 - mean 0.00012932372919749469 - min 0.0\n",
      "Item #3 : Values differ (10 / 362880 diff, 0.000%) : max 0.00133514404296875 - mean 3.465968620730564e-05 - min 0.0\n",
      "# outputs : 5\n",
      "Invalid items (4) :\n",
      "Item #0 : Values differ (1359148 / 278691840 diff, 0.005%) : max 0.00551605224609375 - mean 0.00015834673831705004 - min 0.0\n",
      "Item #1 : Values differ (375780 / 34836480 diff, 0.011%) : max 0.0089263916015625 - mean 0.0001971131277969107 - min 0.0\n",
      "Item #2 : Values differ (10705 / 4354560 diff, 0.002%) : max 0.0070953369140625 - mean 0.00012772450281772763 - min 0.0\n",
      "Item #3 : Values differ (17 / 544320 diff, 0.000%) : max 0.0013980865478515625 - mean 3.483640466583893e-05 - min 0.0\n"
     ]
    }
   ],
   "source": [
    "batch_size, (h, w, c) = 1, trainer.patch_size\n",
    "\n",
    "for i in range(3):\n",
    "    shape = (i + 1, h, w, c, 1)\n",
    "    inp   = np.random.normal(size = shape) * 10\n",
    "\n",
    "    pt_inp = torch.FloatTensor(np.transpose(inp, [0, 4, 1, 2, 3]))\n",
    "    tf_inp = tf.cast(inp, tf.float32)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        pt_out = pt_model(pt_inp)\n",
    "        pt_out = pt_out.numpy() if not isinstance(pt_out, (list, tuple)) else [out.numpy() for out in pt_out]\n",
    "        if isinstance(pt_out, list):\n",
    "            pt_out = [np.transpose(out, [0, 2, 3, 4, 1]) for out in pt_out]\n",
    "        else:\n",
    "            pt_out = np.transpose(pt_out, [0, 2, 3, 4, 1])\n",
    "\n",
    "    tf_out = tf_model(tf_inp, training = False)\n",
    "\n",
    "    print('# outputs : {}'.format(len(tf_out)))\n",
    "    print(is_equal(pt_out, tf_out, max_err = 1e-3)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edfccc49-bc7e-4dea-9f46-ccc642034b15",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Full convertion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84b02d8e-afbd-4410-b40f-3163291fd391",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generic_UNet(\n",
      "  (conv_blocks_localization): ModuleList(\n",
      "    (0): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (1): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (2): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (3): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (4): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(64, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (conv_blocks_context): ModuleList(\n",
      "    (0): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(1, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (1): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (2): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (3): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (4): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (5): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (td): ModuleList()\n",
      "  (tu): ModuleList(\n",
      "    (0): ConvTranspose3d(320, 320, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "    (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "    (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "    (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "    (4): ConvTranspose3d(64, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
      "  )\n",
      "  (seg_outputs): ModuleList(\n",
      "    (0): Conv3d(320, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "    (1): Conv3d(256, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "    (2): Conv3d(128, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "    (3): Conv3d(64, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "    (4): Conv3d(32, 19, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(trainer.network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0518a4c4-eb13-4ac3-a145-db748f0053a7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pt_model = trainer.network.eval().cpu()\n",
    "\n",
    "#print(pt_model)\n",
    "#print_vars(pt_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32fd1405-ee43-4894-ace7-4e293930755b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-21 10:02:33.478828: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "folds is None so we will automatically look for output folders (not using 'all'!)\n",
      "found the following folds:  ['/etinfo/users2/qlanglois/.totalsegmentator/nnunet/results/nnUNet/3d_fullres/Task253_TotalSegmentator_part3_cardiac_1139subj/nnUNetTrainerV2_ep4000_nomirror__nnUNetPlansv2.1/fold_0']\n",
      "using the following model files:  ['/etinfo/users2/qlanglois/.totalsegmentator/nnunet/results/nnUNet/3d_fullres/Task253_TotalSegmentator_part3_cardiac_1139subj/nnUNetTrainerV2_ep4000_nomirror__nnUNetPlansv2.1/fold_0/model_final_checkpoint.model']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_localization': 40, 'conv_blocks_context': 48, 'tu': 5, 'seg_outputs': 5}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n",
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_context': 48, 'tu': 5, 'conv_blocks_localization': 40, 'seg_outputs': 5}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights transfered successfully !\n",
      "Weights transfered successfully !\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>layer</th>\n",
       "      <th>shape</th>\n",
       "      <th>Map layer</th>\n",
       "      <th>Map shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>conv_blocks_context/0/blocks/0/conv/kernel:0</th>\n",
       "      <td>conv_blocks_context/0/blocks/0/conv</td>\n",
       "      <td>(3, 3, 3, 1, 32)</td>\n",
       "      <td>conv_blocks_context/0/blocks/0/conv</td>\n",
       "      <td>(3, 3, 3, 1, 32)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conv_blocks_context/0/blocks/0/conv/bias:0</th>\n",
       "      <td>conv_blocks_context/0/blocks/0/conv</td>\n",
       "      <td>(32,)</td>\n",
       "      <td>conv_blocks_context/0/blocks/0/conv</td>\n",
       "      <td>(32,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conv_blocks_context/0/blocks/0/norm/gamma:0</th>\n",
       "      <td>conv_blocks_context/0/blocks/0/norm</td>\n",
       "      <td>(32,)</td>\n",
       "      <td>conv_blocks_context/0/blocks/0/instnorm</td>\n",
       "      <td>(32,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conv_blocks_context/0/blocks/0/norm/beta:0</th>\n",
       "      <td>conv_blocks_context/0/blocks/0/norm</td>\n",
       "      <td>(32,)</td>\n",
       "      <td>conv_blocks_context/0/blocks/0/instnorm</td>\n",
       "      <td>(32,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conv_blocks_context/0/blocks/1/conv/kernel:0</th>\n",
       "      <td>conv_blocks_context/0/blocks/1/conv</td>\n",
       "      <td>(3, 3, 3, 32, 32)</td>\n",
       "      <td>conv_blocks_context/0/blocks/1/conv</td>\n",
       "      <td>(3, 3, 3, 32, 32)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seg_outputs/4/kernel:0</th>\n",
       "      <td>seg_outputs/4</td>\n",
       "      <td>(1, 1, 1, 32, 19)</td>\n",
       "      <td>seg_outputs/4</td>\n",
       "      <td>(1, 1, 1, 32, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seg_outputs/3/kernel:0</th>\n",
       "      <td>seg_outputs/3</td>\n",
       "      <td>(1, 1, 1, 64, 19)</td>\n",
       "      <td>seg_outputs/3</td>\n",
       "      <td>(1, 1, 1, 64, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seg_outputs/2/kernel:0</th>\n",
       "      <td>seg_outputs/2</td>\n",
       "      <td>(1, 1, 1, 128, 19)</td>\n",
       "      <td>seg_outputs/2</td>\n",
       "      <td>(1, 1, 1, 128, 19)</td>\n",
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       "    <tr>\n",
       "      <th>seg_outputs/1/kernel:0</th>\n",
       "      <td>seg_outputs/1</td>\n",
       "      <td>(1, 1, 1, 256, 19)</td>\n",
       "      <td>seg_outputs/1</td>\n",
       "      <td>(1, 1, 1, 256, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seg_outputs/0/kernel:0</th>\n",
       "      <td>seg_outputs/0</td>\n",
       "      <td>(1, 1, 1, 320, 19)</td>\n",
       "      <td>seg_outputs/0</td>\n",
       "      <td>(1, 1, 1, 320, 19)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                            layer  \\\n",
       "conv_blocks_context/0/blocks/0/conv/kernel:0  conv_blocks_context/0/blocks/0/conv   \n",
       "conv_blocks_context/0/blocks/0/conv/bias:0    conv_blocks_context/0/blocks/0/conv   \n",
       "conv_blocks_context/0/blocks/0/norm/gamma:0   conv_blocks_context/0/blocks/0/norm   \n",
       "conv_blocks_context/0/blocks/0/norm/beta:0    conv_blocks_context/0/blocks/0/norm   \n",
