fastai-example-v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image classification with Convolutional Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we import the libraries we need. We'll learn about what each does during the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This file contains all the main external libs we'll use\n",
    "from fastai.imports import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *\n",
    "# from fastai.conv_learner import *\n",
    "# from fastai.model import *\n",
    "# from fastai.dataset import *\n",
    "# from fastai.sgdr import *\n",
    "# from fastai.plots import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pretrainedmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`PATH` is the path to your data - if you use the recommended setup approaches from the lesson, you won't need to change this. `sz` is the size that the images will be resized to in order to ensure that the training runs quickly. We'll be talking about this parameter a lot during the course. Leave it at `224` for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PATH = \"/local_scratch/wamsterd/data/lidc/nodules2D/\"\n",
    "HOME_PATH = \"/home/wamsterd/github/lidc-binary-classification/\"\n",
    "DATA_FOLDER = \"data/\"\n",
    "DATA_PATH = \"/local_scratch/wamsterd/data/lidc/nodules2D-unsorted/\"\n",
    "DATA_PATH = \"/local_scratch/wamsterd/data/lidc/nodulesRGB-unsorted/\"\n",
    "LABEL_PATH = \"/home/wamsterd/github/lidc-binary-classification/resources/nodule_df.csv\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(os.listdir(PATH)[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2651, 15)\n",
      "(1246, 15)\n"
     ]
    }
   ],
   "source": [
    "label_df = pd.read_csv(LABEL_PATH, index_col=None)\n",
    "label_df[\"filename\"] = [\"%06d.png\" % x for x in label_df.nodule_idx]\n",
    "label_df[\"label\"] = np.int0(label_df.malignant)\n",
    "# label_df.head()\n",
    "\n",
    "print(label_df.shape)\n",
    "label_df = label_df[label_df['filename'].isin(os.listdir(DATA_PATH))]\n",
    "print(label_df.shape)\n",
    "\n",
    "CSV_PATH = os.path.join(HOME_PATH, \"resources\", \"labels.csv\")\n",
    "label_df[[\"filename\", \"label\"]].to_csv(CSV_PATH, index = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's create a new `data` object that includes this augmentation in the transforms.\n",
    "Set max_zoom to 1, since size is the most important predictor of malignancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PNASNet5Large(\n",
       "  (conv_0): Sequential(\n",
       "    (conv): Conv2d(3, 96, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
       "    (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (cell_stem_0): CellStem0(\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): Sequential(\n",
       "      (max_pool): MaxPool(\n",
       "        (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "      )\n",
       "      (conv): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_4_right): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(54, 54, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "      (bn): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_stem_1): Cell(\n",
       "    (conv_prev_1x1): FactorizedReduction(\n",
       "      (relu): ReLU()\n",
       "      (path_1): Sequential(\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (path_2): Sequential(\n",
       "        (pad): ZeroPad2d(padding=(0, 1, 0, 1), value=0)\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (final_path_bn): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(270, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_4_right): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(108, 108, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "      (bn): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_0): Cell(\n",
       "    (conv_prev_1x1): FactorizedReduction(\n",
       "      (relu): ReLU()\n",
       "      (path_1): Sequential(\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(270, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (path_2): Sequential(\n",
       "        (pad): ZeroPad2d(padding=(0, 1, 0, 1), value=0)\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(270, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (final_path_bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(540, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_1): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(540, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_2): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_3): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_4): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_4_right): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(432, 432, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_5): Cell(\n",
       "    (conv_prev_1x1): FactorizedReduction(\n",
       "      (relu): ReLU()\n",
       "      (path_1): Sequential(\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (path_2): Sequential(\n",
       "        (pad): ZeroPad2d(padding=(0, 1, 0, 1), value=0)\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (final_path_bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_6): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_7): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_8): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_4_right): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(864, 864, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_9): Cell(\n",
       "    (conv_prev_1x1): FactorizedReduction(\n",
       "      (relu): ReLU()\n",
       "      (path_1): Sequential(\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (path_2): Sequential(\n",
       "        (pad): ZeroPad2d(padding=(0, 1, 0, 1), value=0)\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (final_path_bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_10): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_11): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (relu): ReLU()\n",
       "  (avg_pool): AvgPool2d(kernel_size=11, stride=1, padding=0)\n",
       "  (dropout): Dropout(p=0.5)\n",
       "  (last_linear): Linear(in_features=4320, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_name = 'pnasnet5large'\n",
    "model = pretrainedmodels.__dict__[model_name](num_classes = 1000, pretrained = 'imagenet')\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for params in model.parameters():\n",
    "    params.requires_grad = False\n",
    "model.last_linear = nn.Linear(model.last_linear.in_features, 2)\n",
    "# model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pretrainedmodels.models.pnasnet import PNASNet5Large\n",
    "# # class MyModel(nn.Module):\n",
    "# class MyModel(PNASNet5Large):\n",
    "#     def __init__(self):\n",
    "#         super().__init__()\n",
    "        \n",
    "#     def get_layer_groups(self, do_fc=False):\n",
    "#         if do_fc:\n",
    "#             return [self.fc_model]\n",
    "#         idxs = [self.lr_cut]z\n",
    "#         c = children(self.top_model)\n",
    "#         if len(c)==3: c = children(c[0])+c[1:]\n",
    "#         lgs = list(split_by_idxs(c,idxs))\n",
    "#         return lgs+[self.fc_model]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for /: 'str' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-554e9f37f7a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage_data_from_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHOME_PATH\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"data\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcsv_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resources\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"labels.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/local_scratch/wamsterd/anaconda3/envs/fastai1/lib/python3.6/site-packages/fastai/vision/data.py\u001b[0m in \u001b[0;36mimage_data_from_csv\u001b[0;34m(path, folder, sep, csv_labels, valid_pct, test, suffix, **kwargs)\u001b[0m\n\u001b[1;32m    284\u001b[0m                         test:Optional[PathOrStr]=None, suffix:str=None, **kwargs:Any) -> DataBunch:\n\u001b[1;32m    285\u001b[0m     \u001b[0;34m\"Create a `DataBunch` from a csv file.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m     \u001b[0mfnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv_to_fns_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mcsv_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msuffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_delim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m     \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muniqueify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msep\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0muniqueify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'str'"
     ]
    }
   ],
   "source": [
    "data = image_data_from_csv(HOME_PATH, \"data\", csv_labels = os.path.join(\"resources\", \"labels.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_cv_idxs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-6403d327ced0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# setup validation set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mval_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_cv_idxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m123\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0march\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresnet50\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# sz = 224\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'get_cv_idxs' is not defined"
     ]
    }
   ],
   "source": [
    "# setup validation set\n",
    "val_idx = get_cv_idxs(label_df.shape[0], seed = 123)\n",
    "\n",
    "arch = resnet50\n",
    "# sz = 224\n",
    "# sz = 299\n",
    "sz = 331\n",
    "bs = 32\n",
    "\n",
    "tfms = tfms_from_model(model, sz, aug_tfms=transforms_top_down, max_zoom=1.)\n",
    "data = ImageClassifierData.from_csv(HOME_PATH, \"data\", os.path.join(\"resources\", \"labels.csv\"),\n",
    "                                   tfms = tfms, bs = bs, val_idxs = val_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LearnerModelBuilder():\n",
    "    def __init__(self,model):\n",
    "        self.model = model\n",
    "    def get_layer_groups(self):\n",