       "conv_blocks_context/0/blocks/1/conv/kernel:0  conv_blocks_context/0/blocks/1/conv   \n",
       "...                                                                           ...   \n",
       "seg_outputs/4/kernel:0                                              seg_outputs/4   \n",
       "seg_outputs/3/kernel:0                                              seg_outputs/3   \n",
       "seg_outputs/2/kernel:0                                              seg_outputs/2   \n",
       "seg_outputs/1/kernel:0                                              seg_outputs/1   \n",
       "seg_outputs/0/kernel:0                                              seg_outputs/0   \n",
       "\n",
       "                                                           shape  \\\n",
       "conv_blocks_context/0/blocks/0/conv/kernel:0    (3, 3, 3, 1, 32)   \n",
       "conv_blocks_context/0/blocks/0/conv/bias:0                 (32,)   \n",
       "conv_blocks_context/0/blocks/0/norm/gamma:0                (32,)   \n",
       "conv_blocks_context/0/blocks/0/norm/beta:0                 (32,)   \n",
       "conv_blocks_context/0/blocks/1/conv/kernel:0   (3, 3, 3, 32, 32)   \n",
       "...                                                          ...   \n",
       "seg_outputs/4/kernel:0                         (1, 1, 1, 32, 19)   \n",
       "seg_outputs/3/kernel:0                         (1, 1, 1, 64, 19)   \n",
       "seg_outputs/2/kernel:0                        (1, 1, 1, 128, 19)   \n",
       "seg_outputs/1/kernel:0                        (1, 1, 1, 256, 19)   \n",
       "seg_outputs/0/kernel:0                        (1, 1, 1, 320, 19)   \n",
       "\n",
       "                                                                            Map layer  \\\n",
       "conv_blocks_context/0/blocks/0/conv/kernel:0      conv_blocks_context/0/blocks/0/conv   \n",
       "conv_blocks_context/0/blocks/0/conv/bias:0        conv_blocks_context/0/blocks/0/conv   \n",
       "conv_blocks_context/0/blocks/0/norm/gamma:0   conv_blocks_context/0/blocks/0/instnorm   \n",
       "conv_blocks_context/0/blocks/0/norm/beta:0    conv_blocks_context/0/blocks/0/instnorm   \n",
       "conv_blocks_context/0/blocks/1/conv/kernel:0      conv_blocks_context/0/blocks/1/conv   \n",
       "...                                                                               ...   \n",
       "seg_outputs/4/kernel:0                                                  seg_outputs/4   \n",
       "seg_outputs/3/kernel:0                                                  seg_outputs/3   \n",
       "seg_outputs/2/kernel:0                                                  seg_outputs/2   \n",
       "seg_outputs/1/kernel:0                                                  seg_outputs/1   \n",
       "seg_outputs/0/kernel:0                                                  seg_outputs/0   \n",
       "\n",
       "                                                       Map shape  \n",
       "conv_blocks_context/0/blocks/0/conv/kernel:0    (3, 3, 3, 1, 32)  \n",
       "conv_blocks_context/0/blocks/0/conv/bias:0                 (32,)  \n",
       "conv_blocks_context/0/blocks/0/norm/gamma:0                (32,)  \n",
       "conv_blocks_context/0/blocks/0/norm/beta:0                 (32,)  \n",
       "conv_blocks_context/0/blocks/1/conv/kernel:0   (3, 3, 3, 32, 32)  \n",
       "...                                                          ...  \n",
       "seg_outputs/4/kernel:0                         (1, 1, 1, 32, 19)  \n",
       "seg_outputs/3/kernel:0                         (1, 1, 1, 64, 19)  \n",
       "seg_outputs/2/kernel:0                        (1, 1, 1, 128, 19)  \n",
       "seg_outputs/1/kernel:0                        (1, 1, 1, 256, 19)  \n",
       "seg_outputs/0/kernel:0                        (1, 1, 1, 320, 19)  \n",
       "\n",
       "[98 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(totalsegmentator_arch)\n",
    "\n",
    "strides = [1, 2, 2, 2, 2, 1] if '3mm' in str(model_path) else [1, 2, 2, 2, 2, 2]\n",
    "n_labels = len(trainer.classes) + 1\n",
    "tf_model = totalsegmentator_arch.TotalSegmentator(\n",
    "    pretrained = (pretrained_task, pretrained_name),\n",
    "    input_shape = (None, None, None, 1), output_dim = n_labels, strides = strides, final_activation = None, return_all_outputs = True\n",
    ")\n",
    "\n",
    "#print_vars(tf_model)\n",
    "\n",
    "name_based_partial_transfer_learning(tf_model, pt_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6035a572-03c2-4ba3-baa2-9d4517043d3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# outputs : 5\n",
      "Invalid items (4) :\n",
      "Item #0 : Values differ (670785 / 39845888 diff, 0.017%) : max 0.013088703155517578 - mean 0.0002474017674103379 - min 0.0\n",
      "Item #1 : Values differ (7583 / 4980736 diff, 0.002%) : max 0.0047016143798828125 - mean 0.00012537573638837785 - min 0.0\n",
      "Item #2 : Values differ (2838 / 622592 diff, 0.005%) : max 0.003932952880859375 - mean 0.00011502899724291638 - min 0.0\n",
      "Item #3 : Values differ (71 / 77824 diff, 0.001%) : max 0.001918792724609375 - mean 9.277729986933991e-05 - min 0.0\n",
      "# outputs : 5\n",
      "Invalid items (4) :\n",
      "Item #0 : Values differ (2096226 / 79691776 diff, 0.026%) : max 0.01634502410888672 - mean 0.00027300516376271844 - min 0.0\n",
      "Item #1 : Values differ (50805 / 9961472 diff, 0.005%) : max 0.004910469055175781 - mean 0.00014871329767629504 - min 0.0\n",
      "Item #2 : Values differ (7795 / 1245184 diff, 0.006%) : max 0.007015228271484375 - mean 0.00013095163740217686 - min 0.0\n",
      "Item #3 : Values differ (396 / 155648 diff, 0.003%) : max 0.0027523040771484375 - mean 0.00010532987653277814 - min 0.0\n",
      "# outputs : 5\n",
      "Invalid items (4) :\n",
      "Item #0 : Values differ (2906105 / 119537664 diff, 0.024%) : max 0.01155853271484375 - mean 0.0002736396563705057 - min 0.0\n",
      "Item #1 : Values differ (49989 / 14942208 diff, 0.003%) : max 0.004199028015136719 - mean 0.0001473160955356434 - min 0.0\n",
      "Item #2 : Values differ (11060 / 1867776 diff, 0.006%) : max 0.0052623748779296875 - mean 0.00013004154607187957 - min 0.0\n",
      "Item #3 : Values differ (583 / 233472 diff, 0.002%) : max 0.0035228729248046875 - mean 0.00010454939911141992 - min 0.0\n"
     ]
    }
   ],
   "source": [
    "batch_size, (h, w, c) = 1, trainer.patch_size\n",
    "\n",
    "for i in range(3):\n",
    "    shape = (i + 1, h, w, c, 1)\n",
    "    inp   = np.random.normal(size = shape) * 10\n",
    "\n",
    "    pt_inp = torch.FloatTensor(np.transpose(inp, [0, 4, 1, 2, 3]))\n",
    "    tf_inp = tf.cast(inp, tf.float32)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        pt_out = pt_model(pt_inp)\n",
    "        pt_out = pt_out.numpy() if not isinstance(pt_out, (list, tuple)) else [out.numpy() for out in pt_out]\n",
    "        if isinstance(pt_out, list):\n",
    "            pt_out = [np.transpose(out, [0, 2, 3, 4, 1]) for out in pt_out]\n",
    "        else:\n",
    "            pt_out = np.transpose(pt_out, [0, 2, 3, 4, 1])\n",
    "\n",
    "    tf_out = tf_model(tf_inp, training = False)\n",
    "\n",
    "    print('# outputs : {}'.format(len(tf_out)))\n",
    "    print(is_equal(pt_out, tf_out, max_err = 1e-3)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72502700-78fa-4b14-9437-da4388730941",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Encoder convertion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fc01f9fe-4a31-4537-b92c-2f2c293e4d2b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModuleList(\n",
      "  (0): StackedConvLayers(\n",
      "    (blocks): Sequential(\n",
      "      (0): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(1, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "      (1): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (1): StackedConvLayers(\n",
      "    (blocks): Sequential(\n",
      "      (0): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "      (1): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (2): StackedConvLayers(\n",
      "    (blocks): Sequential(\n",
      "      (0): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "      (1): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (3): StackedConvLayers(\n",
      "    (blocks): Sequential(\n",
      "      (0): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "      (1): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (4): StackedConvLayers(\n",
      "    (blocks): Sequential(\n",
      "      (0): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "      (1): ConvDropoutNormNonlin(\n",
      "        (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "        (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "        (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (5): Sequential(\n",
      "    (0): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (1): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      ")\n",
      "# variables : 48\n",
      "Name : 0.blocks.0.conv.weight\t- Shape : (32, 1, 3, 3, 3)\n",
      "Name : 0.blocks.0.conv.bias\t- Shape : (32,)\n",
      "Name : 0.blocks.0.instnorm.weight\t- Shape : (32,)\n",
      "Name : 0.blocks.0.instnorm.bias\t- Shape : (32,)\n",
      "Name : 0.blocks.1.conv.weight\t- Shape : (32, 32, 3, 3, 3)\n",
      "Name : 0.blocks.1.conv.bias\t- Shape : (32,)\n",
      "Name : 0.blocks.1.instnorm.weight\t- Shape : (32,)\n",
      "Name : 0.blocks.1.instnorm.bias\t- Shape : (32,)\n",
      "Name : 1.blocks.0.conv.weight\t- Shape : (64, 32, 3, 3, 3)\n",
      "Name : 1.blocks.0.conv.bias\t- Shape : (64,)\n",
      "Name : 1.blocks.0.instnorm.weight\t- Shape : (64,)\n",
      "Name : 1.blocks.0.instnorm.bias\t- Shape : (64,)\n",
      "Name : 1.blocks.1.conv.weight\t- Shape : (64, 64, 3, 3, 3)\n",
      "Name : 1.blocks.1.conv.bias\t- Shape : (64,)\n",
      "Name : 1.blocks.1.instnorm.weight\t- Shape : (64,)\n",
      "Name : 1.blocks.1.instnorm.bias\t- Shape : (64,)\n",
      "Name : 2.blocks.0.conv.weight\t- Shape : (128, 64, 3, 3, 3)\n",
      "Name : 2.blocks.0.conv.bias\t- Shape : (128,)\n",
      "Name : 2.blocks.0.instnorm.weight\t- Shape : (128,)\n",
      "Name : 2.blocks.0.instnorm.bias\t- Shape : (128,)\n",
      "Name : 2.blocks.1.conv.weight\t- Shape : (128, 128, 3, 3, 3)\n",
      "Name : 2.blocks.1.conv.bias\t- Shape : (128,)\n",
      "Name : 2.blocks.1.instnorm.weight\t- Shape : (128,)\n",
      "Name : 2.blocks.1.instnorm.bias\t- Shape : (128,)\n",
      "Name : 3.blocks.0.conv.weight\t- Shape : (256, 128, 3, 3, 3)\n",
      "Name : 3.blocks.0.conv.bias\t- Shape : (256,)\n",
      "Name : 3.blocks.0.instnorm.weight\t- Shape : (256,)\n",
      "Name : 3.blocks.0.instnorm.bias\t- Shape : (256,)\n",
      "Name : 3.blocks.1.conv.weight\t- Shape : (256, 256, 3, 3, 3)\n",
      "Name : 3.blocks.1.conv.bias\t- Shape : (256,)\n",
      "Name : 3.blocks.1.instnorm.weight\t- Shape : (256,)\n",
      "Name : 3.blocks.1.instnorm.bias\t- Shape : (256,)\n",
      "Name : 4.blocks.0.conv.weight\t- Shape : (320, 256, 3, 3, 3)\n",
      "Name : 4.blocks.0.conv.bias\t- Shape : (320,)\n",
      "Name : 4.blocks.0.instnorm.weight\t- Shape : (320,)\n",
      "Name : 4.blocks.0.instnorm.bias\t- Shape : (320,)\n",
      "Name : 4.blocks.1.conv.weight\t- Shape : (320, 320, 3, 3, 3)\n",
      "Name : 4.blocks.1.conv.bias\t- Shape : (320,)\n",
      "Name : 4.blocks.1.instnorm.weight\t- Shape : (320,)\n",
      "Name : 4.blocks.1.instnorm.bias\t- Shape : (320,)\n",
      "Name : 5.0.blocks.0.conv.weight\t- Shape : (320, 320, 3, 3, 3)\n",
      "Name : 5.0.blocks.0.conv.bias\t- Shape : (320,)\n",
      "Name : 5.0.blocks.0.instnorm.weight\t- Shape : (320,)\n",
      "Name : 5.0.blocks.0.instnorm.bias\t- Shape : (320,)\n",
      "Name : 5.1.blocks.0.conv.weight\t- Shape : (320, 320, 3, 3, 3)\n",
      "Name : 5.1.blocks.0.conv.bias\t- Shape : (320,)\n",
      "Name : 5.1.blocks.0.instnorm.weight\t- Shape : (320,)\n",
      "Name : 5.1.blocks.0.instnorm.bias\t- Shape : (320,)\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pt_model = trainer.network.conv_blocks_context.eval().cpu()\n",
    "\n",
    "print(pt_model)\n",
    "print_vars(pt_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b263ec2-0d15-4fa1-a4d1-797f41d0f845",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'0': 8, '1': 8, '2': 8, '3': 8, '4': 8, '5': 8}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_addons as tfa\n",
    "\n",
    "layers = []\n",
    "for i, filters in enumerate([32, 64, 128, 256, 320, 320]):\n",
    "    for j in range(2):\n",
    "        if '3mm' in pretrained_name:\n",
    "            strides = 2 if j == 0 and i not in (0, 5) else 1\n",
    "        else:\n",
    "            strides = 2 if j == 0 and i not in (0, ) else 1\n",
    "        layers.extend([\n",
    "            tf.keras.layers.ZeroPadding3D(((1, 1), (1, 1), (1, 1))),\n",
    "            tf.keras.layers.Conv3D(filters, kernel_size = 3, strides = strides, padding = 'valid'),\n",
    "            tfa.layers.InstanceNormalization(epsilon = 1e-5),\n",
    "            tf.keras.layers.LeakyReLU(0.01)\n",
    "        ])\n",
    "\n",
    "tf_model = tf.keras.Sequential(layers)\n",
    "tf_model.build([None, None, None, None, 1])\n",
    "\n",
    "tf_model.set_weights(get_pt_variables(pt_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c52dd02-f721-43cc-9222-587adf3f074d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values differ (33369 / 67108864 diff, 0.000%) : max 0.00019252300262451172 - mean 8.149160748871509e-06 - min 0.0\n",
      "Value are equals !\n",
      "Values differ (1 / 4194304 diff, 0.000%) : max 0.00010004639625549316 - mean 2.3301138298847945e-06 - min 0.0\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "\n",
      "Values differ (77356 / 134217728 diff, 0.001%) : max 0.00020194053649902344 - mean 7.687681318202522e-06 - min 0.0\n",
      "Value are equals !\n",
      "Values differ (2 / 8388608 diff, 0.000%) : max 0.00011172890663146973 - mean 2.248532155135763e-06 - min 0.0\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "\n",
      "Values differ (96562 / 201326592 diff, 0.000%) : max 0.00019562244415283203 - mean 7.67051551520126e-06 - min 0.0\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "\n",
      "Values differ (151427 / 268435456 diff, 0.001%) : max 0.0002008676528930664 - mean 7.791701136738993e-06 - min 0.0\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "\n",
      "Values differ (186351 / 335544320 diff, 0.001%) : max 0.0001983642578125 - mean 7.799285413057078e-06 - min 0.0\n",
      "Value are equals !\n",
      "Values differ (2 / 20971520 diff, 0.000%) : max 0.00010327249765396118 - mean 2.357145604037214e-06 - min 0.0\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "Value are equals !\n",
      "\n"
     ]
    }
   ],
   "source": [
    "batch_size, (h, w, c) = 1, trainer.patch_size\n",
    "\n",
    "for i in range(5):\n",
    "    shape = (i + 1, h, w, c, 1)\n",
    "    inp   = np.random.normal(size = shape) * 10\n",
    "\n",
    "    pt_inp = torch.FloatTensor(np.transpose(inp, [0, 4, 1, 2, 3]))\n",
    "    tf_inp = tf.cast(inp, tf.float32)\n",
    "\n",
    "    pt_out, tf_out = pt_inp, tf_inp\n",
    "    with torch.no_grad():\n",
    "        for i, block in enumerate(pt_model):\n",
    "            pt_out = block(pt_out)\n",
    "            \n",
    "            for l in tf_model.layers[i * 8 : (i + 1) * 8]:\n",
    "                tf_out = l(tf_out, training = False)\n",
    "\n",
    "            pt_out_np = pt_out.numpy() if not isinstance(pt_out, (list, tuple)) else [out.numpy() for out in pt_out]\n",
    "            pt_out_np = np.transpose(pt_out_np, [0, 2, 3, 4, 1])\n",
    "\n",
    "            print(is_equal(pt_out_np, tf_out, max_err = 1e-4)[1])\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb904d4d-06ac-419a-9589-6a6c4cf3d332",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### StackedConvLayers convertion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "646c830b-9f9d-4895-a1aa-6f1c3735cbc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StackedConvLayers(\n",
      "  (blocks): Sequential(\n",
      "    (0): ConvDropoutNormNonlin(\n",
      "      (conv): Conv3d(1, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "      (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "      (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "    )\n",
      "    (1): ConvDropoutNormNonlin(\n",
      "      (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "      (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "      (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "    )\n",
      "  )\n",
      ")\n",
      "# variables : 8\n",
      "Name : blocks.0.conv.weight\t- Shape : (32, 1, 3, 3, 3)\n",
      "Name : blocks.0.conv.bias\t- Shape : (32,)\n",
      "Name : blocks.0.instnorm.weight\t- Shape : (32,)\n",
      "Name : blocks.0.instnorm.bias\t- Shape : (32,)\n",
      "Name : blocks.1.conv.weight\t- Shape : (32, 32, 3, 3, 3)\n",
      "Name : blocks.1.conv.bias\t- Shape : (32,)\n",
      "Name : blocks.1.instnorm.weight\t- Shape : (32,)\n",
      "Name : blocks.1.instnorm.bias\t- Shape : (32,)\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pt_model = trainer.network.conv_blocks_context[0].eval().cpu()\n",