    "        return [self.model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PNASNet5Large(\n",
       "  (conv_0): Sequential(\n",
       "    (conv): Conv2d(3, 96, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
       "    (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (cell_stem_0): CellStem0(\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): Sequential(\n",
       "      (max_pool): MaxPool(\n",
       "        (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "      )\n",
       "      (conv): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=54, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(54, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_4_right): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(54, 54, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "      (bn): BatchNorm2d(54, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_stem_1): Cell(\n",
       "    (conv_prev_1x1): FactorizedReduction(\n",
       "      (relu): ReLU()\n",
       "      (path_1): Sequential(\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (path_2): Sequential(\n",
       "        (pad): ZeroPad2d(padding=(0, 1, 0, 1), value=0)\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(96, 54, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (final_path_bn): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(270, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(108, 108, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=108, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(108, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_4_right): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(108, 108, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "      (bn): BatchNorm2d(108, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_0): Cell(\n",
       "    (conv_prev_1x1): FactorizedReduction(\n",
       "      (relu): ReLU()\n",
       "      (path_1): Sequential(\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(270, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (path_2): Sequential(\n",
       "        (pad): ZeroPad2d(padding=(0, 1, 0, 1), value=0)\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(270, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (final_path_bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(540, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_1): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(540, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_2): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_3): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(216, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=216, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(216, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(216, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_4): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(1080, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (zero_pad): ZeroPad2d(padding=(1, 0, 1, 0), value=0)\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_4_right): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(432, 432, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_5): Cell(\n",
       "    (conv_prev_1x1): FactorizedReduction(\n",
       "      (relu): ReLU()\n",
       "      (path_1): Sequential(\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (path_2): Sequential(\n",
       "        (pad): ZeroPad2d(padding=(0, 1, 0, 1), value=0)\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(1080, 216, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (final_path_bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_6): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_7): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(432, 432, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=432, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(432, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(432, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_8): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(2160, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_4_right): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(864, 864, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_9): Cell(\n",
       "    (conv_prev_1x1): FactorizedReduction(\n",
       "      (relu): ReLU()\n",
       "      (path_1): Sequential(\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (path_2): Sequential(\n",
       "        (pad): ZeroPad2d(padding=(0, 1, 0, 1), value=0)\n",
       "        (avgpool): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
       "        (conv): Conv2d(2160, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (final_path_bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_10): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (cell_11): Cell(\n",
       "    (conv_prev_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_1x1): ReluConvBn(\n",
       "      (relu): ReLU()\n",
       "      (conv): Conv2d(4320, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_0_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_1_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_1_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_2_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_2_right): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (comb_iter_3_right): MaxPool(\n",
       "      (pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (comb_iter_4_left): BranchSeparables(\n",
       "      (relu_1): ReLU()\n",
       "      (separable_1): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_1): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu_2): ReLU()\n",
       "      (separable_2): SeparableConv2d(\n",
       "        (depthwise_conv2d): Conv2d(864, 864, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=864, bias=False)\n",
       "        (pointwise_conv2d): Conv2d(864, 864, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn_sep_2): BatchNorm2d(864, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (relu): ReLU()\n",
       "  (avg_pool): AvgPool2d(kernel_size=11, stride=1, padding=0)\n",
       "  (dropout): Dropout(p=0.5)\n",
       "  (last_linear): Linear(in_features=4320, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "my_learner = Learner(data,\n",
    "                    LearnerModelBuilder(model),\n",
    "                    opt_fn = torch.optim.Adam,\n",
    "#                      opt_fn = torch.optim.SGD,\n",
    "                    crit=criterion)\n",
    "my_learner.model.cuda()\n",
    "# my_learner.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_learner.model.layer_groups = \"foo\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fec64ee4e134ee2afd655885b8310ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 84%|████████▍ | 27/32 [00:18<00:03,  1.46it/s, loss=2.8]  "
     ]
    }
   ],
   "source": [
    "my_learner.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skip_last_n = 4\n",
    "plt.plot(my_learner.sched.lrs[:-skip_last_n],my_learner.sched.losses[:-skip_last_n])\n",
    "plt.xscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91043ff3efae4ee0acecc951b3e9c254",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss                             \n",
      "    0      0.503283   0.557134  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5571335388953427]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = 1e-4\n",
    "my_learner.fit(lr, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7429718875502008"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds,y = my_learner.TTA()\n",
    "# log_preds = my_learner.predict()\n",
    "probs = np.mean(np.exp(log_preds),0)\n",
    "\n",
    "accuracy_np(probs, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "object of type 'PNASNet5Large' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-b4d5599df5d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_learner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'PNASNet5Large' has no len()"
     ]
    }
   ],
   "source": [
    "print(len(my_learner.get_layer_groups()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "777\n"
     ]
    }
   ],
   "source": [
    "n_children = int(0)\n",
    "n_layers = int(0)\n",
    "for child in model.children():\n",
    "    n_children += 1\n",
    "for params in model.parameters():\n",
    "    n_layers += 1\n",
    "\n",
    "print(n_children)\n",
    "print(n_layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Learner' object has no attribute 'layer_groups'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-02f0b14eb5fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_learner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'Learner' object has no attribute 'layer_groups'"
     ]
    }
   ],
   "source": [
    "my_learner.layer_groups()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "len(my_learner.get_layer_groups())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-dc49a9d8ec53>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mlrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1e-3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmy_learner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_len\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_mult\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/local_scratch/wamsterd/anaconda3/envs/torch/lib/python3.6/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, lrs, n_cycle, wds, **kwargs)\u001b[0m\n\u001b[1;32m    284\u001b[0m         \"\"\"\n\u001b[1;32m    285\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m         \u001b[0mlayer_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/local_scratch/wamsterd/anaconda3/envs/torch/lib/python3.6/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36mget_layer_opt\u001b[0;34m(self, lrs, wds)\u001b[0m\n\u001b[1;32m    256\u001b[0m             \u001b[0mAn\u001b[0m \u001b[0minstance\u001b[0m \u001b[0mof\u001b[0m \u001b[0ma\u001b[0m \u001b[0mLayerOptimizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         \"\"\"\n\u001b[0;32m--> 258\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mLayerOptimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/local_scratch/wamsterd/anaconda3/envs/torch/lib/python3.6/site-packages/fastai/layer_optimizer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, opt_fn, layer_groups, lrs, wds)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwds\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_groups\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_groups\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer_groups\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopt_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mopt_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/local_scratch/wamsterd/anaconda3/envs/torch/lib/python3.6/site-packages/fastai/layer_optimizer.py\u001b[0m in \u001b[0;36mopt_params\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mopt_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m         \u001b[0;32massert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_groups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0;32massert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_groups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_groups\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "my_learner.unfreeze()\n",