    "\n",
    "print(pt_model)\n",
    "print_vars(pt_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f40b0e71-ef88-4e1a-857f-90067e388fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv3D(32, kernel_size = 3, padding = 'same'),\n",
    "    tfa.layers.InstanceNormalization(epsilon = 1e-5),\n",
    "    tf.keras.layers.LeakyReLU(0.01),\n",
    "    tf.keras.layers.Conv3D(32, kernel_size = 3, padding = 'same'),\n",
    "    tfa.layers.InstanceNormalization(epsilon = 1e-5),\n",
    "    tf.keras.layers.LeakyReLU(0.01)\n",
    "])\n",
    "tf_model.build([None, None, None, None, 1])\n",
    "\n",
    "tf_model.set_weights(get_pt_variables(pt_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "91e49f93-1ceb-44f4-8fda-47253cb0aea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n"
     ]
    }
   ],
   "source": [
    "batch_size, (h, w, c) = 1, trainer.patch_size\n",
    "\n",
    "for i in range(5):\n",
    "    shape = (i + 1, h, w, c, 1)\n",
    "    inp   = np.random.normal(size = shape) * 10\n",
    "\n",
    "    pt_inp = torch.FloatTensor(np.transpose(inp, [0, 4, 1, 2, 3]))\n",
    "    tf_inp = tf.cast(inp, tf.float32)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        pt_out = pt_model(pt_inp)\n",
    "        pt_out = pt_out.numpy() if not isinstance(pt_out, (list, tuple)) else [out.numpy() for out in pt_out]\n",
    "        pt_out = np.transpose(pt_out, [0, 2, 3, 4, 1])\n",
    "\n",
    "    tf_out = tf_model(tf_inp, training = False)\n",
    "\n",
    "    print(is_equal(pt_out, tf_out, max_err = 1e-4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f04c8c-6711-490a-a5c1-9583b9b31466",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### ConvDropoutNormNonlin convertion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ea52a2a-0210-43f3-90ab-eb689e8c7f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConvDropoutNormNonlin(\n",
      "  (conv): Conv3d(1, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
      "  (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "  (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      ")\n",
      "# variables : 4\n",
      "Name : conv.weight\t- Shape : (32, 1, 3, 3, 3)\n",
      "Name : conv.bias\t- Shape : (32,)\n",
      "Name : instnorm.weight\t- Shape : (32,)\n",
      "Name : instnorm.bias\t- Shape : (32,)\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pt_model = trainer.network.conv_blocks_context[0].blocks[0].eval().cpu()\n",
    "\n",
    "print(pt_model)\n",
    "print_vars(pt_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f14af11-fca4-4559-aa20-0da00b92d6b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-12 09:15:46.507098: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv': 2, 'instnorm': 2}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n"
     ]
    }
   ],
   "source": [
    "tf_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv3D(32, kernel_size = 3, padding = 'same'),\n",
    "    tfa.layers.InstanceNormalization(epsilon = 1e-5),\n",
    "    tf.keras.layers.LeakyReLU(0.01)\n",
    "])\n",
    "tf_model.build([None, None, None, None, 1])\n",
    "\n",
    "tf_model.set_weights(get_pt_variables(pt_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1836a1a7-dc44-4d59-8e55-56b9f9867157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n"
     ]
    }
   ],
   "source": [
    "batch_size, (h, w, c) = 1, trainer.patch_size\n",
    "\n",
    "for i in range(5):\n",
    "    shape = (i + 1, h, w, c, 1)\n",
    "    inp   = np.random.normal(size = shape) * 10\n",
    "\n",
    "    pt_inp = torch.FloatTensor(np.transpose(inp, [0, 4, 1, 2, 3]))\n",
    "    tf_inp = tf.cast(inp, tf.float32)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        pt_out = pt_model(pt_inp)\n",
    "        pt_out = pt_out.numpy() if not isinstance(pt_out, (list, tuple)) else [out.numpy() for out in pt_out]\n",
    "        pt_out = np.transpose(pt_out, [0, 2, 3, 4, 1])\n",
    "\n",
    "    tf_out = tf_model(tf_inp, training = False)\n",
    "\n",
    "    print(is_equal(pt_out, tf_out, max_err = 1e-4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61c735f-672f-4791-a3ba-45d697a2c89f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Conv3D convertion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c9aa405-0f2e-4a45-b5d5-831c238b54d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-12 08:58:14.391571: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "pt_layer = trainer.network.conv_blocks_context[0].blocks[0].conv.eval().cpu()\n",
    "tf_layer = tf.keras.layers.Conv3D(32, kernel_size = 3, padding = 'same')\n",
    "tf_layer.build([None, None, None, 1])\n",
    "\n",
    "tf_layer.set_weights(get_pt_variables(pt_layer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9faf0c0e-4937-4e9b-9a77-efea0cb6958b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n",
      "(True, 'Value are equals !')\n"
     ]
    }
   ],
   "source": [
    "batch_size, (h, w, c) = 1, trainer.patch_size\n",
    "\n",
    "for i in range(5):\n",
    "    shape = (i + 1, h, w, c, 1)\n",
    "    inp   = np.random.normal(size = shape) * 10\n",
    "\n",
    "    pt_inp = torch.FloatTensor(np.transpose(inp, [0, 4, 1, 2, 3]))\n",
    "    tf_inp = tf.cast(inp, tf.float32)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        pt_out = pt_layer(pt_inp)\n",
    "        pt_out = pt_out.numpy() if not isinstance(pt_out, (list, tuple)) else [out.numpy() for out in pt_out]\n",
    "        pt_out = np.transpose(pt_out, [0, 2, 3, 4, 1])\n",
    "\n",
    "    tf_out = tf_layer(tf_inp, training = False)\n",
    "\n",
    "    print(is_equal(pt_out, tf_out, max_err = 1e-6))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b20e0cf6-6230-4cd9-a099-f63adce7d791",
   "metadata": {},
   "source": [
    "## Pre- / post- processing convertion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5decbe7a-0ff7-4736-8764-ac0274cae4a8",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<nnunet.training.network_training.custom_trainers.nnUNetTrainerV2_ep8000_nomirror.nnUNetTrainerV2_ep8000_nomirror object at 0x7f5ca453f490>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'fp16': True,\n",
       " 'amp_grad_scaler': <torch.cuda.amp.grad_scaler.GradScaler at 0x7f5af12b8040>,\n",
       " 'network': Generic_UNet(\n",
       "   (conv_blocks_localization): ModuleList(\n",
       "     (0): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (1): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (2): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (3): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (4): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(64, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "   )\n",
       "   (conv_blocks_context): ModuleList(\n",
       "     (0): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(1, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (1): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (2): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (3): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (4): StackedConvLayers(\n",
       "       (blocks): Sequential(\n",
       "         (0): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "         (1): ConvDropoutNormNonlin(\n",
       "           (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "           (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "           (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "     (5): Sequential(\n",
       "       (0): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "       (1): StackedConvLayers(\n",
       "         (blocks): Sequential(\n",
       "           (0): ConvDropoutNormNonlin(\n",
       "             (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "             (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
       "             (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "           )\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "   )\n",
       "   (td): ModuleList()\n",
       "   (tu): ModuleList(\n",
       "     (0): ConvTranspose3d(320, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "     (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "     (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "     (4): ConvTranspose3d(64, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "   )\n",
       "   (seg_outputs): ModuleList(\n",
       "     (0): Conv3d(320, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (1): Conv3d(256, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (2): Conv3d(128, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (3): Conv3d(64, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "     (4): Conv3d(32, 105, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n",
       "   )\n",
       " ),\n",
       " 'optimizer': SGD (\n",
       " Parameter Group 0\n",
       "     dampening: 0\n",
       "     differentiable: False\n",
       "     foreach: None\n",
       "     lr: 0.01\n",
       "     maximize: False\n",
       "     momentum: 0.99\n",
       "     nesterov: True\n",
       "     weight_decay: 3e-05\n",
       " ),\n",
       " 'lr_scheduler': None,\n",
       " 'tr_gen': None,\n",
       " 'val_gen': None,\n",
       " 'was_initialized': True,\n",
       " 'output_folder': '/etinfo/users2/qlanglois/.totalsegmentator/nnunet/results/nnUNet/3d_fullres/Task256_TotalSegmentator_3mm_1139subj/nnUNetTrainerV2_ep8000_nomirror__nnUNetPlansv2.1/fold_0',\n",
       " 'fold': 0,\n",
       " 'loss': MultipleOutputLoss2(\n",
       "   (loss): DC_and_CE_loss(\n",
       "     (ce): RobustCrossEntropyLoss()\n",
       "     (dc): SoftDiceLoss()\n",
       "   )\n",
       " ),\n",
       " 'dataset_directory': '/mnt/nvme/nnunet/preprocessed/Task256_TotalSegmentator_3mm_1139subj',\n",
       " 'dataset': None,\n",
       " 'dataset_tr': None,\n",
       " 'dataset_val': None,\n",
       " 'patience': 50,\n",
       " 'val_eval_criterion_alpha': 0.9,\n",
       " 'train_loss_MA_alpha': 0.93,\n",
       " 'train_loss_MA_eps': 0.0005,\n",
       " 'max_num_epochs': 8000,\n",
       " 'num_batches_per_epoch': 250,\n",
       " 'num_val_batches_per_epoch': 50,\n",
       " 'also_val_in_tr_mode': False,\n",
       " 'lr_threshold': 1e-06,\n",
       " 'val_eval_criterion_MA': None,\n",
       " 'train_loss_MA': None,\n",
       " 'best_val_eval_criterion_MA': 0.9161797181657702,\n",
       " 'best_MA_tr_loss_for_patience': -0.5271133326027676,\n",
       " 'best_epoch_based_on_MA_tr_loss': 7978,\n",
       " 'all_tr_losses': [1.3904114,\n",
       "  0.8039812,\n",
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       "  ...],\n",
       " 'epoch': 8000,\n",
       " 'log_file': None,\n",
       " 'deterministic': False,\n",
       " 'use_progress_bar': False,\n",
       " 'save_every': 50,\n",
       " 'save_latest_only': True,\n",
       " 'save_intermediate_checkpoints': True,\n",
       " 'save_best_checkpoint': True,\n",
       " 'save_final_checkpoint': True,\n",
       " 'unpack_data': True,\n",
       " 'init_args': ('/mnt/nvme/nnunet/preprocessed/Task256_TotalSegmentator_3mm_1139subj/nnUNetPlansv2.1_plans_3D.pkl',\n",
       "  0,\n",
       "  '/mnt/nvme/nnunet/results/nnUNet/3d_fullres/Task256_TotalSegmentator_3mm_1139subj/nnUNetTrainerV2_ep8000_nomirror__nnUNetPlansv2.1',\n",
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       " 'stage': 0,\n",
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       " 'plans_file': '/mnt/nvme/nnunet/preprocessed/Task256_TotalSegmentator_3mm_1139subj/nnUNetPlansv2.1_plans_3D.pkl',\n",
       " 'output_folder_base': '/etinfo/users2/qlanglois/.totalsegmentator/nnunet/results/nnUNet/3d_fullres/Task256_TotalSegmentator_3mm_1139subj/nnUNetTrainerV2_ep8000_nomirror__nnUNetPlansv2.1',\n",
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       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3.        , 3.        , 2.99999976]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
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       "    array([3.00000024, 3.        , 3.        ]),\n",
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       "    array([3.        , 3.00000024, 2.99999976]),\n",
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       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    array([3., 3., 3.]),\n",
       "    ...],\n",
       "   'all_classes': [1,\n",
       "    2,\n",
       "    3,\n",
       "    4,\n",
       "    5,\n",
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       "    76,\n",
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       "    89,\n",
       "    90,\n",
       "    91,\n",
       "    92,\n",
       "    93,\n",
       "    94,\n",
       "    95,\n",
       "    96,\n",
       "    97,\n",
       "    98,\n",
       "    99,\n",
       "    100,\n",
       "    101,\n",
       "    102,\n",
       "    103,\n",
       "    104],\n",
       "   'modalities': {0: 'CT'},\n",
       "   'intensityproperties': OrderedDict([(0,\n",
       "                 OrderedDict([('local_props', None),\n",
       "                              ('median', 33.0),\n",
       "                              ('mean', -164.82027),\n",
       "                              ('sd', 470.82532),\n",
       "                              ('mn', -8221.0),\n",
       "                              ('mx', 17985.0),\n",
       "                              ('percentile_99_5', 1099.0),\n",
       "                              ('percentile_00_5', -1008.0)]))]),\n",
       "   'size_reductions': None},\n",
       "  'list_of_npz_files': None,\n",
       "  'original_spacings': [array([3., 3., 3.]),\n",
       "   array([3.        , 2.99999976, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 2.99999976]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 2.99999976, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.00000024, 3.        , 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.00000024, 3.00000024, 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 2.99999976, 3.        ]),\n",
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       "   array([3.        , 2.99999976, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.00000024, 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3.        , 3.        , 2.99999976]),\n",
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       "   array([3.00000024, 3.        , 3.00000024]),\n",
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       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([2.99999976, 3.        , 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
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       "   array([3.00000024, 3.        , 3.        ]),\n",
       "   array([3.        , 3.00000024, 3.        ]),\n",
       "   array([3.00000024, 3.        , 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
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       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.00000024, 3.        , 3.        ]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.00000024, 2.99999976]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3.        , 3.        , 3.00000024]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   array([3., 3., 3.]),\n",
       "   ...],\n",
       "  'original_sizes': [(98, 70, 70),\n",
       "   (76, 76, 68),\n",
       "   (82, 158, 158),\n",
       "   (44, 114, 114),\n",
       "   (100, 82, 98),\n",
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       "   (38, 51, 51),\n",
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       "   (162, 130, 130),\n",
       "   (150, 124, 124),\n",
       "   (44, 70, 70),\n",
       "   (38, 54, 54),\n",
       "   (46, 144, 144),\n",
       "   (55, 96, 96),\n",
       "   (219, 130, 130),\n",
       "   (85, 95, 95),\n",
       "   (217, 123, 123),\n",
       "   (114, 102, 102),\n",
       "   (108, 110, 110),\n",
       "   (56, 46, 53),\n",
       "   (108, 110, 110),\n",
       "   (49, 154, 154),\n",
       "   (102, 106, 106),\n",
       "   (215, 106, 106),\n",
       "   (50, 82, 82),\n",
       "   (135, 98, 98),\n",
       "   (32, 116, 116),\n",
       "   (149, 134, 134),\n",
       "   (34, 97, 97),\n",
       "   (121, 94, 94),\n",
       "   (137, 108, 108),\n",
       "   (201, 108, 108),\n",
       "   (108, 156, 156),\n",
       "   (40, 67, 57),\n",
       "   (226, 142, 142),\n",
       "   (65, 124, 124),\n",
       "   (134, 110, 110),\n",
       "   (166, 124, 124),\n",
       "   (208, 128, 128),\n",
       "   (109, 140, 140),\n",
       "   (60, 82, 82),\n",
       "   (190, 110, 110),\n",
       "   (63, 48, 68),\n",
       "   (46, 64, 64),\n",
       "   (238, 138, 138),\n",
       "   (230, 130, 130),\n",
       "   (78, 132, 132),\n",
       "   (208, 136, 136),\n",
       "   (138, 132, 132),\n",
       "   (80, 53, 102),\n",
       "   (269, 146, 146),\n",
       "   (53, 70, 59),\n",
       "   (219, 123, 123),\n",
       "   (43, 80, 71),\n",
       "   (149, 114, 114),\n",
       "   (169, 169, 118),\n",
       "   (158, 129, 129),\n",
       "   (265, 138, 138),\n",
       "   (58, 91, 91),\n",
       "   (56, 80, 80),\n",
       "   (29, 132, 132),\n",