    "lrs = np.array([1e-2, 1e-1, 1]) * 1e-3\n",
    "\n",
    "my_learner.fit(lrs, 4, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local_scratch/wamsterd/anaconda3/envs/torch/lib/python3.6/site-packages/fastai/initializers.py:6: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "  if hasattr(m, 'weight'): init_fn(m.weight)\n",
      "/local_scratch/wamsterd/anaconda3/envs/torch/lib/python3.6/site-packages/fastai/initializers.py:6: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "  if hasattr(m, 'weight'): init_fn(m.weight)\n"
     ]
    }
   ],
   "source": [
    "# setup validation set\n",
    "val_idx = get_cv_idxs(label_df.shape[0], seed = 123)\n",
    "\n",
    "arch = resnet50\n",
    "# sz = 224\n",
    "sz = 299\n",
    "bs = 32\n",
    "\n",
    "tfms = tfms_from_model(arch, sz, \n",
    "                       aug_tfms=transforms_top_down, \n",
    "                       max_zoom=1.0)\n",
    "\n",
    "data = ImageClassifierData.from_csv(HOME_PATH, \"data\", os.path.join(\"resources\", \"labels.csv\"),\n",
    "                                   tfms = tfms, bs = bs, val_idxs = val_idx)\n",
    "\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=False)\n",
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe62a30033dd4d55a4a16f845bb71f29",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 91%|█████████ | 29/32 [00:10<00:01,  2.66it/s, loss=5.26] "
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b226126b45243e0b6f3e327189731b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                 \n",
      "    0      1.744007   1.98333    0.722892  \n",
      "    1      2.219562   3.067059   0.558233                 \n",
      "    2      1.97983    1.220322   0.542169                 \n",
      "    3      1.387448   0.59751    0.783133                 \n",
      "    4      1.068051   0.666294   0.714859                 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6662940584033369, 0.7148594384691322]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = 1e-1\n",
    "learn.fit(lr, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ef3f7b352834681ab50c0ae7f5bb0a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=15), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                  \n",
      "    0      0.744176   0.595452   0.698795  \n",
      "    1      0.595348   0.441043   0.803213                  \n",
      "    2      0.471079   0.440442   0.811245                  \n",
      "    3      0.445709   0.546294   0.827309                  \n",
      "    4      0.385294   0.45697    0.839357                  \n",
      "    5      0.359455   0.420031   0.843373                  \n",
      "    6      0.319655   0.39442    0.843373                  \n",
      "    7      0.288704   0.378121   0.84739                   \n",
      "    8      0.30571    0.457131   0.827309                  \n",
      "    9      0.306871   0.368066   0.883534                  \n",
      "    10     0.287891   0.378287   0.875502                  \n",
      "    11     0.281368   0.357197   0.871486                  \n",
      "    12     0.238489   0.352105   0.875502                  \n",
      "    13     0.21828    0.364039   0.86747                   \n",
      "    14     0.198309   0.354391   0.875502                  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.35439124727344895, 0.8755020092290089]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lr=np.array([1e-4,1e-3,1e-2])\n",
    "# lr=np.array([.5 * 1e-4,.75 * 1e-2,1e-2])\n",
    "lrs = np.array([1e-2, 1e-1, 1]) * 1e-2\n",
    "# lrs = np.array([1/9, 1/3, 1]) * 1e-1\n",
    "\n",
    "learn.fit(lrs, 4, cycle_len=1, cycle_mult=2)\n",
    "# learn.fit(lrs, 5, cycle_len=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "learn.save('299_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "learn.load('299_all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "There is something else we can do with data augmentation: use it at *inference* time (also known as *test* time). Not surprisingly, this is known as *test time augmentation*, or just *TTA*.\n",
    "\n",
    "TTA simply makes predictions not just on the images in your validation set, but also makes predictions on a number of randomly augmented versions of them too (by default, it uses the original image along with 4 randomly augmented versions). It then takes the average prediction from these images, and uses that. To use TTA on the validation set, we can use the learner's `TTA()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8875502008032129"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "probs = np.mean(np.exp(log_preds),0)\n",
    "\n",
    "accuracy_np(probs, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "I generally see about a 10-20% reduction in error on this dataset when using TTA at this point, which is an amazing result for such a quick and easy technique!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Data augmentation visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "arch = resnet34\n",
    "tfms = tfms_from_model(arch, sz, \n",
    "                       aug_tfms=transforms_top_down, \n",
    "                       max_zoom=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_augs():\n",
    "#     data = ImageClassifierData.from_paths(PATH, bs=2, tfms=tfms, num_workers=1)\n",
    "    data = ImageClassifierData.from_csv(HOME_PATH, \"data\", os.path.join(\"resources\", \"labels.csv\"),\n",
    "                                   tfms = tfms, bs = bs, val_idxs = val_idx)\n",
    "\n",
    "    x,_ = next(iter(data.aug_dl))\n",
    "    return data.trn_ds.denorm(x)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ims = np.stack([get_augs() for i in range(6)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(ims, rows=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's important that you have a working NVidia GPU set up. The programming framework used to behind the scenes to work with NVidia GPUs is called CUDA. Therefore, you need to ensure the following line returns `True` before you proceed. If you have problems with this, please check the FAQ and ask for help on [the forums](http://forums.fast.ai)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, NVidia provides special accelerated functions for deep learning in a package called CuDNN. Although not strictly necessary, it will improve training performance significantly, and is included by default in all supported fastai configurations. Therefore, if the following does not return `True`, you may want to look into why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.backends.cudnn.enabled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Extra steps if NOT using Crestle or Paperspace or our scripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The dataset is available at http://files.fast.ai/data/dogscats.zip. You can download it directly on your server by running the following line in your terminal. `wget http://files.fast.ai/data/dogscats.zip`. You should put the data in a subdirectory of this notebook's directory, called `data/`. Note that this data is already available in Crestle and the Paperspace fast.ai template."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Extra steps if using Crestle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Crestle has the datasets required for fast.ai in /datasets, so we'll create symlinks to the data we want for this competition. (NB: we can't write to /datasets, but we need a place to store temporary files, so we create our own writable directory to put the symlinks in, and we also take advantage of Crestle's `/cache/` faster temporary storage space.)\n",
    "\n",
    "To run these commands (**which you should only do if using Crestle**) remove the `#` characters from the start of each line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# os.makedirs('data/dogscats/models', exist_ok=True)\n",
    "\n",
    "# !ln -s /datasets/fast.ai/dogscats/train {PATH}\n",
    "# !ln -s /datasets/fast.ai/dogscats/test {PATH}\n",
    "# !ln -s /datasets/fast.ai/dogscats/valid {PATH}\n",
    "\n",
    "# os.makedirs('/cache/tmp', exist_ok=True)\n",
    "# !ln -fs /cache/tmp {PATH}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# os.makedirs('/cache/tmp', exist_ok=True)\n",
    "# !ln -fs /cache/tmp {PATH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## First look at cat pictures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Our library will assume that you have *train* and *valid* directories. It also assumes that each dir will have subdirs for each class you wish to recognize (in this case, 'cats' and 'dogs')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['057706_array.npy',\n",
       " '044503_array.npy',\n",
       " '096109_array.npy',\n",
       " '000601_array.npy',\n",
       " '073304_array.npy',\n",
       " '070104_array.npy',\n",
       " '019901_array.npy',\n",
       " '040601_array.npy',\n",
       " '046605_array.npy',\n",
       " '070704_array.npy']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(PATH)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/local_scratch/wamsterd/data/lidc/nodules-unsorted/valid'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-133-236fc2dfbdf8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{PATH}valid'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# os.listdir(f'{PATH}heart-stack')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/local_scratch/wamsterd/data/lidc/nodules-unsorted/valid'"