       "   (74, 70, 94),\n",
       "   (198, 127, 127),\n",
       "   (52, 72, 72),\n",
       "   (99, 116, 116),\n",
       "   (114, 128, 128),\n",
       "   (102, 122, 122),\n",
       "   (104, 109, 109),\n",
       "   (112, 124, 124),\n",
       "   (118, 104, 104),\n",
       "   (242, 166, 166),\n",
       "   (50, 57, 57),\n",
       "   (112, 90, 50),\n",
       "   (92, 104, 104),\n",
       "   (32, 121, 121),\n",
       "   (83, 99, 99),\n",
       "   (19, 166, 166),\n",
       "   (217, 101, 217),\n",
       "   (98, 64, 46),\n",
       "   (64, 53, 53),\n",
       "   (102, 130, 130),\n",
       "   (232, 142, 142),\n",
       "   (87, 116, 116),\n",
       "   (142, 116, 116),\n",
       "   (38, 58, 62),\n",
       "   ...],\n",
       "  'preprocessed_data_folder': '/home/team/data/nnunet/preprocessed/Task256_TotalSegmentator_3mm_1139subj',\n",
       "  'num_classes': 104,\n",
       "  'all_classes': [1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   8,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   12,\n",
       "   13,\n",
       "   14,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20,\n",
       "   21,\n",
       "   22,\n",
       "   23,\n",
       "   24,\n",
       "   25,\n",
       "   26,\n",
       "   27,\n",
       "   28,\n",
       "   29,\n",
       "   30,\n",
       "   31,\n",
       "   32,\n",
       "   33,\n",
       "   34,\n",
       "   35,\n",
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       "   39,\n",
       "   40,\n",
       "   41,\n",
       "   42,\n",
       "   43,\n",
       "   44,\n",
       "   45,\n",
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       "   47,\n",
       "   48,\n",
       "   49,\n",
       "   50,\n",
       "   51,\n",
       "   52,\n",
       "   53,\n",
       "   54,\n",
       "   55,\n",
       "   56,\n",
       "   57,\n",
       "   58,\n",
       "   59,\n",
       "   60,\n",
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       "   62,\n",
       "   63,\n",
       "   64,\n",
       "   65,\n",
       "   66,\n",
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       "   68,\n",
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       "   70,\n",
       "   71,\n",
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       "   73,\n",
       "   74,\n",
       "   75,\n",
       "   76,\n",
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       "   78,\n",
       "   79,\n",
       "   80,\n",
       "   81,\n",
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       "   84,\n",
       "   85,\n",
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       "   87,\n",
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       "   89,\n",
       "   90,\n",
       "   91,\n",
       "   92,\n",
       "   93,\n",
       "   94,\n",
       "   95,\n",
       "   96,\n",
       "   97,\n",
       "   98,\n",
       "   99,\n",
       "   100,\n",
       "   101,\n",
       "   102,\n",
       "   103,\n",
       "   104],\n",
       "  'base_num_features': 32,\n",
       "  'use_mask_for_norm': OrderedDict([(0, False)]),\n",
       "  'keep_only_largest_region': None,\n",
       "  'min_region_size_per_class': None,\n",
       "  'min_size_per_class': None,\n",
       "  'transpose_forward': [0, 1, 2],\n",
       "  'transpose_backward': [0, 1, 2],\n",
       "  'data_identifier': 'nnUNetData_plans_v2.1',\n",
       "  'plans_per_stage': {0: {'batch_size': 2,\n",
       "    'num_pool_per_axis': [4, 4, 4],\n",
       "    'patch_size': array([96, 96, 96]),\n",
       "    'median_patient_size_in_voxels': array([114, 114, 120]),\n",
       "    'current_spacing': array([3., 3., 3.]),\n",
       "    'original_spacing': array([3., 3., 3.]),\n",
       "    'do_dummy_2D_data_aug': False,\n",
       "    'pool_op_kernel_sizes': [[2, 2, 2],\n",
       "     [2, 2, 2],\n",
       "     [2, 2, 2],\n",
       "     [2, 2, 2],\n",
       "     [1, 1, 1]],\n",
       "    'conv_kernel_sizes': [[3, 3, 3],\n",
       "     [3, 3, 3],\n",
       "     [3, 3, 3],\n",
       "     [3, 3, 3],\n",
       "     [3, 3, 3],\n",
       "     [3, 3, 3]]}},\n",
       "  'preprocessor_name': 'GenericPreprocessor',\n",
       "  'conv_per_stage': 2},\n",
       " 'gt_niftis_folder': None,\n",
       " 'folder_with_preprocessed_data': '/mnt/nvme/nnunet/preprocessed/Task256_TotalSegmentator_3mm_1139subj/nnUNetData_plans_v2.1_stage0',\n",
       " 'dl_tr': None,\n",
       " 'dl_val': None,\n",
       " 'num_input_channels': 1,\n",
       " 'num_classes': 105,\n",
       " 'net_pool_per_axis': [4, 4, 4],\n",
       " 'patch_size': array([96, 96, 96]),\n",
       " 'batch_size': 2,\n",
       " 'threeD': True,\n",
       " 'base_num_features': 32,\n",
       " 'intensity_properties': OrderedDict([(0,\n",
       "               OrderedDict([('local_props', None),\n",
       "                            ('median', 33.0),\n",
       "                            ('mean', -164.82027),\n",
       "                            ('sd', 470.82532),\n",
       "                            ('mn', -8221.0),\n",
       "                            ('mx', 17985.0),\n",
       "                            ('percentile_99_5', 1099.0),\n",
       "                            ('percentile_00_5', -1008.0)]))]),\n",
       " 'normalization_schemes': OrderedDict([(0, 'CT')]),\n",
       " 'net_num_pool_op_kernel_sizes': [[2, 2, 2],\n",
       "  [2, 2, 2],\n",
       "  [2, 2, 2],\n",
       "  [2, 2, 2],\n",
       "  [1, 1, 1]],\n",
       " 'net_conv_kernel_sizes': [[3, 3, 3],\n",
       "  [3, 3, 3],\n",
       "  [3, 3, 3],\n",
       "  [3, 3, 3],\n",
       "  [3, 3, 3],\n",
       "  [3, 3, 3]],\n",
       " 'basic_generator_patch_size': array([154, 154, 154]),\n",
       " 'data_aug_params': {'selected_data_channels': None,\n",
       "  'selected_seg_channels': [0],\n",
       "  'do_elastic': False,\n",
       "  'elastic_deform_alpha': (0.0, 900.0),\n",
       "  'elastic_deform_sigma': (9.0, 13.0),\n",
       "  'p_eldef': 0.2,\n",
       "  'do_scaling': True,\n",
       "  'scale_range': (0.7, 1.4),\n",
       "  'independent_scale_factor_for_each_axis': False,\n",
       "  'p_independent_scale_per_axis': 1,\n",
       "  'p_scale': 0.2,\n",
       "  'do_rotation': True,\n",
       "  'rotation_x': (-0.5235987755982988, 0.5235987755982988),\n",
       "  'rotation_y': (-0.5235987755982988, 0.5235987755982988),\n",
       "  'rotation_z': (-0.5235987755982988, 0.5235987755982988),\n",
       "  'rotation_p_per_axis': 1,\n",
       "  'p_rot': 0.2,\n",
       "  'random_crop': False,\n",
       "  'random_crop_dist_to_border': None,\n",
       "  'do_gamma': True,\n",
       "  'gamma_retain_stats': True,\n",
       "  'gamma_range': (0.7, 1.5),\n",
       "  'p_gamma': 0.3,\n",
       "  'do_mirror': False,\n",
       "  'mirror_axes': (0, 1, 2),\n",
       "  'dummy_2D': False,\n",
       "  'mask_was_used_for_normalization': OrderedDict([(0, False)]),\n",
       "  'border_mode_data': 'constant',\n",
       "  'all_segmentation_labels': None,\n",
       "  'move_last_seg_chanel_to_data': False,\n",
       "  'cascade_do_cascade_augmentations': False,\n",
       "  'cascade_random_binary_transform_p': 0.4,\n",
       "  'cascade_random_binary_transform_p_per_label': 1,\n",
       "  'cascade_random_binary_transform_size': (1, 8),\n",
       "  'cascade_remove_conn_comp_p': 0.2,\n",
       "  'cascade_remove_conn_comp_max_size_percent_threshold': 0.15,\n",
       "  'cascade_remove_conn_comp_fill_with_other_class_p': 0.0,\n",
       "  'do_additive_brightness': False,\n",
       "  'additive_brightness_p_per_sample': 0.15,\n",
       "  'additive_brightness_p_per_channel': 0.5,\n",
       "  'additive_brightness_mu': 0.0,\n",
       "  'additive_brightness_sigma': 0.1,\n",
       "  'num_threads': 12,\n",
       "  'num_cached_per_thread': 2,\n",
       "  'patch_size_for_spatialtransform': array([96, 96, 96])},\n",
       " 'transpose_forward': [0, 1, 2],\n",
       " 'transpose_backward': [0, 1, 2],\n",
       " 'batch_dice': False,\n",
       " 'online_eval_foreground_dc': [],\n",
       " 'online_eval_tp': [],\n",
       " 'online_eval_fp': [],\n",
       " 'online_eval_fn': [],\n",
       " 'classes': [1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5,\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  14,\n",
       "  15,\n",
       "  16,\n",
       "  17,\n",
       "  18,\n",
       "  19,\n",
       "  20,\n",
       "  21,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26,\n",
       "  27,\n",
       "  28,\n",
       "  29,\n",
       "  30,\n",