     ]
    }
   ],
   "source": [
    "os.listdir(f'{PATH}valid')\n",
    "# os.listdir(f'{PATH}heart-stack')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Our first model: quick start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We're going to use a <b>pre-trained</b> model, that is, a model created by some one else to solve a different problem. Instead of building a model from scratch to solve a similar problem, we'll use a model trained on ImageNet (1.2 million images and 1000 classes) as a starting point. The model is a Convolutional Neural Network (CNN), a type of Neural Network that builds state-of-the-art models for computer vision. We'll be learning all about CNNs during this course.\n",
    "\n",
    "We will be using the <b>resnet34</b> model. resnet34 is a version of the model that won the 2015 ImageNet competition. Here is more info on [resnet models](https://github.com/KaimingHe/deep-residual-networks). We'll be studying them in depth later, but for now we'll focus on using them effectively.\n",
    "\n",
    "Here's how to train and evalulate a *dogs vs cats* model in 3 lines of code, and under 20 seconds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the below if you need to reset your precomputed activations\n",
    "# shutil.rmtree(f'{PATH}tmp', ignore_errors=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local_scratch/wamsterd/anaconda3/envs/torch/lib/python3.6/site-packages/fastai/initializers.py:6: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "  if hasattr(m, 'weight'): init_fn(m.weight)\n",
      "/local_scratch/wamsterd/anaconda3/envs/torch/lib/python3.6/site-packages/fastai/initializers.py:6: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "  if hasattr(m, 'weight'): init_fn(m.weight)\n"
     ]
    }
   ],
   "source": [
    "sz = 224\n",
    "# sz = img[0] * img[1]\n",
    "arch = resnet34\n",
    "data = ImageClassifierData.from_paths(PATH,\n",
    "                                       tfms = tfms_from_model(arch, sz),\n",
    "#                                               classes = [\"alive\", \"deceased\"]\n",
    "                                              )\n",
    "learn = ConvLearner.pretrained(arch, data, precompute = True)\n",
    "# learn.fit(0.01, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9441836752640c094c7ecdf30a32abc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                  \n",
      "    0      2.29661    266.103836 0.686992  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# arch=resnet34\n",
    "# data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz))\n",
    "# learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "# learn.fit(0.01, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "How good is this model? Well, as we mentioned, prior to this competition, the state of the art was 80% accuracy. But the competition resulted in a huge jump to 98.9% accuracy, with the author of a popular deep learning library winning the competition. Extraordinarily, less than 4 years later, we can now beat that result in seconds! Even last year in this same course, our initial model had 98.3% accuracy, which is nearly double the error we're getting just a year later, and that took around 10 minutes to compute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_d = {k: PIL.Image.open(PATH + k).size for k in data.trn_ds.fnames}\n",
    "row_sz, col_sz = list(zip(*size_d.values()))\n",
    "row_sz = np.array(row_sz); col_sz = np.array(col_sz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   0.,   0.,   0.,   0., 989.,   0.,   0.,   0.,   0.]),\n",
       " array([50.5, 50.6, 50.7, 50.8, 50.9, 51. , 51.1, 51.2, 51.3, 51.4, 51.5]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.hist(row_sz)\n",
    "plt.subplot(122)\n",
    "plt.hist(col_sz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here is how the raw data looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'img' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-32ad6cb7aa56>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'img' is not defined"
     ]
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667]],\n",
       "\n",
       "       [[0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667]],\n",
       "\n",
       "       [[0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667]],\n",
       "\n",
       "       [[0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667],\n",
       "        [0.66667, 0.66667, 0.66667]]], dtype=float32)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img[:4,:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Analyzing results: looking at pictures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As well as looking at the overall metrics, it's also a good idea to look at examples of each of:\n",
    "1. A few correct labels at random\n",
    "2. A few incorrect labels at random\n",
    "3. The most correct labels of each class (i.e. those with highest probability that are correct)\n",
    "4. The most incorrect labels of each class (i.e. those with highest probability that are incorrect)\n",
    "5. The most uncertain labels (i.e. those with probability closest to 0.5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the label for a val data\n",
    "data.val_y[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['benign', 'malignant']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from here we know that 'cats' is label 0 and 'dogs' is label 1.\n",
    "data.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(246, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this gives prediction for validation set. Predictions are in log scale\n",
    "log_preds = learn.predict()\n",
    "log_preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.6858 , -0.70055],\n",
       "       [-0.5251 , -0.89526],\n",
       "       [-1.39647, -0.28431],\n",
       "       [-0.48916, -0.9497 ],\n",
       "       [-1.30517, -0.31626],\n",
       "       [-0.43805, -1.03646],\n",
       "       [-0.64033, -0.74891],\n",
       "       [-0.27356, -1.42989],\n",
       "       [-0.69769, -0.68862],\n",
       "       [-0.62718, -0.76378]], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "preds = np.argmax(log_preds, axis=1)  # from log probabilities to 0 or 1\n",
    "probs = np.exp(log_preds[:,1])        # pr(dog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def rand_by_mask(mask): return np.random.choice(np.where(mask)[0], 4, replace=False)\n",
    "def rand_by_correct(is_correct): return rand_by_mask((preds == data.val_y)==is_correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([184, 143, 191, 146])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_by_correct(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def plots(ims, figsize=(12,6), rows=1, titles=None):\n",
    "    f = plt.figure(figsize=figsize)\n",
    "    for i in range(len(ims)):\n",
    "        sp = f.add_subplot(rows, len(ims)//rows, i+1)\n",
    "        sp.axis('Off')\n",
    "        if titles is not None: sp.set_title(titles[i], fontsize=16)\n",
    "        plt.imshow(ims[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# def load_img_id(ds, idx): return np.array(PIL.Image.open(PATH+ds.fnames[idx]))\n",
    "def load_img_id(ds, idx): return np.array(PIL.Image.open(PATH+ds.fnames[idx]))\n",
    "\n",
    "def plot_val_with_title(idxs, title):\n",
    "    imgs = [load_img_id(data.val_ds,x) for x in idxs]\n",
    "    title_probs = [probs[x] for x in idxs]\n",
    "    print(title)\n",
    "    return plots(imgs, rows=1, titles=title_probs, figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correctly classified\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. A few correct labels at random\n",
    "plot_val_with_title(rand_by_correct(True), \"Correctly classified\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incorrectly classified\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. A few incorrect labels at random\n",
    "plot_val_with_title(rand_by_correct(False), \"Incorrectly classified\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def most_by_mask(mask, mult):\n",
    "    idxs = np.where(mask)[0]\n",
    "    return idxs[np.argsort(mult * probs[idxs])[:4]]\n",
    "\n",
    "def most_by_correct(y, is_correct): \n",
    "    mult = -1 if (y==1)==is_correct else 1\n",
    "    return most_by_mask(((preds == data.val_y)==is_correct) & (data.val_y == y), mult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most correct benign\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_val_with_title(most_by_correct(0, True), \"Most correct benign\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most correct malignant\n"
     ]
    },
    {
     "data": {
      "image/png": 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CXBZ7qX9Odm//lBX/0fpPrIgwuH5SriOT8jw4Ofey+lN0/w+Y2ddZIU9JNlgJIfx7M/vrVoTbfhCHH87z/OHJea+0whLjM1aEOZ6xIiz/J83sg5Nva4V4Stmv/TTK93NmZnmev3iH5+tZsfacm+Rz2gpP37dZ8aXLxyfnXW4//XkrQng/OcnrBiv+0/mrrIgm/MOd6vorinwPxArvxx8rGvz7rOhQq1Z8q3cjzrnRio76U9HfWlY03Met2Kb6His60XbG9r9gZg9bof942Mz+rZkd3Oa8f2LF5kH9Sb6/aGbX4ZzXWhl7v93PjRXPevHa1+PvVfn9AM5dNLP/Z1K+iyEK37PNvTqT53loct6dtkPcv370s9ufZ2q/tfLbyjut2EzlghULhn9gqVb7u8zs81Zow1es2Djs1duU7xus+E+m1Ume/93MvvVKv0P9PHN/9lj/PGSFRcpdViy8HzKz/2pmz8F5l9WfJucesSLUcEezeyv2Xtipv/8Uzn2lFfq3xyZ99HYrFtAzV/pd6ueZ+7Mf++nk3JdMyvR3a57vuJn9Zys+RG5Zse/JN+Ocy+qnZvZXzeyPrfjPoq3JM/+KXaa+9CvlR9+MCiGEEEIIIYSYOtKMCiGEEEIIIYSYOvowKoQQQgghhBBi6ujDqBBCCCGEEEKIqaMPo0IIIYQQQgghpo4+jAohhBBCCCGEmDqtad7sTcs/6LfuPXrIJccH5y/93rzvhDtm2dglw+xs5b3yzQpfeO4gPBz5dKft04Nh5b3CgaUy67kZf2xjy6WzU7AJbPj/DygsOSd0u+5YvuXzygfeoiy08Dr5nFHe4cZr/akt/L/EvQ/7dJa5ZOPAsj/eLK9P6mB1w9/rwopPj/27zUfl+3D1YWbW9u+Gx8Ocbxcsi50s65/1l9Bs+nTVuzK0g3MXfDlGvo19dO0X8WB7g9d87Cdco2k2/HtvRelR5uuj0/DvcZT748Oxr8+tkW+v7WZ5PSunEfIdzzUzy3J/BcsSPwfzWh92XHpz6NvYGM85GJXPwb3I+31/7WADY8kI/ayBHKJkc9a3mXzsn7HZ8u9mfs6PD3w/cdmGfYwVmc87R9r8rfx/Y/LYLp7RzMza+MO4omsEjmk4zryZV9xOmBfK8cAP/OSe7KNv/cMfdQVdHfh5YqZVzlcffs6Hd5X3y//8u1z67N0HXXrh3vLdLt/l58XZuzG3nV/16f62NtYlHG9jcjSygLE4GnvNzMaHFspjQz8WNM75cnFOzrEeaCzOu3TAHOTyWsEzY97kM4a274d5tNZI5vNkPmIfRXtuPonvGirWDixLwLw4vvaIS9/7Hb7+bni5X1ucXptz6d5/LdcWBz74RV8srBU+uvqePdlH3zj//b4C0Q6ciwXXP3iPoe69g/j8pA3h2hxrW9ZvkneUH8uVXIs2FLiujsvZ6/lL5/06bnTYt6H1q/2Yt37Ml2UQDQf9g77usyX07y4+WyTzla+zrGp+4jw6RB/ktfEcVNeSObflT2PTrzNZiY/XFOOBH373ZRVU34wKIYQQQgghhJg6+jAqhBBCCCGEEGLqTDVMl6E0Ob62H8+V6VbLf+2erfhQz4QhQ9rw1ftMFAbQQNhDB98ij2ryQoiwC8lkCCpDZxDSwvCNsFiGF+U8hrCbsOBDF0je3zmMN7/vocprk7Ac1EGG53SXrq37cjAMGqEioVHRDBka2/UhlTZGGBTrl8RtjmElNdc2EAI8vuawS28dLY/P3uvDSBora9Xl2iMw/JWhtXHIKkN4B5k/t9Vg/KZnjJCW/rCss06rOlyIobSdFkJvEII5GpflZkgvz+0wBJihys2dyzbX9X1uHe11bcWHjedbGIuiOskQ4sPQ2Wzor13Bc7VQh40o/KjVQZ8EGd/7OsN644w5xtWkGV7EkKD4ffBc1EHoIPyNYVDs0hyP47xYzj3KB265zaXf9OVvcek4NPetd77ZHfvgrR+pzHsDYeZ8N1VRzqn8xfeFZB7tYCxnGF8M5uR4njQzGx326cZGGX7YWPXzUSK74VyHkOB804e/h5myD+czfpwPqIN8FeM+51E8V9w+OW9y/q9bS7hycJ6sA3N0Y8aHUcZ1mCNcs3nyvEsf/TM/bz7Uv86l24hsXv5ieT3lLUmo516lKizXLF17RDB0u2rMupx7u3KwHSRrTMxHDCuP+wqP8V7JOhr3rmiTlEA1Z/xY0V737aB7gQN9eX2OftLvILwYchcOB6TR3Pl95ImMBOMB5S+xpIXVwXIwXdeln8oo3irlDEOTK+qnCn0zKoQQQgghhBBi6ujDqBBCCCGEEEKIqaMPo0IIIYQQQgghps5UNaO0uzDYnrTbkWUCtlPnp+Z8WGO3wtj3OH49UHtRs0U64Tbz1x679Ot4HhoSxMWPFvzx7j0nfdYbkTaW23KTmm25A3Ue1KfEsL5q6jch1h5Qv0u9xKBaBxK/O+oOEr0Ptxdf9/qgQG1GbJfDvGvaBTUPzXP+XrNrpXVBuAAhzD6BGlFqSKmvdMcgMOO1tBppQqPXaZUaM9qpxJrP7cpZ12V7kaZ0ru21bMzLkp30d858A9rVAexqem3fxlrLvs2srfs+Ojxfts98k9vy07rBp8dbsIloQ3fTLt9Hu12tyc2Qd6Jf3YwqidWzs3xnkhnSif3K5eeV93ECNaZVVjA81Nylrm6P0Gv6NvaWO95y6fdzW16jTH3pbc/9kEsPoBkNlJhFh5OqxviYSIFrNKKxToxzBjWiw6sPuHTzgl9L2N33X/p1jHG+QVu4xMakRosZ72fRouYbGrGavSyStURclkS8Rj1v9b0q70NrnCpbHTPLqJuNLT5oT4P9JBZ/x+tmlz7l3yWfIzt9tjzEZ6oT9O0REn0kdchRO2Ddp7pN5o113pOpkxqroaQvxCQWM8irZl3t+jj7iXlrxsaKb/ttrKPHPV/OLNJXj7u+HKM1rC2wzs7n/RpzV9XLMRHzaKOFth7N6Tkt0epsy+rs1+KsqOvc7f4IVXpWDp9V1jcV6JtRIYQQQgghhBBTRx9GhRBCCCGEEEJMnamG6SZkCAE8G4U2Ikw030IYDuPyuOV3RUgqQz9p2WEM44FViR095JIXXnjw0u+9M77cvXtPu3TzpA9Vzpl3vnO4a0IXIcF85qqtsxnCC5Jd+wewiWEYb7x9NsMzxvwen7EMCAEc9Xc8NwlRQTmTe7d9u8i7ZTqJgmAbowVQv++SGc+PwTb8u95a/wrx8Akf/ra07C2Vep3IMgEhkD3YqwT00S7CCVu9neukj3BXjlRVobNmqX1LHE7LvP/oxe+vzKuK77r3m1z6rjNHXJqhyQHlbiFcdtiN6mTAEFSrTiM8JoPVThaFAeUsF+xrkuPY/j6P7bCS0CRY4XT8M44ZjlQBw5yS2FBQE43oz0XW7Rq7m73Kb97y0R2Pvfpz73Dps5uzlcdHCPVuMhSsqvqTcFafV5if88e3MJ6ulPM/x/kmQnwbA6wdTp9z6VE0VjPskXlzbuPxxvyyL3c8J53FfA6ZSD6ijUxNeGyomEcxntaGd0blTOogsZSpmZ9osRaFcybh17zXmg/TzU6dqb4XO2Z8qEpqtJ+InpH2f7Vhtwx3rRpOd2lfl65TLt9+xRjia+x36AvRGjLHOi2pAcizWqt+7Oi0EXrbK/Nr9n1u7XXMkwjxzWdga9Sm9UtZ/1lWM0cThMfG3X3XhijJGmjndlMblps40tTMs3F4MeZohekKIYQQQgghhNg36MOoEEIIIYQQQoipow+jQgghhBBCCCGmzpXVjFK7EWtIqA2gHqILPQp1n8w7spUJNZYp+SpsOXj+yOe9/McPXfo9O+u1K2PqPqEDaR6AHiV6zlhDU/zBx643Zr3uM2fM/ipsTiq2zjdu3Z7oOpGm9iB+PzUaiCR6nTrQWP9btR34dvB86JKc1pXPVGelg3cXEKMfb0Of413VbZ2/V7j2N3wdnHneQZc+f2NZf3NHvJ50q+11HZ2Wf69dWL3Q+iXWlDbhKTGk/hGahhY0jzwea0y3qEd9Erz35o+79Dvz17v0PWcPu/QY5RoOUJZIbxF61M3BKmeL2/BjG/nOzvYtDeg6m0hn7MPQDsZaV+pgG9CntNEONre8Poi6mzyqg0QbXKMV7s15/R+tdWJd825si/YrS10/rj+2sujSa5t+3wEbcZynPq08nnVqtEEcq/vQZjIdzQOJJQrm5CZttza8FYTTMXJ/CdrGUI+64G3lsuuv8udH1mThvLcxqbRX2QauReKyUB+ZZ9U2MQ3sIeHmK2pVuccGxdVDvJvEYiU6n3Mb8277+uVeApX6Vc65+8TaJTS5Pqqo7yp7H7NUz8s6oWaXOtGqvHgv7tXCvhKvIaHzbCCdrKeq1pTUR/Nc7vOy4tce7Zav7/ZC2SaH0ICOe1iDb2JO7vlyZ01fJ81o/4QG1x0sd8U6xMwsi5+b+vw6SSjTT+arxeTWu5gLee1ubWMm6JtRIYQQQgghhBBTRx9GhRBCCCGEEEJMHX0YFUIIIYQQQggxdaarGWVcODUOkZYg8bWEHjL0oJdkHP2mj/MOsQ6EekdqLRGfHqghoSbiTKQThX4i0XEkOlmv6ckjP6UMescG7pst+3IND864dOcEnut8pLvhfRPdZrV3WKK7ibUEzIvvroN3B+2QkwvWeZayXNRLUPuS7axtTeC7qtGrxrrQRO9TpePYQ8x97HaXXviU9x0d3Fz6aJ5+0ZI7dv5Z8KI85Ou+O0M9ir93J/J5pAaUukTqDLvQBvL6WKux0PX96ulkMPLtczyu9h0Nkc6T/mb0wWRrpO7zpiPez++2536otrw78a77vtGlN0adHc40W+n7/k2vVZZ7QN1slOQzMT3b8W3q2JzXFlZ5cNbzfz2Ja68cb/zSWy/9vjLw8w/fxaCPuYxyNUqao+yGc3iviQ4Rfn7cAyHx85zb8ViiIT191ufF+SjSq+XwKEy0lvDcpkb0sa9bcOne2fJeBzEfNU75cuWr3mMzcD3AOovKmvhgcw7h/IV5Nt4vIYdHdgOer8l6Chr9xPM81vfCR5R5hXn6uMPjfMVfH6/9EpncPtl7oQ62uSoS3SbXVxXazNo9NkCSV8U6j2uznN7zTLO9Vr3LvFpXzDporPv23TtdPscQ7W00i/0P1qgp9eNa1oafbyzFrtlnoK76G9G+Bblfvls2xOcU+nPX+Y7HUJILL9BE58k0fUernptr9stE34wKIYQQQgghhJg6+jAqhBBCCCGEEGLq6MOoEEIIIYQQQoipM13NaF28P/2WYuhNBQ2ErXtPTYNuYXys1L411uA9Sc0CNIxm0ESseY+jSl8nHqN+EnpU65ZarNYFr7HJl7x2ZTzj9Sbd+71GLGc5WxV6SehoE80j6pvagipNJDU7NsZ7pj4oCsoPbWpucS00N6GH+uS9K67Ne7gX9RJ4H/RmjfUZifaCuuQ9Ct/z6JHHXLp54uSl349/0WtGjzz7apc+9VKvS7rwHOijFnwbG0X6ymyAsYJeltBTZrO+3DPQEg4jrSY9SJ9KHl71vsFbmztrK7ejE+lqqY/c2vB5teAj+pxjJ136A7fctqt7V/GrN/3OZZ/7fQ98vUv/0g1/4NLfc9/rXHplCM1e9H74rubb/j3/+rN++7LLZWb2jrvfcOl3er6d63tt2+8e21XWe5KNvm8zm2iP403ooQZ+Hmhw+Ixt8ZrQHc3AT3Klep+BZO+GRnP7322b8RTzQLIeqPRaxFoCc0QY4loMF1k0jOVd7BEx64Vfgd6L9J+sWNck/puggfUAtZpO15l4eeJd0O+Uz0G9P/ac8CejXUCzl836dpKsCjcq8q5aI+4h8nG1z7jTcrKtov645wbXWvmm99iN+1XSb7gGp76P7QJ7t7hys5x1e3BUUbf3R3Ic7QCa5sZG2V47qxjzutRLQjMKX9JBl9r4yGObyxSf3MYyFuNrpBnlfE/LUnqUJzrOXWhG88S/23hCRWY4nuhLqy/dCX0zKoQQQgghhBBi6ujDqBBCCCGEEEKIqaMPo0IIIYQQQgghps50hWz0R4LXVR5pBRgnbwGfmxkzTu0FPI6aj5yO7uNj7DN6GDHOnj5laSB4+Tu1l/RLurDi0uPn3+DSD7yl1FfMPXKlP1TVAAAgAElEQVTUHTt0u9dSdO895fM+d6G6nHEQOuv3Oi+QWr/Ray/bK/7ddT99r0uPz5Veq/RSo9dqmIPvGMgXonZBXcJ5X3+pJoc6JWge4ne94Nvfo9/s67t/0Gd1zR94jSQ1uhZrdKnFqNH/7BnYzwz6lFiHtOo1tM1P3+HSx+9ddOnll/i2fvqFvl2sPauso8Y8+xx0HVu+/W5CA9Fp+XIPx2W72ISO7gV/8r0uTc/S+a5vQ83Iw/TMum/L66vQRw2hKeuwPqFP2Srbc7tb3WZme75cdRrRWKtJn9BR7t/7GvwpN4boZ3E52tDnQhfz1jvf7NKdBvQ9fHfR8U7T19dg7OvzTV/+Fpc+v+W1bsMxx+Oyvsc1/qf7lSwSAI3w/OM+/SOr/z8668DftxV5gSOrrOfbSIsafO5DAB/tPNr3IczBB5NrBej1DZpRN/dhLE68v7mPw2k/jx7/79TVRuPUGT8fcd0R4CfJcudr2Osi3neA+yE0oQld9uOrYY+IuH4DdZycj+gVOo+9F1gWq4C6WGpyW+h39B2di/rwCGvG0f7w66a2NfHJpCAwhlpqzMm1bapCL534ts553bHbV8TMDD657l7UrjJvrjETvWqsEadAEs/AdTS96dHmYt13Z9W3v3EP/sdoj9SM0nd0HOvEu2if0E8myynMda1W2U64xmmwDfEjD9XW2S72wtjtthlVnqZP0RYc+mZUCCGEEEIIIcTU0YdRIYQQQgghhBBTRx9GhRBCCCGEEEJMnelqRg97Ed54weutGrEu4fHT7lhiRsV4anpCUkMaH6eGkbHt9EtiPDq1mHF8O6+F1qKx6GP0+3P+3ld9onyuhTvP+7we9T6CSRh3TZx9FnkxNQ96jeh9b/f+iEde5f0l16HVWvn157r00Y8+UCa6Xo82uNrnPVzwddI77TUPjZVS00tPV/rJJc+8Dm9V6leiOhkd8e+iMfL1deSz0MneecJnBT2F0x7RDm24PzSj7AvUYsTal0R3BIHERz79Wy795md/nUtfd/dVLr36olKze/KlXhczusG3kfac14y029AWjnx7HQ7L5xqPfDlH8DTdDH58ODf2+qlGpPOo00uEZo0vHnQi+VZZtgHKGVo+L2pbydvveqNLD7LyOc9u+rE3QMvSa1XnvTko+3B/6NtMrKndjsHIn7813Hkaop50oef7P73XNgZ+bBmhHfA5/bEdD+0pXvuFt7v0773wN1061kfz+W1EPRoyZ/XQFjMa2oez0F7N1MyjNT6EsUYyQG86OubnEHqBNh+HpjmeFzAfZcu+P4cNzCnnvA60cdeDPu/oOehRmIjEoO+j3i/xwY7G22TvCpBTBzrceW8L+lwmeePdZGfP4TjWW9GeHYleb8brtpO9QqyaeE+JHPc17hGxV6E+MlmPRl6VdV6g1OvS4x3aa3/fau2qUeOc+LajvcY6zwxrrzpvUHoHR+0m2d+kDgooqS2OytLo+2OtTX9tC76jrQ2fbm5CQzoblbtd7Q1KjSiJ5yP6jGYZta3sg1ib8V5Po5/603EffTMqhBBCCCGEEGLq6MOoEEIIIYQQQoipM9Uw3bDpwwmaCMF04TQLPpSGdixJ3vx+vCoMZTioPjfHltWb2Ea648N+XDhC8lV5ddhu71Fvj9E4VYbmMuwmCTll6A232mbYRBxShNCO4Zwv5x+86Desipse+iGXXr6rDLlsnfPvqs30Wf/eA8Ki4rCTHPVlbdQ9yRleuHMIUeuUr/vjH0AYNMJws6Q+kXfUBpNQmort1vcSYXam+ngUVpYhZJrP/KbrX4bjCK05fdYlF/6gtFSYu/+4O3byFd7G4PwLfV7NY94igbvjxyEvHVimZAgzyRgei+3aG80otAYWMtyePXWB2kVoTU3oEm1L3nH3G1w6Dss1M3t8rRxThwi7byG0luGxJD7OcxkKu4nQ2WajOu/BoGxHDL9mWO4W82ZZWjv3O9qeDAbUguxNHr/g5QVv+NK3unRswzNmmO4QVlmwFmrgOO1b4hjLDA4p/WX/Lto9jNUbGPchCcjjkFVYnjQv+HFpvIT0VT6Mt7FV9vHxAmyKjvtr5x5EHVzw80ISYhmHOtJ6jGsDhlAybJchxNGck1jOcb5nWC4kKjzfnVtnNcbwWA6otNaL8+5XhI1ulzeJ1ik55S2UVO1REisdfu0TzwN8r5iDGbLOkNTkXcbttVkdQpmsq5kXQ2vjsuwytJZ2LC7NsFuSWPbV3DtqN41N30/aaz6vcQeynR7sV+Z8HY7myvOzNso1hzU7SFQR0XzWavj3mvEzTQfrFqwlxrR2GTyFYbpJ+PFTl/VF9M2oEEIIIYQQQoipow+jQgghhBBCCCGmjj6MCiGEEEIIIYSYOlMNwM+3vD1DshV0HAe+MOcOccvpnHpTbLEeoHHI4zh7xp8zTp4x/NyunbYyg53j3RMtK+L9G6uI2Y90d3Vbuydx89Qw4ni8Hfv4+GF3bO5RX86bf+OHfd7zPhZ++XP+fXSi7e+z8xfMH6zWeVZG//dRt4ntTo3OC1vnO13Spq/7ZKv8Gl0Ct7SPt/VPdBx173KPEGB7lGqLyvpL2jbtlfjM1B2hX8Va4fCle9yx448tufTSfde59KN/wevoBjf5sabbK/UpHdiWZB1uoc52gLEl0iVmYz4T9KXQRzabsKCBziOPdbWNai1mf+j74CDz9d1r+rYfazUHo52fqTju89ro+3u1om3oqT/dWocdAOpvbs6/m16FRQ3rfqPvxxL2UGpGxxl13ZG1E/SUib5yj7K14vvow+b1ks4igNrfDuauoa+fGvcVy6JmMOpBS9VDf56FhdoKxgPqLWPLlIHXlzXW/FjdqNkvIWyUbSz0YD20BX30BvaQ4JxRpa/EfTPqYvEc1HmSWEeb6E+5JwT0qCxnHtVnwPoox4tO9Ki0a+HeDbuB80Qq6Ec6KjfqoFaPukdJ6rcbtcnETsX3mwxr4ca67wvJ/hSx/VpieVitzczRFUJAm4r3W0ksErFGZ1vnXi2uTqhtZ5+rsY0BztpxhP6+6fNubWD/BGhEm32ktyJNcwvjJ/SnjXZ1fY/j9QO6GK3bGgHzFY4n+1PE6dEuRZ+cNxLNc/XlTwR9MyqEEEIIIYQQYurow6gQQgghhBBCiKmjD6NCCCGEEEIIIabOdIVsFKQwHcW6B/hYjQ97n8EwBy+mE6dcelcOSNS2USPaor6qWivns2JeiHVnXrFWg7pXXov4/8A4e8TV5wtlnQ0PeF3X1R/zno+N3/S6hPEhr8lrPPCQPx7pRBvwUks8NqnvoS4krs+tao9X6mQSbUsX77bCI44knpuDar1PpVdWhS/bnoZ6qdjnrcY7NfFHY39HfTUib2G+1wweejOfus+lb7xwjUs//nLvU3zhBWW7yA/7vMbQfc7Pek0j/bw2tso2OOr79hWaeM/wVqVnaQs+mhalqWHM4Am5ue77wume1xYdnfM+uYdnS+/Gx0Z+PB3RdxTaVmpf41QiXYfepIln7A98nQ3xnLFOdDyq1uSSTqe6TcZ+qiNqcmv8T/cKoenLOWR9xppmeqdyyGtxTsYJaM6xX2LW3vmYmVmGsbc1N+tP4HwVeUAbPErpN51oROFVHdM86/vBDDRk3MchoJz0Ao33XsjXvR9q8kzsHCx3D/rqqA7oG0odbV4zp8Q6xWR/gzp9P/eb4NxX5xVaVS7O4dTCxutA7gXyZLSrVxJqHOPnwHvMz/k9N/iNUdIXqC2O392u9booC+5to+gvKHeYq9m3hXnFfYX+sVhDpp8dqr1W3b27qC/k1cSeL60tzNGbTJfPmbXh9bmFOZvVj7E73sOgDU/sXqvaC3iEdQn3r8ijPp+zIHW295x2oX3N47XImJ+HavK+vFsKIYQQQgghhBBPP/owKoQQQgghhBBi6ujDqBBCCCGEEEKIqTNdzSg1dYz/j2PQ4aHVWPM6rrAKn9E6H8dOdDyDRmlzZ69PM7MQkDc1JJEPFL09M/gQhQ3/XGETvllRneSIdc/pVdWv8m3yMeNm3tO0u0n/M8Sno/6bpxAzjhj9RqzhZf2hmHUh5U6TOwPdJjU3eBeJNoO6RqcXqNbz5DTdop6CdTAbaY0OH/Dl5Hveo9ALON9Ev4u9xRJjK/Rvvgvof1JtduSxB71u4pm7supv9eUHXPqaE/QlPXbp95Mv9XrJ4c3+GZvz/l7tpn/P/WFZzmGNhjGDTyZ9HRMtR5RudakZwbW49To8OM80vPYt9uCk3mQ4pKbJJ+n3GXt0tqB1aXd8uXsdrzfbGvhxjZrd+F51Ok5eO+BYQ11N5Ck7htalTf3uHoXvndMqdXa7yjtQR8s+G+lRu9CuzvtzBwe8ZrwxoBfoLsZEaBa5p0Sin4zHZvgdBniUB+zFkHMvBujX3FhETSPGy0QPyXmWurAKT9NEG8hzq3ScGIvp05po9Aj8PePnCND7UV9KrSv3EmjMen1/voH1WEy72rN8z0BtJtqga2PJugNe1Ru+TSX6SuqBq7TChN7qNft7VPl7Jn68PLfCxDhp21zX0VO35nqunR1j1C80o01oRpuo/mbUPJsoZjaD+u5inMJ8Fus8OcfOtOgTjrxwfoa1RLz24MiQj6t9WpO9AqqmYa5hatZEO6FvRoUQQgghhBBCTB19GBVCCCGEEEIIMXX0YVQIIYQQQgghxNSZqmaUuo4QGJhcprOjXnM3Wvaahvbp8/5aaBoYr+68w6gBoXaVmgfE+9NfqVHh95UdP+jvNUQMPjQkcax7ohHFudTNJCTiojId+IzQzSQekTV+abHOgT541DgkiiZqXeN3Sf8oaoMT7088F/3UYg0FNAw8t85H02lEzSx79rXlpbO+nJ0HzlTmtVegRjTRkET66ox1T99Qtt8GtEXss1U+rTWa8AzlNqTnzpbjxY0PXu2OnX6ZH2vOvBhCkEPQq0QaiUQDmthi1vXRikPUm9ISblSt+8x6/vqFTvkcm20/5o1GNRoSkPh/RjThtdofQvuO56KHaazlTDS1NSS2zdCBOl0N67dSGLOHQN1Tl+zgIyViK47VOJ1DYGwzSM3oLDz3oJ/K2xgPqH2Lj0HXmWjb6uarqF8m8zf1jw2sB6j34zwb51eh8bwsKryrk7G3rn2y3Lu5tsZDO9G6xvlxnJ+pGUs4ltPPO65vanLbzxCf0ejdJmsU7rkB3WcOPWVSJ/G7rFvj1FHRpkKNT3uaF/dHiMqCZ076N+6VaGGp+54r+3SOZ+D+E4mnLq2ZcXor0oxmbV+u8QzSsyi3gSjzgBt1oSdv1nj7UpIb76cwxP4IrJN0XmDmO+9tkVA1H1Wgb0aFEEIIIYQQQkwdfRgVQgghhBBCCDF19GFUCCGEEEIIIcTUmapmNFx12P9hdd0fj+OYoa1s3/6gS+fw2KQvYaJpjPwTA7UY0JBmVy34vOHJaSe9/i+PYuHpSWajGsOeRKdYnj9EOUZzPph95uE1lAO+olu+3HmInpsx+HU+mCwndSKxRyQ1uMwqg48Yzo91dvQsq4XlorbA6X+o34Uf3QLawZxvY+ND/vjW0fLdN4a+vkbHlncq8Z6C7y5QRBYJExKvuhqNEzXjiQ7J3Yc+gmgz1BJRh0S/31gn9sW73bGjp/y4NP/ItS79+Mv8e9+8Lio39dHU6EDfF5o1GtJINzIeQAdDXQd9xeC5uQHf0dhntAM9yiyGrcHI1yc9zGKoVaWelBrRFnSc7fbO7WCM/y/lM1Kf2oHHKXU44wpfN2pX9w30Bo3aJNufQTtkfOaaKgixXhpdbowpeAjNaNbG/EStXDxfcdynjo4Fo49jvIcBNHV5o1qPRp9RehxaFun91qFtJcl8VKOnjNcSjV0uzzj2RONxct86L9oarXucd77m13GNrtfvBqbhHU4/dFc2ruMqS7VPScV+Po32Ozg659Id9pW4TbL9JRsPVK/rqnSeidbVauAcHfuMUz8OXWzO9SrXDjiedVvb/m6W7rUynvHHx13MX7TRjOogYF4MHF+HSFOTn+08H/Wa/r2+95aPu/Q77n6DzwvXDyMvUc7fOb29BzUa0hErwXZGPqNCCCGEEEIIIfYL+jAqhBBCCCGEEGLqTDdMF6GgtHbI43COx0/7i2tsNrjFOsM741CcJPRz04cAjq9aculTr/Ehlsd+z1db40IULlu1vbqZhT7uzbCIKFyhMfDPPDjmQxcaR7y1CMN4W+s+9q57ogynaXDr/IowJzNLw2VQZ3HoTV4XAlQXxhuFYCRhzwz1QLnCog+dzRZ8SFDjvA9tdsz7+mQ4TGPL36t11ocnzX3m3I7lYojqXiW1X6JXRtnGGqhrhtrkaGPZyorPm5Y/8bvme60LJ2L4EcP6oj6fD3z4eoYws9nPPuTS12x5K5iTLynb1OrNeM89vmeEzmU1//8XV3fNduuhhVBmhOk0G/74YreUKlTagZhZp+Wfa50hv1F4LENhW43qtj5GHbAs3Shstzvrx3VeO9fx7/LaeW/59Z9v/L3KslTzj57EtU8jSagt+2gURoYw3LyN0DpcWmXlMrmiPIRrx1SowOZgBLur5jpCCDejEECEygbas9BViuNrXCcIcwwtPBSlCYnEhxZUUShtB+GENeuQHAVP7G3iF1Jjw5FIEyhdiMN8A8PyID2qWV9xPeBCcdn+2rDGWpp3yeHRRZ/XJiwsonViYm+zX2CoN9eFUToZiXkubEv6B3x7ba1gjXMqyrHGEi0OOS/SDNvHc0TzbhLyyz7I9ss5uR3bGFbbQGVzsLNBOQPkcP3D5WA0glyg2Yd0o4sQ3124nAVUXwPjUkD4aw67tdgKbmuIzxU14a7vf/bHXPqNX3qrS3ejOTzrUO4CaQ1lOAzbJVVFe2LOLvpmVAghhBBCCCHE9NGHUSGEEEIIIYQQU0cfRoUQQgghhBBCTJ2pakYTjShjzDdKHWLA1s6pVhCaB8aQc7v2Kv3F2B9rP+T1qgex9XOArQz1ljHNc6u4F56ZurmNyIIGcfKr1/n/Ozh/C7ZMZ/h/Bxq+EJ9/0B1r4JEayGvxfl/uhXu9zq55NtJiXoAuk1qWOa/NzHvQwsb63hrtapIX9BWNk+f88UjLwWvDutfBdh9Ae4UVUWwXZGa+jXK7+hr9z16BOqREWxQ/x4Kvv/5xrwXq3QfdNzWj1GrNRvlRS8Wt3KHNSjSk1G5F/a5O58Wxpn3C9+HDnyvf7bjn++D6DXjPtM7gf/9VSUxpGzOu3p49w/kt2J58+Dkftp14+11vdOnfvOWjO577TOWd97zepd9/7AoVZLdUWf6w/dHqhbYwbeiQqTUMUT/K/bFxz+c1WIDVwAL61Rr6/2ZkD4axOYG2EiOKt8p7j4/6PR+Gi1hbsItijm6f8uO+Pfr4pV8zrGm47kg0oYllFeazOuuXGO69wDkmHjOh4+Z9knkWz9HAmOnufcjX79a1fh7YOlC9zJx7xM+jzbMV35Hs1uptj5C857h+qRGlkBttu3PBz1eNLcxf8VoYuk7aHFkDaeqludaNLX3YBxONY431WzR35nNecD6epR0T5r6Wr7PBkm9jF24q00O/9Ye1sV0K183JOppdOC4apcAD9Ju+Tyd2bY3y3dEibVS3vwTgHhHz3XJsajX9Q41ov0bHH+5LwLQ7mX94YqJRfTMqhBBCCCGEEGLq6MOoEEIIIYQQQoipow+jQgghhBBCCCGmzlQ1o8PnXld5vP14qSkL1AYwdp06L3pd0Z4q1mogRj/RyUEL2H3I6w4D/BOdHg2eRaNjXk9x/hYfwL56vT9/cKDMa7zsn/HYNY+7dBMx+pvwKZrr7KyvqPMwYrz6ypbXxj120nuHzd5Xek4e/xPU3/1eO5hTRwOywwcu/R6oSzh7wecFDzjqeRNdZ6yBmoF+Z9Ofm8N/kjqbQF1oY2ePuMSrbq9CbQv1lZGGNKx5je1w8YBLN9H226jfhLg++R45HgxxnFps6lOq+n/Xt+1EewWdV/tc+Ryzj/n62TqMvEfQMFP6OufvlcVtsk5nzEfc8vda3fA6nLfe+eZLv3/w1o+4Y0+lRvRtd73JlwuGlBx7ek3/bi/0S9+8lYF/N2dX/Pg5WPX13zrrtUadc/7eM6fz6Hf/Xpt9tKEP2t6k6198AzrP2AOW3rMZ6j6jhpTpKk0zZYaUrkGORt/RrOvbazPS++9WYZ/Pea/FbL5sFys3e/3p2jXV/wff8sOaLd/lyzkb7S3Q4F4WNZrPMOvLaRgTs9Vyv4VEP0qN/YzPi97fTs+6C//I7e4doI0f3lwKqs8+15dj8yr/ntvYQmLpXt9Q2mewF0O8b0bPj2H5LMxs9yqsP+pCs4pxnrpN7NvQWvfjZTYLD+hD0TxcoaUu0tSrws+T2uJo3Z0oA+vaGMij9rx2k/csXzuO+3LLGEzZW4f8vUdHyjprYLzcol6yjzl7yDncP2lrvUy3V6ERxyMHai2H0L62y3tl3AOixguczLZ8O4nnWc65Gx3fB3nvnHsH5Gy/Udm4t8UTRN+MCiGEEEIIIYSYOvowKoQQQgghhBBi6ujDqBBCCCGEEEKIqTNVzeipl8A7DCHRx/64jGNu3P9YdWbUeUFTVukrmlXHsid6P3ogHfJ6ycZGGat9/nleJ/fY6/y9rrnR6z6vaftyx1rNzaHXiFAjOkZMeadFoezO9Mc+Jn8Mjeg483nPQn96/Fn+/Zw6Vmq5zqwc8uee8pqS2EvVzCxvUsNX6hboJxVmoBlZ8R6QOXU2bfijxVpD6inglxaoO6zztow1E/RWnYfZ1R6FfmiJ9jLWmKA+2qv+3OEi2u9hryltnPO+o64c7M8bMAdjuQi0MFmUX8C7aSx6vUqiS8J40FgtyzL/mG/bw3nf3nrnfP/vrPh07IdmZrZya3mvvMt3UeP9hT68dcaX7fYL11z6/VkP/xVfzhmvN9lch4fxGf9c7ZXyXi28mu45X1+z0Ga2V3y7udCDviqq7x6q4KYz8CQ8fcql8/NoU2gnWaTRy6E7/uijn/HX2k/YngRjc6vtn3G2F+m6MWcMRr69bW7495rBBy+0MBfG96Y/54BaQej9qXFuUqcU3Zu6bowHgb7DIPbUzprw+sOUkHWhN5vz569u+XGstXX40u/t02j8GCvG874fbVzj10CNkT9/5uFIPznk2OuTYQ0a/A3Ud6xnrfERTfYKwDybH/DeoYMlCIIjZk76ey087N/VzN3wn67yJcf+EtlSjf/sXqFuHo3XDvSeTTx04Su64cet0QHokA8v7nhu7T4E6FfUuro9JFpc/3DfBqSheV6/tVwnnn6Rz2vrapSDEwH8kzkGdps7r/EDt4ioaVIj7PswPFtm0FrHeFnjWRowdufRHD6GlnVrvHMf245ff9Zvu/S77vvGHc/ttn399ge+n3EfgjGbzSAqK7/SfIISUn0zKoQQQgghhBBi6ujDqBBCCCGEEEKIqaMPo0IIIYQQQgghps5UNaPXvP/+yuN55ItFz8fGgtdp7spX0MxCrD2A3pQ+o4meAn6KmzcccemNw5Fe8lVe23LzjSd9MaHroi401oHy3CG8gGgZRX1QlQ40y6r/H4J59eFhesG8pqQZxfCffoGPR2/2D7r0gbv8u21dgAfnqHx3jVWYvqFdJB6x1HWynfBdx9d2EDdPLeEF6NGoW4p1IA3oKeiTuVep8dh0Wi6c24F+Kuv6+kw8YDf8u4297AL1KPQ780d9/zbzfrJm1qjw/8vnvWgkW/QaHOpuQqR1nbmT2tWjLrVx1Jfr9It8ergITV63rO/OrG8zLWjC6/rwFrTay58u9T6LD0KHhP7d7Pt7dR7x2sxYw5fzXXH8fBw+wzXa62w10oGzH2HQG9HbLqvREsdlQznfeO1LXfpj1bLEK0ajs7NG1MysV+EvvdjzY+0W0idPe21gvsV3Gx3rQVc0Cw0o3lVzAD/ULejCBlEa43aiEaW2Delmr5xXO2uYq/pob9SQofkOFv0fVm4o82sf8QLUEX3G4a067uE49GqNm5fK3/EaZ874Oln+gvc/z09AQxqN3fQJpX9n0mcJ/L1no/7feww62AHeDfaI4ByeSMyi8SFf9XrSsDDDs5/5ZNReY51Hv97IhzSseA/XZN8Lwv1SqF+N1k95C+LrBjSO6P+b1y+59MmvKc/fusmPQ+0e5qcKDWgd9Fpmi6tr+k3cO5svyzaa8WNea6MmMx6O1uQ51vcbQ9TvLvnVm37n0u/fde83uWMLXV/fXN+zTnIYvTp/6qfGZlTfjAohhBBCCCGEmD76MCqEEEIIIYQQYupMNUyXYXn5AOFvUbgWrTAYShfWEBLIrckRtheWyvCjnCF9yKtua+2Fz3l7ls6N5RbVZ15RbZmygbDcIbaNbkUhAbRXYbhBjlBa7JSfXB+H3jYaPvRgCKsXxiq1cP4A5Y5tZa6+2YflrV3tQ4TueciHgi3f7kOGDn65DCHoPOzDCbjVeG1YLislvnYLIb9r1WERoY0wlI4/P4+tYmjt0n1yIRfTIglZx3PElisZbHVY04EhvwjPymDX0sijsLI59H/mxXbA954hVGy2HD9C2lF81mtoc1W2UWs+DGrc9XmfeTH60U2+zjoIAepF/azdRKgswnQ5tpDBgn+XnZWyz8594n5/MsPfZ304HPtKbJXTOOgtexJrHNpwcXxFTFBjoQyPzzcxZzBkjWN5xnEMtgnRPPHhhz/ljn3Lja+0/UAGCwBu0/8nX/W+S7+/6cvfUpnXcnez8vjjJ7zdlcXVOYR9EmxgsjZ1JMg8sReJ+j9tt0gSlu/zyqIwXd63s4JydnAthuqBjy60YRy2m2O+78FyYs3nvfAQrFxOI+QyKsvaNRh7MW4tUlYC64wsHpuG6BctzJuUkbB+sb7Ko7G/QdnTHGKPaeHDsXoXhNETv3aa1D2je7O0T2G/qJAWbX/zyB4Ma+wk5LdifWS2zXogPoZnzGZ8x+kf89K6U1/tj2/eXE7xkvYAACAASURBVM6zDMs1rG2zZC27Y7HMzE8xDdjAMJ1ei/mINjK9sq8MDlFGAjkRqjfnvZs7l2Vlq7vjscvhe+573aXf33vzx92xt931Jpfe6vn37FcpqXprEIcXU8ohaxchhBBCCCGEEPsFfRgVQgghhBBCCDF19GFUCCGEEEIIIcTUmapmlLqDQLuG6Dh1Wra6vuO5Zttoh5j3VqQDW/daNaMWkDpExs1z++tIVxe6vlxbI1g5QGtJC5U4pJ/HmB6NqU+1SlqRBo22EMmO04ibp6aUerbYdoZatnnYByzd6u1uTkFbcGK51Ixd+9teO9jklv+tassP6/f5lxK+V7SxpF1AqJBT1xTr0/ojHHqK9r9+mkk0JtRmxhYK1JNSE16jDaQeJVsvr2+gvqr05ZPMrZK4T7e8FiOxmKFGlOlI60q95Mr1aFPX+rFmeR5jTwUN9PfNAWyg0M9mu76Olpd9ez7zVeVzL9193B1r3fuYvznfFZ4z1gNTI5q3/btpjHwfTjypaMsV6Vdj/aiZt/8yS7WtHPcTrVuko3vLdS/3eTX2hx6t0YbOmPq/iNue+6Enda8X/Y/vcem1M7M7nGmWU2aIVzOGhjQMMYfHYwvH1hptG/eBiPWVqW6LF/tk1vZ/GC5CdxxZ2oR2dZsZXfB9trPqb77wkL++e7587sGCv5bdaOu4fxeza4dcuhGPr1wcBOo48S4S3zisF+K8qcuGvjzpk4T7KzSj58YcnMy5exU+E9cO0fgZaEOIvSi4xsmaNXNdNGcn+lNeyzUQ9b3ceyWal6lhHh3wHf7M8/1zrD/Lz6PdOaw14vti/ZnB9oSa0iRdQbL3Ss2lVRrS5rJ/huHIry2aG+g3I+rAI+0lugntVnbLr9z0uzse+8Att7n0G770rS7N9T/rYBx9jhmFne2/doO+GRVCCCGEEEIIMXX0YVQIIYQQQgghxNTRh1EhhBBCCCGEEFNnqprRsOC1gdRihVgPRB0RY8ZJG+Zg1H3GPnrUl0CHQC1bvgDPQ/gpZa3Yc8eXswkdUhtegUlsfBS/PoDelOdSt0lNaZJ3pDFjTDjThD6jTfglxZ6mY3oS4TlaKDd1dGe+uszr9BmvGTv8SXhGrcMfscajy/mSUsdJfVniYYo2SV1jfD41d3W6mT1C4udJrWasZ+EzzXjtYIN6081qT8NY953B9zIfohyzO2vXzMxCDx5dkf9XojtC3tQlJmWJjofjR92xLS/bstlZnxd1nuyj/UiL0YN/JPs3+xHzIuOF8vy8g/GU2uoedaDoR5GPY9iiRyE0TtCUNmq0xU6njD7XWISGFGNz2PTPkfPdRX2UfbJxEJ6ae5QcPqN9ekQ/hXz+Fb/i0rf+/vdf+n0Y4GsJz7z+Mtr2kk/Ptyt03zVewInQC+1zPFumNw/C6/tqf+loDn6J8Aq1Od8Pm5FOtG5fBzvqr72AtcNw3tfh/ENlWXvnUK5VzP/rmJ/oM3rdsUu/N/pYa6Gf0Ac7H6JPJ/todHc8xj5HDWQtUVnymWqP4r0KvVcT4nGN6wruf4J3k8369GjWt+9WpEtucu8U7sWSo11A0xi4xumV9x4d9mPxuVv8OL96s3+u1ry/F71Dq2jAj7turftE72OW+oqmfbz8vYlyjeG13EC3yfvQXsavEsXkvi5PJ0sdPyc3jOt7eEpHdZqN8VmCnsaXib4ZFUIIIYQQQggxdfRhVAghhBBCCCHE1NGHUSGEEEIIIYQQU2eqmtEkXj2D5vFQ6WWXr675c6kZoT8SdF+Jni3WONBDi+WkP+LG1g5nFnRPl/HWS7cvuWMrR30cfQeecPQKjb0F6/QodXHzDeg843slmtGauPohTJCYd1wWVu8Iel9KNak/XV4o6/PsrV4TduBOr5doXoD/LH2x6EsaBfznGQL604L5NLzWbMZrdEKsnaH+tM4Edo+QaG6hfUn8fKtgH6VXXZWWKKNGrNpHOEAvldw7psp7dptrE23hgXKcuvASaEZv8OPQPDTi9A4dot81o+NJf6Y9Z1at+2a/ijUpYYSxYwjd12DVXwpdmNNEsa13vSfpeMmPgSH32sxkfI3G/ryPcR0eholXNfSo41X/HLHmrHXsKnfo3DfcZPuBHBqd9f4uNXlPgpuvOn3p9zvugfiS2qGub2Nbh3z/X73Bt4uFrJw7W+fhx8vxE+PQ4KifJy7cVNbJ+jW+XIMDaK+z0Oyh3zQ7/ni7XabHI85t0Nz5nK0x4/vRxvXQkPXK9rx8p7926R7fttsPnnbpHG29cfRwdDLGwy2MgdRici5kuuJrjKRPcrzlfgrUo8d61QsYhxr75PsTenCz/cbPwWeq8cwOI6xpqEuM9wPgvEjtbx04P4zLd8V9BzaPoNyL1XseVEKfS6te67LKgltHI+uavRVIVd5Jc+xB5w1dfOA6O05zm5zx7spZxffc9zqXpgfprz/rtyuvf8sdb3HpTrRfxRnoSbdGT6yP7pOeLYQQQgghhBDimYQ+jAohhBBCCCGEmDr6MCqEEEIIIYQQYupM12cU8et538dXD4+XWqJWC5rP89D+UCNKTQMDsCOfweTcHDH4czUehtBXNTbKshz8si/XfS/yWpbDxy/4axFHH2tO6O1J6P1DT9Mh9KjjSLtJLyXCOHlqRBl3P4rK0kz8EBFHT3vPCr1q3kb9tODTCG1GgC45h0/mrqjRV4TZGX+vtVLnlEOTs199Rhssd+Tnm69Dr7vm04luGyRebNG7TPSkbWhfSKJ5hH9arP+FJ3GgjzB1x3iO7PpSa3jypb49Hjhy3p/Ltj6CnqpC502NzRDX8vhSz+spN4e+zpqr5fWNLWjyADWkAe3Cea1CY9uAjpuMFqEhXfDtoBX5wFIDmsGrNqCfZVvV+v7WDddd+v3EG691x85+9Zin703Qpkaj6Y0ttz33Q5d+/5q173bHzj3s90vI4Ts6moeG9IDvO91zZb9sbmDchnl13vXPPFj0bXDjeNmv+lfRjxPzfat6nqVGd5BFujnMXaFCp21mFlAnrUX/nP3Iu3X0APy5T674clH3SX3/SrTvBsfPZM+NGl9RCtbjNPPinEz95LBmr4Z4LweO6/tkHk3gHgiR2DDZp4F7U+A9N0/7djCD67N2eT19WkPisU2PaLQT7JuRR2muxTJM2Um/wnwVr0E5DyZ2sjU6T55fdXrqI4pick2esWzROgXXNru+frOu7xvNTfSNYbSHCTxK86fQZ5Qa0d3y4ed82KXfdtebLv2+MfBtpr9Zs1bbAX0zKoQQQgghhBBi6ujDqBBCCCGEEEKIqaMPo0IIIYQQQgghps50fUbpNwXvpdg3K4c2KAnOpt5sTJMexKvH96bnFjQLg+sOuXTehrbl7pO+aJGeNVy96I91q/WS1GJW/e9A4t+JR6zSiJqZNSPtZrvp62scqvWnI8Sv816xfyqPGcrRhvciNaOxP2K25DUNG0d9PHrvTmgeqIFItMQRDZQTvqOJtoXChDPn/OFIn0rt5X6hVtsa6zqhmU004TV6vjG11zOlljApB/s3yKnJgfYl9nmjlmV4zGvdBkte/DL7IPrVbKQZo84j8T+r1k8nHrxRP2sEf2637euLnob9kR/XHjvtn+vop8rfm497bWte5flq22hwo3efL877Yzi3TkM6nsNYzv0C4nKkgnOXbF7lfV/7L7zOpR96ZXmvzevxTL19ohkdwNdtY3o+ozF//rL/6tK3bnyfSw/PeW3waNa/u/6yb79rV5ftt9n3+zZQszzq+TbSX4KuO95rgBox9P8G0/RDpJ931EerphezdH5PSO4VXYvmyL0q8po9DbJoPgqz2AdjecHnzcUFvdZn/Lt0p3KvAFYK1mqNns8r8XWPvcA5n9d4cO4VkrUDcDpR1n0GH1y+Z+7rsOL7fyMeP6n172G/BHqpU1OaiDEjj036nT6J4ZPa67p5tI6qPVFS31GfzrDWrbo3r030ptjzxDZ58yjNqt7xrleeD9xy26XfX3f7t7ljq2szPP2y0DejQgghhBBCCCGmjj6MCiGEEEIIIYSYOlMN0zWEb9H6Ie9HYY7r3nogsVth2F5deGFeES6DvDoPnPbHGcrAsIpeGYayftyHB84veiuXJsIHtobY+jn6mp8hfUk4a004EcON4hRtYRguyDTLwnvFobldhOHuNm8XQjzCuVs1YSe05TCwWYaKJlvj0+KHoUkMl6FFSBSmylDF/QJtOhLiOkA4VpjzFinhwLJLNzZ9mG6GPh6HWI9xLsN2Qw+hnTXE4Ug58mY8zIWbYInS92Goq9eV7z077K0Zeh3/3rew7Xmn5Y93EC7PbdJd3i3f/jZg3XLirJcILP6JD5c58InHLv2enfNhurHVgFlqrUOJRRyaF9AvkvAijK/hnB8TW1cd9tf3dg47ZTto3Hy9S5/8C0dc+uxL0KcXI/nHEPNPhcXUngLSj92GsO2Gt9zxFpfmFv8xd379L7n0TR/+n1yaFinDJcyFo8jWbMu37fZG9TPm/G/1Km8HvOcMcwxXCruqX0aRjxny548nYbxR2TKqSJb9ONRAiDptzOIxLzD0nfMT+z/WW6OjfmwZz5TzRPd+hFiuwIYP/T+nhArkVXPQfpG/sD4Dw1DL5wisDi5lUR+B9it8l2sVtl0VEohJwXwa78pZE3FJyAhfjqfok1k0HiRhuXXh7TVWL1XQqqWOKusX5sVy55AA5GwXUZ0lLftJPOM0OdD17e2x1uIOZ1ajb0aFEEIIIYQQQkwdfRgVQgghhBBCCDF19GFUCCGEEEIIIcTUma5mFFt8c1tpZ78wg+2BacNB/V6NHjVOUw9lC16Lka+u+TRtDWBhEZdtMO/jvHuwY6COkxYrMdSIUudJaMdSFXPOvLgVNstJGwnqaKqi2+usXKgXiLVxjXX/HrvnvC6G0KYj6+L6e0+VCWpGCdtY4DboOB5pY3Lq5Cq2xt9LZNRq0gYpei4ey2Hdwn4yvtbr+fqHfJ00RmWb6j5w1ucFHQzvldQ3NTpRH+a5zQ3fprorGHvQuDeOl39YOuBtDRY7vk2NYHO00fdjz0LPnz/fLcsyrNli/tQJWLf8vq/vg5/11kOxNUTAmJdhzEs0+ahPN4ZCO5xt+HfVwLhPq4L8vgddunn9teUxaEK3bvCWFI+/1D/z1jUYq2f8c8T6wIAxb99oRukEsen74dd++jsv/f6Jl/zak7rVkMLFXbB8xLepCw/79jru+flqsFy+m/6ab/udNd9m2qvo7yhn/0BZJ4PDsC2bRxtBv6LOk+SxxpS2MZwXazSinPtiHd5wwR8bLfh+1F7zbZ/2NxbpPvMZ9EH2Z1qAtGAfdNiP1SvXl/V7sOE13717fTE4z+Y55vCkkmK7Cxyr0ZvuFZL5h+NprB3kHiRc6yaeflyX4F4V+zrwWpaTlmi0QYzbUd6qGS/r5L1RX0ly4nvnfijcw4SXR9dnNf2ZOk+uhdmt4uPNJh8S94L1Gx+02Y90s7CQzLFNzl7l/c/+mEu/+OxffEL56JtRIYQQQgghhBBTRx9GhRBCCCGEEEJMHX0YFUIIIYQQQggxdaaqGQ3zPgia2kynB6AvKOPkkzh7xLbTSzAuBzSjZ159tUsf/PMz/vyTPp0QlaV7wceIb8BH9MDspksPRtByRLHu1Je0KvSlRV6oA8Tdx/kxb3qW7lYjGpeN59JLleVanPHvan1Qvp/e49D7bEDvM/J10hhU11Hsj0hftnpvsDq9RfSc9FaraI97icSnFR5nsaaU3pMB2kDqVcbXHnTpk1/j+2Ezyu74utcGNle9NjMf4l3w3vSMjY5nF1bcscYJ378PXfD3IrPXXnPp91NnvbfqMvo3+yz10gNoSmO9NPvJw6cOuPTSZ339Ld7rtZqjZa/zGh8px9/eg/AZhedr0jcqCMveV6yBsTnx8wWJhjQqy9lXH3fHTr7K12drGfrUMbVa+P/WqP7zIfr7LOaYPUqzCx0stFurG0+dPv1jz/tvT/jaT7/8V136prM/6NJhA/NC1BUyrEyaW5iv+vCynoP/bNTk2uf9ex4E+EPPY18HeAMmerZO9Rzjrm3QE9YfH7MNRl6rYwxpW4dR7pHvd81VjMcVnpwBfZLzKI/3ZryW8NytpeZ8/Zg/1rsPfTCjIWWNDjRKp96pT1zDPE04piWa+3i/FPqqJmtfvEfueQLv0DCM/TvRVtl0qRHlPi5YD2Sz5b37B/y1Ga2/aySlrkoSrXWNrzDmxkTnXbGO5l4BdTpveolm0V4O7GKJPhX9u4llYNzfszaeabg/vyvsti5/fIzZn08rhBBCCCGEEGJfow+jQgghhBBCCCGmjj6MCiGEEEIIIYSYOtP1GUWANfVocWnoI0iNKH1Is4NeYxYePeXSeT/SHkAbOP8o9FEjekJBpwCtXBzzP3fC53Xice/nd/Bmr3FqIn59EPvgMS4evoP0FaU3KOPwK88F1LoxRj8tdzM615drtuN1nnwO6lNPnS3f5dV3QCP2EN4rNCVNaDMbDOqPBAGJ9ye1K/DJbOD8fN3rA2Ndc5I3fXD3KEmfpEYn7oeUBtCrDu+m87D3Dr3qU953tLVa1lHzjofcMXpXJtoh6mzgCRt6paClMarRruNdUf9z5LfK52pv3uiOPfS1XuPYvM6Xe7bn62R9y+t/zvVLDWr+iB/jjn7S1+/SHdB9UpuFca49jHTdfV+OZDTAtfTJi7XXgeNhTTkSn9dFr33b+JobLv1++qW+ZM1lX+6MGlGMJfmoRrjkyrU/fEYzaIk4xA36ZR9+211vcsc+cMttT1u56rjmuPe9feQ+70+ZR559ozn/LvrLflxqrUPnOfSV0F2J2yA8S8/5vAYH/FgyOApP0wW07wr/bs659CGM342ZWXjMzxO9s9H+E+eQVx/z/QhaOOpA4/mJY/MQYxx9MEHz9KpLd89H41QTfY77eSSexfRi3Pk7EXrRJ3uH7FFqfa/jY3WZsb4499XMV/5gjfknx2r6jEbvOukGtP6s8W2u8nVObEaR5rXMyWm1a9a6hBpR3it+TOpNuZZtbGDsuYC84+XUDNb7VePMHubQbPWeGzuhb0aFEEIIIYQQQkwdfRgVQgghhBBCCDF19GFUCCGEEEIIIcTUmapmNDsF/07Eo1s70k8xLh6ehfmMNzVavXXJpZfoFRjF1ecbXuvX+eLDvlwMUIePUz6Cr1MUZ9953OvPFr/kdTErV/ty99o7x/eP6FlUQ6dV7WlIrWZMq8mAf5QlyYvXl/dOPKBw35m21zhcPX/BpbtRXutLXoO3sOB9HcOQZmzU90CnHPtqoY3la9AOUgsHf6/AdjGM9GzQ8yTtea9Cbfas1y02Yg0P/SHRKOgvmZ32mtEetZpRfrw26ZOsT+pqqHGM84NeivrzsOh13mxToxOPX/p96WP+2Mypm1z61It9XhuHfXtsbfjnmo+kWQfu9PrImc/7cSrf9OMY6z+pg7jOuvCqA3zvhjEzz8p3laF+PvjQJyrz/rYXv8GlV153i0ufeFVZJ+2r/TieeL7lvh1kmzv7ihYZRnWU7U5LtFdItK3QkGbRvgP3nfXevleSP3rx+1361nPf79KjcamfHCzCr/sq6J83fPvtnENfiX4P6BbZqq+/Li13+36vgK3jGBPnyrbfgCa0UeMFnsFbdeExn/fM6TK/9jo0oWPcaw37alzw46nTDkJTn/g007sS+yWELV+/y/eU9x71sB7q+/E1o8c252R6RLtzq9cle5W6+crtecBxiHMbdbMc1+kJXaX/TfaAqF5jUofcjPKexVphMO/3YhjN+zY1Ooa5rxPtsUFNKKok0ZfW+JIyvyqSfQY4LVTcK7HM3fL9u3fB129rHd7r3WgPE34U2KXWda9w23M/hL/8m8u6Tt+MCiGEEEIIIYSYOvowKoQQQgghhBBi6ujDqBBCCCGEEEKIqTNdn1HEp4eDB1zaeRpBX8Y4+LABP0n4jA2v9lqZ1nrk9wetW2DcPPQSFEjSxyl0Sl1Y3vYx+Yc/72Pu77vFP/NVN5/2eUVx4vQsqvP+7EPL2oIPaawppZ6UMffUq3YrtK1m3meUebHcq1teI3Ku5bUGJ1fK+lzeRLw+PWChfaF/WqILja9nXvTYpCYSXpeJ1iPWLVK30dgf2hdqNZ2O2yzVZsaHoENMdDP0n6vQ0YYFn1fiO8z3zHcHYn1lBi1wo43+jufIDnofzOZS5GkMT83uXY+79LX3QdO46Nt6oFfg6s7jVLYGHTy1Q2xzHDOjOsrqdEY1voNVvoRvvfZlLt06dpVLn3/9jS792Df6vHoHy3fd4BjW8enBoEazlxjWlefnXcxH+0WjQz1Um1rh8oS1s769vfXON7v0B2/9yFNZsl1x81V+7rvj3DWXfs9bmM/hOzpYwPx0xp/fORu1oaHvz4MlP1aMh6jQR30yb8KX9HB5/hjtcZz5c0dbvv/PP+TLffhzfizqnoz8e7fgH9mDjhNroMTfM0pnK17zHbjGoUaUfpMYbzt3lJXUwXhJDWOY9W3QMvRRzqPROEWf0bzOJ3OvUKURNTOL17p1+yFwvxSuW1h/cTppE8ib4zj0vYH1Hfl1N1HOxQd8eoR9Xc7Pos0dinyvMc5zbVunn9yNRpTwERuNy29jA2hEW6f9M3a9tbI1uaVJdHmDewHsU5/RJ4q+GRVCCCGEEEIIMXX0YVQIIYQQQgghxNSZbpguQtqy+Z5L552yOM0VhMohfDBfWXXphc+e8McZMhiFfuQM3WTI3zrCMUkS0lY+V+OsL1fvzIpLH/+9a1368QMLLn3kQHl9A6EJGSxSGMLGL/WbCDeIQ2nrQn5p9UJ7libuPdsp65BhuBfu8CHThz/jy7k6Wnbp6+4v67/xGX9yDhuOZNtzhDbGlj5mZhZvI7/kbTeyBR9O1DztLWeSENYxQh3j8JhkC/V9EnLBcmNrfWeDwvCsPupjxvfvpN8w3D0Ol+35NsRwrQz9P0fobUDYbojyCwy7Z8jqYyddsrnkw3TzuagOaix7shM+r3BhZoczJ3lH9R3mfftMQtZwbcD4SiuieAwNaI+UHjD90Ud9Pxzm5bv81pu/zt/nZc9z6Qdf7fvV2nN8fR886sdIjnMxHPOMUeQ4n+PWbHcQHfNnj8b7w34p1IWRNXc+/sC5Azsemza0ALj15Pdd+n008v1kNOefaeugf6+9Mz48rvdYaXPSQbhr+zxC57t+rOgf8mNPBguv9nqZztD/GYY3e9K317lHEZb7IORIsT0L+mCD4y2tnGiDEsUuNmhXlXhS0C4EIcIcI+MwU0goeC3vlcicOFa7cQ5jAfPeozQOw1KpykKFdc01DMOaayQUbk5i+DUtfEiNzCl+lwESqfZpv25efNBfu3XEl6W/VNZJA+tNhsrW2a88ldHbtA9juOxoGEnSzvj6nH0EtlHn8BxsBvHh/aHketrQN6NCCCGEEEIIIaaOPowKIYQQQgghhJg6+jAqhBBCCCGEEGLqTFUzmmxv/Qj0VNHveaIjgM4TQeL56ppL2wXGnEcB2bSn4L1mvV4l0ThQbxVr5aBdYzmXPnPKpc8+/5hLN15V6qdop0Krl+GoRq+GOPo4vzY0o0PopahXbdIyATx6stR9zn/WawWf/fteE9Y87dM5dQ3r5Tb0iUaUUCzQ9XqffMNvaW+RPUsDGrwmNTjQT3A7/ESmEL372EqkyHx/6NES7RC3eu+V7zafhSaU9it96BBZv9S+xBoUahihR81QvxwfAtqBswto1+iO8N6zc+d93rFete3Hjoz6H+pTV73WNdFLzc9FCW5vX21FQi08dUqNSDdLDX6y5T/0Z2+63tu1hOc++9Lv59/pNd+nvxr68xv8Mx+c8ffuYZzbHJT9jOPSTMu3mWbDX9sf+vqkxtRr3aFD5oC5R8kHmL8olK14DFq9vOFL3+rSH3vef3sSJXty3HCk9EG4ZwCLlMxrs7YO+ofePOLPb6+VY1PrlG9/3NehyXlg3esrG0OfHiyWbXLjMLSr5317W7oD94bFitOImvk5BDpNzimJ/q9dsZyrsb6ipQetm5L1VzxWJxZT2GeA83uNXtVZ6bVxLi1p9iij416bncHyrxHNja2Tfm+KZB6FNrPWIi3WdfK9J1YvGEvYhirEmYF7ROA9ds77Obh32ue9dVXZfgeLmDPaaBO0dnkStifckyDRm9IpZxV69BPlc3Tw6uZO+LbfXvfp4Zy/93ChfA7aWdXuDfAMQ9+MCiGEEEIIIYSYOvowKoQQQgghhBBi6ujDqBBCCCGEEEKIqTNdzSi0mNREZWvrl35vtKHPY6z7GPHVOD+jV2gUz85yhHmvo9m85YhLn7/Z5330U17n0bj3kTKBmHt6MdmpMy556HZ/r0efVeqvrjriA9LblfqnFPrm0asppgNNaH/kn+PUWe+H2rrf6wWv/kxZloUve+801gG1hmEL2oO4XdRoXcIc9L3QvtDbNta+5PRHOw8BAPQU1L4k2uOYxK9zf8T/J36T1K9E/aixvOTPZX0wPaaeErqQ6F3RNzTxuaNmnHnxXcV6wDrNUo2+19URy4FyBvil1hLll/ra0gsQOlroVRMdbZXeCl52Tbzb4QtvcumH3lCOmeNb/Fi7MOfbTK/jy0Xvz8Fo5z7OMYveoDxOrTt19/GYSIkoNfl7FhaT2qI4yXNH/g/3nTjsj3uL2Kky0yrbSW/Wt+3NdWhI5/xzrB/zbaqzWurR5s/trKkzs6QhNOANPnvynEv3jpRzdP48r5funfFtvfnIaZdONOQbvu/E416dbpPjVOMAxmNq+uO8zmHfBh6nHpUepnHRqDukZpR6U96LOsVorZeM+3Xa1z1C6wG/H0qONWY2X65bcvjH2mbNM7L9sl1E7SbxfOUcAQ/dpH7pXR2na/S7jaG/trPq33zv8fLefYxLwwV47HaoIa28tWX9Mu/mef9MrXXMIdyaAQ20d9qfMPdYWZbWlj+5a/eSnwAAIABJREFUvYZy99GHqX0/UPEg0owKIYQQQgghhBBPL/owKoQQQgghhBBi6ujDqBBCCCGEEEKIqTPVAPwc/n2MZ481TdSupXHx1F7Qgwvx7MPIewn60pWXeK/PrSX4EDWhW+r6sjRj70V6klJ7AeYe9JqR3l2lNvNEH5qvtn/mBtL0HaRX03AY6aVGeMZNf6/FL/s6uvZu/1yzD3rvxdi7LVvyvmz9a7yWpfug15Tmp5F2+l5oLa496tIBuprkf1eomYi0Lxn1paBBr0qQ6AOjsoa8un3uWSr0J2Ze85StwDMT9ZVoxOmpiTqK+2UOzXeijq7ReYY5326cHrVCB2tm22igoPuInjNpA3Ua5xq9T6wTpW42yZuaMQw1Ga53eUGD3zhw0KVXXnuLS594lT+/eV2pmz+0gHcFEc5gVK1dp4bU3adGF59oRnH+cAxft0gz2sR4Sb3pnqVVXSeuiVF3hLksG/r6eeGffq9Lf+GVv7zr4j1RPnDLbZd+f8n5d7ljWzO+D47HaGN4jo2j5Xtur/r9DrrYwyAkujro5qGba0Qa0uUR3gV9hrmOoZ6yyteR4w7GDq4tRld5/erqTaVncWfV5zW3ST06dZ5YHyQe3OVzJV7JHKdmsEcExdqcByKv5TzaR2S79F4lYzmRbsZ+0tSMYn2apDknc28B6nvdjTEWQ4Mf2F65J0LsM8r3iLY8WKI3MMb52E52BePSFp459+nWBsqNKTwWJs8/DK3qOYwlXZ/XqIe5bgDt60qkGd3AXgsbWA9gb5sOqmx2tqyT4YJ/N/3RV9Z3hV9ZTyuEEEIIIYQQYk+gD6NCCCGEEEIIIaaOPowKIYQQQgghhJg60zVtoucmtVqxvgcehbX6KWov6G0Zx75vev3ewue8J9TCBe8jaoyjB3msr6CWtcb/sPWI9x298X3Rc1LLAm1B1vPPPJ7xeQ8XvFajtRXFuq/4+mysQHO7Cq0G9L5J/V5V+tWdedkBX+wLiLl/+DGrJNY1UE9Cv0561S7N+ePLXr/aOF++21jra7aNlpBaGOqQ6X0bezXu0rtyr5B4U877+oufK4NWpUFNEzw2qUOylm/P/VuuuvR7+4zXITYeh654FV6/h7zmcXzI68SyVnnvFvrR+K77fLnQDjjWNA6W7Tsgr6QN1UF9T6RXzanJwTjUoIcpx0D66I7KPtxEfZ3/Jq8Rfez/b+/MfjQ7zjJ+tm/rbXoWe+zx2MbGWYQTIIlESKKIoLDYcRIikMJFJP44JG5QJCJQUHIBQiD2i4QliiHYcTT22DPT20x3f/1t5xQXlua876/cddwTctJtnt/VV3O22k5Vnen3qec3fb6uP+PHKQs1nyvoNAdVjeNpb9CzQI0oNaT0MLXn04P0wrCkn69PBqMpZd1GXqrQJR0feJ33F/7zay79Nx/71hky+uiwbUr4DK5G0AJv+euPn2zHsbz27+j20u9hMLjjPTdz6ie51jDejPkU7/vca8Y6ezbH2yrhh3xGr+rDp9s6Gu/CK/EH6bElzKmjT4ztXXNd5F2NtRy0r24Ox/yd33onuxCwTtCWbq8BeqnyWq5DsG6JvUHN9bGZcvJZUQ/j2rcwbQuP0hrrzcOnfbueXPd3L2dtndDLs5riXMhiq3nHXgLmdpN7fu1aHcGfe4hyjLEXA3Sf5XF7P+pmc2rIcW0x8/VZHbfnV1OMBdOLsWYkX3/9iy79zSdOORHoL6NCCCGEEEIIIXpHH6NCCCGEEEIIIXqn3zBdhDlG25rb0M+h/06OQiQZChKF0iC0wYa8MlyQYbm0RDhLiGWHtUNAGE8UimssQLpCkwvUX4ktwIfcMnzVhmR03Tvakv7xqz7bT/gwyIMX2rCd4xs+5OLqt3wYVBSaxO3GzbOjrdxfu+WvZZ+CFUwqfDuy4WH4C8OkCENvbNsyrPmChOlmsDHI1xH2vGzLmCO8iH2quY8QFli90GKpMbYTx8/5uLvRlm+b4Rv3XHr1lA87PXoazzLFmmBsGR5dd+nV23dcmpIB26fCOuxrGErHEDVaPzBEyxCF4QJaE3XZSBWTNq/TTz/vjr3zOZ/vyzfuu3SFcNiVCc2NookRtluwTyF0lOfbUNtByT37PYyUnyNEuGlo/WLOXfqxoSrToV/nhXxIKQgqwaSbgJAz2H2FEtYNCAF+856XXPws+eqPXnr/J6MPhYFP12tter7t6+fkcf+eFHM/Z3Ckzikvsv2Z8zfheMBw14kfP0LWzv+cfThWcLwtf+xDWK9cb+8928Ycy3ELVloRDB3NTJrzecc41LVGypftvWuM+9XmBk8/n2DMi+Z/O2jSBo5h4lnaboVyGNdPuI6LbHXS8xXXOFaW0mz592b6FNJPpMPMJ3fb9No9X+bqGJYpsFdpYFFF+0VrqVJNYSFJmyK88SVCgKsHmGeN9VMYoF1ZX6xfpItl+6xyjnZd+Hu99OorLv2dj347O4/szta7T3oP9JdRIYQQQgghhBC9o49RIYQQQgghhBC9o49RIYQQQgghhBC9069mlLHa1IEmNHpRHD1j3wH1aMHqFGinsIRui/H9kdYV97YWCg20FbQmiexB8Cyblw6dIfORUYOHbdGDEUwVR97KJdAiZd1rNWZPeD3A4MhrCzZvteka+bB2KlmWZQ3i5iPdnMkn9SeBMfjoB2E6TR632pfYdgdaF/aTLq2L1SJX1BJeDD1a9J5Bl+T0vNCXfPf29126Rpm//OHP42HQkPyotVha3vAa0AbbrYdN3x8DdeDUbph+sNjEe/OC14wOUK5m78Cfb7XX0M0U7K/sY9RPUqtt65tlWPfPava9rjPSdXFs+fAvPPz51uf9czee8WWktQZ1nYuVsbeAfo9pUhZ4Z9FPlnV7bz53BJsYPqtkGuWw1ib2ORcJ2pywtq2GtFn6MjZzlJlWLxW0WdCQvviP33j4+wef+ZP3kdv3z9zY8Jws/Dtarzr+33wETfNmW676gS/zYtPfazLB3gt7GKuhzeYYaclhd5FveP1UfcVr4Rs8u3qttT0LXTpO7lmAuW/99XZ8CC94O5tmhDmaaw3q5Dm+ptYmnCdxbV5AI468uHXKFDpZrp8uClzzmDkm5zjPuiaR9UtCj9qxTu4isjUy9R+GsBYcwZ7FLzGziXdQzLZfb+c6a5eSZZEkPBqm8iF1nqgTa7ECPSktEPkelehz+TE0o+b9z+d4B9mXuR8KnmVtY2hfU+Leh4v0HhLnhf3ppPuk90B/GRVCCCGEEEII0Tv6GBVCCCGEEEII0Tv6GBVCCCGEEEII0Tu9akbpM0isBx91hJEn6YAemvDzmyJg3dKhO4ieHcXoIy82hhxlpK6O+cppyWVizDtcLqNY9+aS14guL/u8NFV7/rCCF+DxHGkfJ7/2mk+T5Qutxq9cwPPtMa9XyanBi3xHTd7Qznnk2wi9KT24Em0d9SkQ4C8X6ZR5/cjE9Ed+kh1+dOcE1gk1j5EGMsErz/6aSxeb6Oyo3/Cg1RYPWF/0zEU7l9BqjHd8nxrdMbplXDt/0nvmLj/6lEsP/wftbHSf+cni1GPvnoD/7+uoP1u/kV6HfoeJa7Msy6rnnnXpN7+w/fB38ZzXca+PfDnowUlqo+Usi7PpoQMEQEwXRjBUdOhPa/iIUmPKci2NDynv3VCYdE6JvVPhXWd0n+x+pF7gBNybHqbTe60G8iN/90fu2H99/o/TDwP0FW0SMx61waFDQxrG7btwch31hWurE6/F2lh4XWeFMTE3+y2EE+y9gPGA7/DJTa8hnV/yebmyY8aiQ/+Ocj6K1jx4/8ud/Ye/R1gLhAF0nNx/AjrEZsPr1a1ve07v9KKj03GOnqHO7PHIp/Vi/P2Ee1vknBfsuVz/dAynbo+SLK05DUWHxrZLU8p9XEy5rB9slmXZeMfP2dUMc/C+Pz7YMRpntjP10PDzpFcotcX5SbueDWt+/5N6DI04ri0eYI1OjbhtS9YffYQzQD/UldGMLtFnVvBHXlwMvXTX2uE0LsabLYQQQgghhBDiA4U+RoUQQgghhBBC9I4+RoUQQgghhBBC9E6vmlHGbq8ehzbjTuuLld/dTd8MegnGamdFomi8FnHfOT20Cvp5QY9qdCHNVV+mvMa9d07PFon8TBlXTz0kdKDD2/AhtFq3OTR58LYK1A5OvGZvecVrSIb7bdz9Y7eP3bH5Na9XGbz4nH/0nj8/u39oLob5EtuqS0tMf08b/x8do09j2luNHnLB6hr3H/hr2efOKZFOduz1VIXRxdZom9+9+Sl/7hhtQQ1kwns1zNju8OdEvnh8uAuPvrt77bXQnw7pl7YOr9B1339z4zOaU8fFfEZaV/SZOfXp7fWR3jzhb5hlWVZMfD4Pf/lxl37w8fZZ1zd9/YxK3/ePZmh3avbMb/qG5vn71xW/F1aD2uVZ2kAjWnXoV+1QvoRGlB6l55VIxsXj5jd9QkNDwz7OfdTF+/oMJ+0YuNj38/nH/ukbLv38VT+H//mHvpNMf/m/X374ewg/WeqKWeh8iePGQzYMfRlmV6AhnaMPzX3fX1v497AwDUBf0bC779LN7p5Lb3zPZ3wd+zzkU6MHpL955N/Z4UM+adtncPfQH+RaAgSUa37D59POw6N7fiyhjt6VKYvHvGhutPNCl+j5opBYS1Dz2bkvQ3QY61HbL9Bn6NseeZRyrwu0jZ3vcrwXw13fzqN7mAuhMXVrMa7fAftUwT0lEnpqztGDyEsd/RHwXbBr5UhPysF5RV9sakbNXIdvhaL2fYb7IZxX6vrR8nkxSieEEEIIIYQQ4gOFPkaFEEIIIYQQQvSOPkaFEEIIIYQQQvROr5rR+rLXS731G