       "  31,\n",
       "  32,\n",
       "  33,\n",
       "  34,\n",
       "  35,\n",
       "  36,\n",
       "  37,\n",
       "  38,\n",
       "  39,\n",
       "  40,\n",
       "  41,\n",
       "  42,\n",
       "  43,\n",
       "  44,\n",
       "  45,\n",
       "  46,\n",
       "  47,\n",
       "  48,\n",
       "  49,\n",
       "  50,\n",
       "  51,\n",
       "  52,\n",
       "  53,\n",
       "  54,\n",
       "  55,\n",
       "  56,\n",
       "  57,\n",
       "  58,\n",
       "  59,\n",
       "  60,\n",
       "  61,\n",
       "  62,\n",
       "  63,\n",
       "  64,\n",
       "  65,\n",
       "  66,\n",
       "  67,\n",
       "  68,\n",
       "  69,\n",
       "  70,\n",
       "  71,\n",
       "  72,\n",
       "  73,\n",
       "  74,\n",
       "  75,\n",
       "  76,\n",
       "  77,\n",
       "  78,\n",
       "  79,\n",
       "  80,\n",
       "  81,\n",
       "  82,\n",
       "  83,\n",
       "  84,\n",
       "  85,\n",
       "  86,\n",
       "  87,\n",
       "  88,\n",
       "  89,\n",
       "  90,\n",
       "  91,\n",
       "  92,\n",
       "  93,\n",
       "  94,\n",
       "  95,\n",
       "  96,\n",
       "  97,\n",
       "  98,\n",
       "  99,\n",
       "  100,\n",
       "  101,\n",
       "  102,\n",
       "  103,\n",
       "  104],\n",
       " 'do_dummy_2D_aug': False,\n",
       " 'use_mask_for_norm': OrderedDict([(0, False)]),\n",
       " 'only_keep_largest_connected_component': None,\n",
       " 'min_region_size_per_class': None,\n",
       " 'min_size_per_class': None,\n",
       " 'inference_pad_border_mode': 'constant',\n",
       " 'inference_pad_kwargs': {'constant_values': 0},\n",
       " 'pad_all_sides': None,\n",
       " 'lr_scheduler_eps': 0.001,\n",
       " 'lr_scheduler_patience': 30,\n",
       " 'initial_lr': 0.01,\n",
       " 'weight_decay': 3e-05,\n",
       " 'oversample_foreground_percent': 0.33,\n",
       " 'conv_per_stage': 2,\n",
       " 'regions_class_order': None,\n",
       " 'deep_supervision_scales': [[1, 1, 1],\n",
       "  [0.5, 0.5, 0.5],\n",
       "  [0.25, 0.25, 0.25],\n",
       "  [0.125, 0.125, 0.125],\n",
       "  [0.0625, 0.0625, 0.0625]],\n",
       " 'ds_loss_weights': array([0.53333333, 0.26666667, 0.13333333, 0.06666667, 0.        ]),\n",
       " 'pin_memory': True}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(trainer)\n",
    "vars(trainer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c7ae19-c019-4c7a-81af-ad39943a1777",
   "metadata": {},
   "source": [
    "### Performing prediction\n",
    "\n",
    "**WARNING** : file `~/.local/lib/python3.9/site-packages/nnunet/training/network_training/nnUNetTrainer.py` modified to return pred and skip saving (due to an error when saving)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c49e38c-8eb0-4504-b979-69922f24a7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('local_props', None),\n",
       "             ('median', 33.0),\n",
       "             ('mean', -164.82027),\n",
       "             ('sd', 470.82532),\n",
       "             ('mn', -8221.0),\n",
       "             ('mx', 17985.0),\n",
       "             ('percentile_99_5', 1099.0),\n",
       "             ('percentile_00_5', -1008.0)])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import is_equal\n",
    "from utils.med_utils import data_io\n",
    "\n",
    "properties = trainer.intensity_properties[0]\n",
    "properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "870900bb-86ae-4129-8788-2527356eec3d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using preprocessor GenericPreprocessor\n",
      "before crop: (1, 213, 188, 249) after crop: (1, 213, 188, 249) spacing: [1.49999988 1.5        1.49999988] \n",
      "\n",
      "no separate z, order 3\n",
      "no separate z, order 1\n",
      "before: {'spacing': array([1.49999988, 1.5       , 1.49999988]), 'spacing_transposed': array([1.49999988, 1.5       , 1.49999988]), 'data.shape (data is transposed)': (1, 213, 188, 249)} \n",
      "after:  {'spacing': array([3., 3., 3.]), 'data.shape (data is resampled)': (1, 106, 94, 124)} \n",
      "\n",
      "(1, 106, 94, 124)\n",
      "preprocessing...\n",
      "using preprocessor GenericPreprocessor\n",
      "before crop: (1, 213, 188, 249) after crop: (1, 213, 188, 249) spacing: [1.49999988 1.5        1.49999988] \n",
      "\n",
      "no separate z, order 3\n",
      "no separate z, order 1\n",
      "before: {'spacing': array([1.49999988, 1.5       , 1.49999988]), 'spacing_transposed': array([1.49999988, 1.5       , 1.49999988]), 'data.shape (data is transposed)': (1, 213, 188, 249)} \n",
      "after:  {'spacing': array([3., 3., 3.]), 'data.shape (data is resampled)': (1, 106, 94, 124)} \n",
      "\n",
      "predicting...\n",
      "debug: mirroring False mirror_axes (0, 1, 2)\n",
      "step_size: 0.5\n",
      "do mirror: False\n",
      "data shape: (1, 106, 96, 124)\n",
      "patch size: [96 96 96]\n",
      "steps (x, y, and z): [[0, 10], [0], [0, 28]]\n",
      "number of tiles: 4\n",
      "computing Gaussian\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/.local/lib/python3.9/site-packages/torch/amp/autocast_mode.py:204: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction done\n",
      "done\n",
      "(105, 106, 94, 124)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'logits-1.5-1.5-1.5-105-106-94-124.npy'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = '/storage/Totalsegmentator_dataset/s0001/ct.nii.gz'\n",
    "\n",
    "inp, seg, infos = trainer.preprocess_patient([path])\n",
    "print(inp.shape)\n",
    "out = trainer.preprocess_predict_nifti([path], save = False)\n",
    "print(out.shape)\n",
    "\n",
    "data_io.save_medical_data('logits.npy', out, voxel_dims = (1.5, 1.5, 1.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f025d6a-4b51-4c9f-a660-bef693d58520",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Comparing with the original output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c737b83-8780-4fd8-aad1-2214ce9f24bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(249, 188, 213)\n"
     ]
    }
   ],
   "source": [
    "import nibabel as nib\n",
    "\n",
    "out_path = 'evaluations/totalsegmentator_fullres/segmentations/s0001.nii.gz'\n",
    "\n",
    "orig = nib.load(out_path).get_fdata()\n",
    "\n",
    "print(orig.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a91ef707-a12c-45af-8b07-85a05930f70c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(249, 188, 213)\n",
      "[ 0  4 10 11 12 13 14 15 16 17]\n"
     ]
    }
   ],
   "source": [
    "pred = np.transpose(out, [3, 2, 1, 0]).argmax(axis = -1)\n",
    "print(pred.shape)\n",
    "print(np.unique(pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "67c95ff0-c923-41ba-944c-9a1ef7fb937c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  4 10 11 12 13 14 15 16]\n",
      "Values differ (62400 / 9970956 diff, 0.006%) : max 17 - mean 0.10623334412467571 - min 0\n"
     ]
    }
   ],
   "source": [
    "offset = 40\n",
    "orig_copy = orig.copy().astype(np.int64)\n",
    "orig_copy[orig_copy < offset] = 0\n",
    "orig_copy[orig_copy >= offset + len(trainer.classes)] = 0\n",
    "orig_copy[orig_copy != 0] -= offset + 1\n",
    "\n",
    "print(np.unique(orig_copy))\n",
    "print(is_equal(orig_copy, pred)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76229efa-42ee-4bd5-b2cb-ca487d6dc45b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Test tensorflow convertion\n",
    "\n",
    "The logits comparison is only performed on half of the patch as they combine the outputs of multiple patches. It is therefore to avoid any influence of another patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d303fbd-d96b-4d34-ba66-3d10b20405f3",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available folds (fold_0 used) : ['fold_0']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_localization': 40, 'conv_blocks_context': 48, 'tu': 5, 'seg_outputs': 5}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights transfered successfully !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/etinfo/users2/qlanglois/phd_segmentation/models/weights_converter.py:102: UserWarning: Unable to determine the root based on candidates : {'conv_blocks_context': 48, 'tu': 5, 'conv_blocks_localization': 40, 'seg_outputs': 1}\n",
      "  warnings.warn('Unable to determine the root based on candidates : {}'.format(parts))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights transfered successfully !\n"
     ]
    }
   ],
   "source": [
    "pt_model = trainer.network\n",
    "\n",
    "importlib.reload(totalsegmentator_arch)\n",
    "\n",
    "tf_model = totalsegmentator_arch.TotalSegmentator(\n",
    "    pretrained = pretrained_name,\n",
    "    input_shape = (None, None, None, 1), final_activation = None, return_all_outputs = False\n",
    ")\n",
    "\n",
    "#print_vars(tf_model)\n",
    "\n",
    "_ = name_based_partial_transfer_learning(tf_model, pt_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "62ee2695-81ea-4fb1-b1f8-824ab8454281",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(124, 94, 106)\n",
      "Values differ (1213063 / 1235536 diff, 0.982%) : max 0.22455549240112305 - mean 0.0034582193475216627 - min 0.0\n"
     ]
    }
   ],
   "source": [
    "from utils.med_utils import resampling\n",
    "\n",
    "importlib.reload(data_io)\n",
    "importlib.reload(resampling)\n",
    "\n",
    "tf_inp, vox_dims = data_io.load_medical_data(path)\n",
    "tf_inp, _ = resampling.resample_volume(\n",
    "    tf_inp, voxel_dims = vox_dims, target_voxel_dims = [3, 3, 3],\n",
    "    interpolation = 'bicubic', preserve_aspect_ratio = False\n",
    ")\n",
    "print(tf_inp.shape)\n",
    "\n",
    "tf_inp = tf.clip_by_value(tf_inp, properties['percentile_00_5'], properties['percentile_99_5'])\n",
    "tf_inp = (tf_inp - properties['mean']) / properties['sd']\n",
    "\n",
    "print(is_equal(inp, tf.transpose(tf_inp, [2, 1, 0])[tf.newaxis])[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a182f95f-d5fa-4a8f-9755-fd0b04da872a",
   "metadata": {},
   "source": [
    "#### Test simple prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "59b4e00e-d5ec-4da4-bb92-f8470f9b72c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 96, 96, 96, 105)\n",
      "Values differ (127379 / 15876000 diff, 0.008%) : max 0.9999545216560364 - mean 0.0012833111686632037 - min 0.0\n"
     ]
    }
   ],
   "source": [
    "px, py, pz = trainer.patch_size\n",
    "\n",
    "if any(s < p for s, p in zip(tf_inp.shape, trainer.patch_size)):\n",
    "    tf_inp = tf.pad(tf_inp, [\n",
    "        (0, max(0, px - tf_inp.shape[0])), (0, max(0, py - tf_inp.shape[1])), (0, max(0, pz - tf_inp.shape[2]))\n",
    "    ])\n",
    "tf_out = tf_model(tf_inp[tf.newaxis, :px, :py, :pz], training = False)\n",
    "if isinstance(tf_out, list): tf_out = tf_out[0]\n",
    "print(tf_out.shape)\n",
    "\n",
    "n = 64\n",
    "out_patch    = np.transpose(out, [3, 2, 1, 0])[: 42, :60, :60]\n",
    "tf_out_patch = tf.nn.softmax(tf_out[0, : 42, :60, :60], axis = -1)\n",
    "print(is_equal(out_patch, tf_out_patch, max_err = 1e-3)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aa89166-2d55-45c6-8a95-2f77fdf51273",
   "metadata": {},
   "source": [
    "#### Test complete inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4ef9e9f4-1b19-4a14-a85a-3ce846c0ce57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1858802/1087549148.py:1: DeprecationWarning: Please use `gaussian_filter` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
      "  from scipy.ndimage.filters import gaussian_filter\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps : [array([ 0, 42, 85], dtype=int32), array([ 0, 60], dtype=int32), array([  0,  60, 121], dtype=int32)]\n",
      "(1, 213, 188, 249, 1) (1, 213, 188, 249, 19)\n"
     ]
    }
   ],
   "source": [
    "from scipy.ndimage.filters import gaussian_filter\n",
    "\n",
    "def _get_step_size(image_shape, patch_size, step_size):\n",
    "    patch_size     = np.array(patch_size)\n",
    "    image_shape    = np.array(image_shape)\n",
    "    \n",
    "    step_in_voxels = patch_size * step_size\n",
    "    \n",
    "    num_steps = np.ceil((image_shape - patch_size) / step_in_voxels).astype(np.int32) + 1\n",
    "    \n",
    "    steps = []\n",
    "    for shape_i, patch_i, steps_i in zip(image_shape, patch_size, num_steps):\n",
    "        if steps_i > 1:\n",
    "            max_step = shape_i - patch_i\n",
    "            actual_step_size = max_step / (steps_i - 1)\n",
    "            \n",
    "            steps.append(np.round(np.arange(steps_i) * actual_step_size).astype(np.int32))\n",
    "        else:\n",
    "            steps.append([0])\n",
    "        \n",
    "    \n",
    "    return steps\n",
    "\n",
    "def _get_gaussian(patch_size, sigma_scale = 1. / 8.):\n",
    "    patch_size = np.array(patch_size)\n",
    "    \n",
    "    center = patch_size // 2\n",
    "    sigmas = patch_size * sigma_scale\n",
    "    \n",
    "    gaussian = np.zeros(patch_size)\n",
    "    gaussian[tuple(center)] = 1\n",
    "    gaussian = gaussian_filter(gaussian, sigmas, 0., mode = 'constant', cval = 0)\n",
    "    gaussian = gaussian / np.max(gaussian)\n",
    "    \n",
    "    mask = gaussian == 0\n",
    "    gaussian[mask] = np.min(gaussian[~mask])\n",
    "    \n",
    "    return gaussian.astype(np.float32)\n",
    "\n",
    "def _infer_nnunet(self, volume, patch_size, step_size = 0.5):\n",
    "    volume = tf.transpose(volume, [2, 1, 0])\n",
    "    \n",
    "    steps = _get_step_size(volume.shape, patch_size, step_size)\n",
    "    \n",
    "    print('steps : {}'.format(steps))\n",
    "    \n",
    "    gaussian = _get_gaussian(patch_size, 1. / 8.)\n",
    "    gaussian = np.reshape(gaussian, [1, * gaussian.shape, 1])\n",
    "    \n",
    "    volume   = tf.reshape(volume, [1, * volume.shape, 1])\n",
    "    \n",
    "    pred, counts = np.zeros(volume.shape[:-1] + [len(trainer.classes) + 1], dtype = np.float32), np.zeros(volume.shape, dtype = np.float32)\n",
    "    print(volume.shape, pred.shape)\n",
    "    for start_x in steps[0]:\n",
    "        for start_y in steps[1]:\n",
    "            for start_z in steps[2]:\n",
    "                pred_patch = tf.nn.softmax(self(volume[\n",
    "                    :, # batch axis\n",
    "                    start_x : start_x + patch_size[0],\n",
    "                    start_y : start_y + patch_size[1],\n",
    "                    start_z : start_z + patch_size[2]\n",
    "                ], training = False), axis = -1).numpy() * gaussian\n",
    "\n",
    "                pred[\n",
    "                    :,\n",
    "                    start_x : start_x + patch_size[0],\n",
    "                    start_y : start_y + patch_size[1],\n",
    "                    start_z : start_z + patch_size[2]\n",
    "                ] += pred_patch\n",
    "                \n",
    "                counts[\n",
    "                    :,\n",
    "                    start_x : start_x + patch_size[0],\n",
    "                    start_y : start_y + patch_size[1],\n",
    "                    start_z : start_z + patch_size[2]\n",
    "                ] += gaussian\n",
    "\n",
    "    return gaussian, np.transpose((pred / counts).astype(np.float32)[0], [2, 1, 0, 3])\n",
    "\n",
    "tf_gauss, tf_out = _infer_nnunet(tf_model, tf_inp, trainer.patch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6e9b7a44-eaf4-4111-ba7e-4602aaf6b5ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value are equals !\n"
     ]
    }
   ],
   "source": [
    "pt_out = np.transpose(out, [3, 2, 1, 0])\n",
    "\n",
    "print(is_equal(pt_out[: 43, : 61, :61], tf_out[: 43, : 61, :61], max_err = 1e-4)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6f401355-2448-4f48-89e0-70350c9dbbcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values differ (8 / 189448164 diff, 0.000%) : max 0.0010589957237243652 - mean 1.7566922849709954e-07 - min 0.0\n"
     ]
    }
   ],
   "source": [
    "pt_out = np.transpose(out, [3, 2, 1, 0])\n",
    "\n",
    "print(is_equal(pt_out, tf_out, max_err = 1e-3)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "10448d3f-109a-4b68-89b5-4d5d417ee28b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value are equals !\n"
     ]
    }
   ],
   "source": [
    "print(is_equal(trainer.network._gaussian_3d, tf_gauss, squeeze = True)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "aecebf0d-418d-4091-854c-bd84188d238d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values differ (15 / 9970956 diff, 0.000%) : max 17 - mean 2.2465248066484298e-05 - min 0\n",
      "Values differ (62393 / 9970956 diff, 0.006%) : max 17 - mean 0.10622371616121865 - min 0\n"
     ]
    }
   ],
   "source": [
    "tf_pred = np.argmax(tf_out, axis = -1)\n",
    "#tf_pred[np.all(tf_out <= 0.5, axis = -1)] = 0\n",
    "\n",
    "print(is_equal(np.argmax(pt_out, -1), tf_pred)[1])\n",
    "print(is_equal(orig_copy, tf_pred)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6347b7e5-70d9-4197-90b4-37874e40e013",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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