153+MQ/t7HZowNo7gi1BCltYJY5PWC+uXHqsSx7D00ovYSGeLaJdy/ues0IY+5Jvu7rwGrlIj0Z9WeImy/uQ3tJXYjTS0IDdgLvqsLna7XhdTTlzJereuNOe6+lz9ewuuHSy0v+Xnntn1WYGP0c2svI143x/xW1hV7XZGP6wxS6QkgDIj0g+xj1AQktUZe37XmhoA6W9W10zDn0uWGZ1l5Q/xvpUYxHb6SX3kS+OjRP1LPk5en/71YceD+//IgaEejC7L2gBaImJNKQon5TmqjmGP2zoZ+fz1ex5f1S9z/kh/fta0Y3C63gvPb3GpRpzV5t0vQ/G1bQEkH/VzesM5+XxarNN+/VZNSnptN8lvUlpb6U514UCpRjMDBzHfS8qwX8I5fo29SjsT8PTveAnD7wY+1r4apLf2H+NX8r9DGriRqh3cdrvu+fwB81lNSMmjQPrfv6Wlz2dXA0x94AK//OjnbbcSxaQE18HdCjPBz5sSZD2tUoxwZKRKkp5VrCzkH3oRnlmqZimaF1g56tNmsgagHzB/RH7dD3AecdHnmQp3Wy54VIGwjtZtf85aCGlPMR69Put0CtKvsn5ozmmveEj9aQU3NvrD/LWdrzvctL1IH+Rz/5Tl2trSOuM/AuRP0T82qOvW6Cee/CT7muyxP9gFsvPKoWs29Wq0d7Ry9G6YQQQgghhBBCfKDQx6gQQgghhBBCiN7Rx6gQQgghhBBCiN7pVTNa7nktweZPoMW43eoDIy+qJu0fRyI/JaPrXLxw3R06fsJrGC///S2Xpm4uReQFSM0DY/gj30wTj97lM0hvJmhdo2dZb0b4OEZaN+gQigV0ISv4z9n2grayeONtnw3qOKmrsb6O9FaDTrEYQzuE+H9qdPKNVi/MPhLYx5Yd9b934NNWj4F7p120zhEJ788sy7LM1m+Xhpb6HugrqFMORstRbEO7Qg0u2iIM6A0MXaLVW9OjDETeYeF0LWyAT2C9Bn9O6Ghy6mroYWzHmgaiEdRvgfqsn7zm0ifXfTkuJTw4F9B5xN6h/trV4nStDP3QKvQTytWoR7UymiW0rOsjX39LerHh/MnQt3UT2vN574vCWb1XLSU19fAVbaApDSv65pk0t22gvOy+9729DX3qpQ3//peJ/jlGOy4m6PsnFPzbg+hfI/+cuX9tsgZ+3Q28rDdG7f02MA+WU85tHEtQSdxTwo5TeFHCIbRuh9CfQpNv90uI9PnQ5FNLGKCxL0/89bWd37j/RIePezQvcB1j9IEBY3W+3uuS9dHh2itBpH9kfSId6ZB53O6LwXtzHwIcLzDHLG5s+3ubvUOqfdzrEGnCfRvM+ortnPSezbL30FOfvq7mfhGd/ZNGrtRPm7YNJdsG6xLmq8NPNUVRnEFn3COf+/ffd+lQP1oZ9ZdRIYQQQgghhBC9o49RIYQQQgghhBC902/MA6xJrv6DD98M+23YI21MIusRhnownQg3HNz14S0T/im9a9ttPsuGOjB8ACEAzdTbr9B6JDz1WHvu2G+NXx4iDOcIYREMk2SIkA07RRhJYIgPtyaHlUsY+65TmPaKQqwRghE6bHnyI1NnLFPUrigHw3q49bZNM/qSocvYKp99Ltqm3/TRqAdtb/FfLgYss+0nOFYwpJphzwzFQVva8Jl6d88dKxGymk98CGBBCxVud2/fBb6/qS3msywKGbJhUfnefZ9PhmdxHBsjfBtjTbHZbrXfHPmxIqxg5YRQ5nu/4vtY87jvzzZ8E5FH2Xjgyzhb+rZcrfC+m1DRIa5dIXR2iRBghpnSmqSkTYeB9ivM9wlCfvksG37cYGyuw8UIpufYkor8KkuEVze0csHFDAVjJLOtfl7L+qOjx5Hv+/toy8mkHZALtNsI7by54SUp92EPFmzYLsOaUcZQ+fRyKx0SaC1T6pGXGq1t+TIOd/wcnZ+k5UfBhPlHlilo6EhOgHEsbLV541ohYGzJjhFuiHGsQqhyPU4sHbtkEGt+7A6X/bhVHLVtGdmvFRcjtJ7rjkjik7Im4VzWMT+ljkfrZPYRttWOn3chdstW19r5idKYKHw4kj3hZvb8nza0m6HKNtS+Sz7E9T77WGIdE1n40WoQMgeO3dYyqan8uc3Anz3okGf8vNg/4jr50fKpv4wKIYQQQgghhOgdfYwKIYQQQgghhOgdfYwKIYQQQgghhOidXjWj+QG2Jp/B2sHGYkOnkUOzEGkzSRRjbtLv7LhDwzff8flAvH9kAQL7EBcnjm3hGQufU1e3vemSJzdancfwAPqSlA4uew+tZgrG3Ec6znTcd2A5U1C3Se1L7uvE6St4LrakDxs+Xp25iqxeHpg+yDLTZmPda1uiPoX+a9sj51b5XTrkc0qkOTHvQqTH7ajPqP/yWaNWLxWivg49Cvp6Ts0JcNu5R7rutIVKZM9k3mFqrRvYLRQjb/WSX72czKfVUxWR7Yvvb4sXnnTpvRf9vUYTaEyNNrNu/L1PYNVC25MKus7UvUgDLWHXyDEo2/agTQmlRMwnbWLm0L5azSmtRAYJa5HzRLNK12AwlVQNfN8ejqANbvzc1izQlmxbm07L92Nwr/rY97mp0aBWQ59v6o6pIV3f9O/G3MwTyxOMUwvOdUh2aEjrobEHWvP3mm379339su9/4128k7PTtYPlFGPcPG1fRT16s2bGatpCde0vQQ3esdfolieYG+1zMU5FczjtRMa+zsKoba+88JrcLj3quYHrvDqxNqNuuMPCL1pDJojmb8B1SWQFAw1pZe1Y1mBjxP0SFh3rV1suzskDjjtnWz9FdegOsn4xv0fr1cSaPr3siNcSSFsbqUD5qX8tIh39eWGx+L/5jNRfRoUQQgghhBBC9I4+RoUQQgghhBBC9I4+RoUQQgghhBBC9E6vmlF6GlED5bRZkXkaAqqXp3v/ZVns32n9gAI9CxnXzdj2La/rpAdXY3SIOT1LGd8PbdvqMX/vyVut5iy/7bWt1LZFujrWGfUCNu6e5zImf+g1IdR5lPvwLbPei/BWzKDzjHyboAe2/mnhxGtVAvIZJngW4/3fuefTVksQ0IfoEbV74NIZ25a65pHREtLH7YJoXTr90My7EvU/wmupvRzSxez0a6O2KXGc/ZnX27GGWitoW6izydehW7J0lKmBT15BPTrGhzBt+3vo0MEe3/RjXH3V97F16Oqsx+ayQ3c4QP2W8GZM6USpEaXvKL0vR0xXbb4X0ITS/3TJLpbwFc0yjFM4d314Md7RAj5ubAmrm12hnavK1/Vw5Mu8pH4f97Z61UANI/3lUnrTLIs0p03TPnuJPlTj3vOZf4+KqJ3b68sRxoIBxxaMB0ukfTazZtJmfAZd12rDX1uPfR2tRr5ck12fl8Fh2x7NhJp8aAfR+QP8eW0VFhyLU5q6LIvnOh4+brXyVpuaZVlWXNn2+YCOPtIxQv/fXG791csDf204hu/oeYV7RHDNY9LRfhLRnhod/seYz9walFrMlL/pe9wryptZj0W5ivpYx7PcWo7n0guU65L0rZPwPWJf5xoyasuEhynT/C6Bwbf1GQ/0gIV2nX7cP08+829/8PB3g/GyqB5N26q/jAohhBBCCCGE6B19jAohhBBCCCGE6B19jAohhBBCCCGE6J1eNaPZEtqstdO9qqI47UhDhthr6hAiLyETB04dZ5eH0cx7CYatDf9sowcM07SmoXjsqkvnK/j33d1v74UyUAcbKmgeqVNM6EIirypq1+CfmB/h/y2iOPvhqccibTDrG8+yOgf6ijYoY7HvNSXN1S1/nHVmtZvQ5BVb0AZC+xoOUb/ItytVpHm+INDzjHpK287UOFATTj+5degl0TZWE02tZTj0HsXllm/n6J2mntfmm+fSi5b+aCDM2rGGeh7qpSO9zyqtU65N//7LW//qjn31s7/n0vefhxYT2jhqN+nVaBlDLzmD7+gc2kPr/0ntJf3QqBGlrpPXW50o9ab0HaVXKHU1vHdVnj4mUp96XhkMfR+q0Ta50U9SS1l36Hcb6DqpB8qtdpMaUU6jkXYI6RXeHev/uaL2kvM9ysUn2cMhrVWNxG+osyySkJnjKMNq0998OsTxiS/Xao0a0vZ4NUXfXvqMlCeYv5YYu808HIbYl4G+7dzTYI4xEeuxwq556McNorUG12pIN2sD89v7MldHF0Qzyv0UEj7jnZpQ7ksAX9ZIp2jnlI51c5SvSE95usc2rw3wXed6ICqH3X+C+cA8GdVRl67TwvUmtNXx3ioYIFZn1MKmwHtn39GGRYqydX7+drh7v10r5xwvH9EP9fyUTgghhBBCCCHE/xv0MSqEEEIIIYQQonf0MSqEEEIIIYQQonf61YxGnkcJTSPj4ifQl+3sJR9l/fr4LOq6AjUN1JtGN4fmycR959uX3DFqGI+f9r6ia2888Odbz1L6MEax7IirZ52lYttZxmWXTg4aUsbs27wt03rfZHz/uzdrT4UOJmo76PuK/UMch27Blhu6zrCx5tJHH73i0mtvQq/6xtv+3rac1CEMLoYeLdJeT+A3a3VIHd6p9NSM+gF8c61ehTqjb//kX1z6Szc/5dIlvdmQ78y+G/TvxHtGf8/IT9WUgz25oFaY6QE0pdBXl5fb8eMrv/WH7tjd37nm0tObeP+h71tBI2p9CKmtDIHedr5kDfSUo4HxQ4SWhdfS63OxSnuHWirkc0idPPLN9KqGbtaUg/fKzpEmJwXrl5qdxulCqamld51Pjyd+PF2Wvm2s5jQaxXGvyHJvRU8+HB+Y6zkFL7lnAcYOPttqX1FflJBmdTpf8Utu/oE6LwyJDabw2ePQiEMzOr/c9s8CPq6DI/ji3vXHhw/w8NqcH3kYQr8XeURiPPBHs2B09vkc7y/3eYDPKPej4FtXzNp7zx/zc/JgM+H5fI7o8ohOQm0l5+QOL8vUGicvzrbkzzf8/ijhUpvOpx17gRCuX828G6lmuT6NPN2x1uVeF3a9yjqg7z3XKdCIsi2dfvWMet8w9gOC9SGuMVbkXHZEA1d//PYPv+LSdj1A32vOwe+XizH7CiGEEEIIIYT4QKGPUSGEEEIIIYQQvaOPUSGEEEIIIYQQvdOvZvQsIA5+79efcOlLP/TageK1Wy4deVuZGHT6IUW+gzW9FuFtBW3h4aefffj77if9vaqP3/f5Xtt16Vt/7cv19J8Z3cedHZ+PE+hgGTdPPQV9MW1MP+PoqSmZQ0PaMI4+fb07Bv0e4+bzE/h12mcndIVZlsW+jcdn1aca9nxbbf5HWvcRaY1T+gGW45wS+ZAV+P8q6r7duegT0GLmW14vvbrpPXfL+23/zuFN+6WnPunS3739PZd++YXP+rxc83rf3OhAml2v046gzoM+uMXp+t+m9u8c9SblNV/m2S8949L7H27r7MgfyppnfZ1ATpk1kRfo6e8obYLn0G0uoRGlPyW1cpZV5AXq+1QFvV9K97lY+XxZreq794IGrzn9Xlnm9ZbDKp2P8wrzSd2nHWoatBtf7+WS/tysv9PzUY5932b/C9RiRjfAzW052HcXuPcMuro16LpMv4jalfladfyffNWxh4RlAP0UDtOCb7mJtqvavBSUgE7Sus/Rmi9HNW/zbbVpWZZl1dS/wMUc9Qf/8/wYa6bdgzYf8P7OL/lxnn7yOfcKQacs91qNaTU5g5/keYL6SM6jdo5hmXgu9x3o8Kp2azH6yRLUPb2/6+vbp+atpGYUz4p0xtHeIYn3iusMrLXCtu9jDfpJ+c6+uRfeG6w/M+qnsR7NU23HNd+Se5Sk27YemTSl61hj05+7T+4ceu2wHchKzKN1ws88hf4yKoQQQgghhBCid/QxKoQQQgghhBCid/oN001ZjWQIaUNoQoGIyfl1Hyo7edOHrIYpQmuDsV9hjBosKiK7ENi1vPWSD60dv3z34e9Xf/Wb2Vn48vhll741f+7h75t/gXBhht0yxOIIZeYW1taWA6E1AaGHtFDp2k7ctR1DLAJCftd8KMjsI74+i3lbrsGPbvtruWU6txOvGCKAEBZbJwyz5b0RKhpWZwi1pf0PbXouCHwXbPhRFArPPoIw8uXT3prk3id8WPnm7bZfbNzz1k0F6pNhucUlb6HUrPs+lh+0oV8NbYrb6onCAAAE2UlEQVQYXsSt9FFO25a8Vxd87+ZX/L33P9H2wWrTj0ub674/Hp/AkqYjfLMy4TSjge/rtFehfQitYFYmjJfBQwwnopVLyfhi4J6FcGGGD2dZOtS2RsjwgJYghuaChOmS2Orl9PodVAzpS2/Lv1zA2mVh2r3gHMCQ/o6wMtq1JAgDxqQj9O4EofXD9vyCZa4QGsuHReVgZuwx2mrgZIYiMwwaeVuZvBWLdP0EjLcr2MQMjtr0aIgw5xJjxcS3czlDaD3Ddm0oLiU9DMNlqCLXgQwlNVZa1cHs1GPnGc73gXXAMN7UMa5Xaf2CNZALhz2rxQzm7GKG8O3jVkrDuYztGoflsg6COURbKIa38r3xdbLcQr4P29DwHP0z5zqO6YTl5Lvnn16nUTkQltsMK6TbcvF95thAvv76F136T5//q+T5Z+GlV19x6fkcYf1mjok+DR7xmfrLqBBCCCGEEEKI3tHHqBBCCCGEEEKI3tHHqBBCCCGEEEKI3ulVMxrFUyMWPp8YnRcCkbf/9sf+XtQpYJtod684Iz45m+IwdYi+mo6e8ce/f0adqGWt8uUIdsdvaikue11cDg1pwLbS+Rg62kmbZhx9Sl+aZVkcV886svcreAxbkU+9Rc1gw+dz/lirBx6iHcPegU8jH8W61xJHepRVQk/BMlGbMYJeDXUUTH3n0LYEbs9+XqHOg5pce4iWJyDHcepPar/jf7bzYttWo52n3bHBq966KVDPC21rZEVgxxPaFM1RZuhmAuvAPKsY+veG2+x/9/b3XXoZ/LO+8tVfdOnqfvuO12N/bqQRhR4yQGO2gn56bdy2By1QqPOksnI89G1nu/O8Yyt39qCU3UqWZVljykV9aaRP5ViTpbVH9tnUk15UIusSk+YxDnklNI3UBlcDvCv2N+1XeC5sZSJJbkpTCvuVPPBe1J+dbtdC5VVeso/449TCRve213e5vkCfGlBHqTpoRv7mixHe73WfLuY+n9VWm55f8sdGB37cGh76fEzu+byUOd9Zk6btxgasXDhPHngLtagBzByUQz8ZtmBfd1657vdHyB8c+eNmbgzUwXJ9yuPUNLKO7EvOuYvrZO77gP1Tsp19lwzmOG3LkjrYLEtb34HoWwHlKB74NTt35LD1HZCvyKqF6Tgz6eP23nwXRv49a6DNrgdtnTR0MeLSFfPVdPWz24fk7Qf+W4NjfWn0rBWsXWgp9375YMzGQgghhBBCCCEuFPoYFUIIIYQQQgjRO/oYFUIIIYQQQgjRO3mkjxRCCCGEEEIIIX7G6C+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6Rx+jQgghhBBCCCF6538B2mhXCh5sHtgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_val_with_title(most_by_correct(1, True), \"Most correct malignant\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most incorrect benign\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_val_with_title(most_by_correct(0, False), \"Most incorrect benign\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most incorrect malignant\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_val_with_title(most_by_correct(1, False), \"Most incorrect malignant\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most uncertain predictions\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_uncertain = np.argsort(np.abs(probs -0.5))[:4]\n",
    "plot_val_with_title(most_uncertain, \"Most uncertain predictions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Confusion matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "preds = np.argmax(probs, axis=1)\n",
    "probs = probs[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "A common way to analyze the result of a classification model is to use a [confusion matrix](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/). Scikit-learn has a convenient function we can use for this purpose:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can just print out the confusion matrix, or we can show a graphical view (which is mainly useful for dependents with a larger number of categories)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[166   6]\n",
      " [ 13  61]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm, data.classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Looking at pictures again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most incorrect cats\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_val_with_title(most_by_correct(0, False), \"Most incorrect cats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most incorrect dogs\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_val_with_title(most_by_correct(1, False), \"Most incorrect dogs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Review: easy steps to train a world-class image classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "1. precompute=True\n",
    "1. Use `lr_find()` to find highest learning rate where loss is still clearly improving\n",
    "1. Train last layer from precomputed activations for 1-2 epochs\n",
    "1. Train last layer with data augmentation (i.e. precompute=False) for 2-3 epochs with cycle_len=1\n",
    "1. Unfreeze all layers\n",
    "1. Set earlier layers to 3x-10x lower learning rate than next higher layer\n",
    "1. Use `lr_find()` again\n",
    "1. Train full network with cycle_mult=2 until over-fitting"
   ]
  }
 ],
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   "display_name": "Python [conda env:fastai1]",
   "language": "python",
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  "toc": {
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